[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"_public_publisher_byId_2ca2ca83-2578-4a31-80fa-3e93c50e280e":3,"_public_publication_all{\"sortAscending\":false,\"sortField\":\"updateTime\",\"page\":0,\"size\":10,\"facet\":true,\"searchKey\":\"publisherId:2ca2ca83-2578-4a31-80fa-3e93c50e280e,\"}":145},{"code":4,"data":5,"meta":18},"SUCCESS",{"id":6,"createTime":7,"updateTime":8,"relativeEntities":9,"slug":10,"properties":11,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":20,"manageAffiliations":61,"indexDatabases":85,"url":18,"thumbnailPath":18,"statistic":18,"gsStatistic":18,"type":18,"analyzePriority":18},"2ca2ca83-2578-4a31-80fa-3e93c50e280e","2023-12-05T06:20:03.903+00:00","2025-11-21T09:46:55.893+00:00",[],"Information-Processing-Management",{"issn":12,"title":14},{"VOID":13},"03064573",{"EN":15},"Information Processing & Management","PUBLISHER","PENDING",null,0,[21,29,37,45,53],{"id":22,"createTime":23,"updateTime":24,"relativeEntities":25,"label":26,"description":28,"parentId":18,"standard":18,"scholarHubFieldId":18},"c41bb0c4-b86d-4bd6-ac06-d1efed5edcce","2023-05-29T10:24:12.280+00:00","2023-11-21T07:21:58.105+00:00",[],{"EN":27},"Information Systems",{},{"id":30,"createTime":31,"updateTime":32,"relativeEntities":33,"label":34,"description":36,"parentId":18,"standard":18,"scholarHubFieldId":18},"478cff3f-676b-4c95-aca9-7a6f01dcc3b1","2023-05-29T10:24:45.390+00:00","2023-11-21T05:38:21.287+00:00",[],{"EN":35},"Media Technology",{},{"id":38,"createTime":39,"updateTime":40,"relativeEntities":41,"label":42,"description":44,"parentId":18,"standard":18,"scholarHubFieldId":18},"1700c9d4-8702-4a01-8854-5ab9fde7d2df","2023-05-29T10:24:26.796+00:00","2023-11-21T07:28:56.604+00:00",[],{"EN":43},"Management Science and Operations Research",{},{"id":46,"createTime":47,"updateTime":48,"relativeEntities":49,"label":50,"description":52,"parentId":18,"standard":18,"scholarHubFieldId":18},"bb1bad44-29ec-44f6-a988-069cb69215fd","2023-05-29T10:24:07.710+00:00","2023-11-21T08:09:16.461+00:00",[],{"EN":51},"Computer Science Applications",{},{"id":54,"createTime":55,"updateTime":56,"relativeEntities":57,"label":58,"description":60,"parentId":18,"standard":18,"scholarHubFieldId":18},"904ea45b-ba1b-47c1-aec5-86b57b26bb13","2023-05-29T10:24:18.284+00:00","2023-11-20T23:29:02.117+00:00",[],{"EN":59},"Library and Information Sciences",{},[62,74],{"id":63,"createTime":64,"updateTime":65,"relativeEntities":66,"slug":67,"properties":68,"entityType":71,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":72,"url":18,"parentIds":73,"statistic":18},"85fc6dd3-e353-4ce6-9d33-a98bddca22e8","2023-05-29T12:06:36.475+00:00","2024-02-19T17:14:20.667+00:00",[],"ELSEVIER-SCI-LTD",{"title":69},{"EN":70},"ELSEVIER SCI LTD","AFFILIATION",10,[],{"id":75,"createTime":76,"updateTime":77,"relativeEntities":78,"slug":79,"properties":80,"entityType":71,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":83,"url":18,"parentIds":84,"statistic":18},"cbc888ff-f0a8-49ab-90e6-8acb752d4f88","2023-05-29T10:24:05.444+00:00","2025-11-21T10:07:47.637+00:00",[],"Elsevier-Ltd-",{"title":81},{"EN":82},"Elsevier Ltd.",12,[],[86,105,128],{"id":87,"indexDatabase":88,"url":102,"indexYears":18,"academicFieldIds":103,"indexDatabaseRanking":18},"ddb6df52-5e7f-47d2-921c-df1a82bbe4dd",{"id":89,"createTime":90,"updateTime":91,"relativeEntities":92,"label":93,"description":95,"key":98,"publicationTags":99,"standard":18},"a8273be3-1221-4f26-949d-71dc71ff1fc3","2023-05-22T09:59:54.622+00:00","2025-11-21T10:07:52.181+00:00",[],{"EN":94,"VI":94},"ISI-SSCI -  Social Sciences Citation Index",{"VI":96,"EN":97},"Cơ sở dữ liệu SSCI","SSCI database","ssci",[100,101],"SSCI","ISI","https:\u002F\u002Fmjl.clarivate.com\u002Fsearch-results?issn=0306-4573",[104],"1c47afbd-8aba-418e-ab5a-24927a739899",{"id":106,"indexDatabase":107,"url":119,"indexYears":120,"academicFieldIds":121,"indexDatabaseRanking":127},"080917ea-32f7-468f-9594-ec7df764094e",{"id":108,"createTime":109,"updateTime":110,"relativeEntities":111,"label":112,"description":114,"key":116,"publicationTags":117,"standard":18},"3c7051d4-eb7d-4c57-a56b-36fc74c5d1e9","2023-05-22T09:57:18.509+00:00","2025-11-21T10:07:52.274+00:00",[],{"EN":113,"VI":113},"Scopus - Elsevier",{"EN":113,"VI":115},"Cơ sở dữ liệu Scopus thuộc Elsevier","scopus",[118],"SCOPUS","https:\u002F\u002Fwww.scopus.com\u002Fsourceid\u002F12689","1975-2025",[122,123,124,125,126],"9257bd7d-2e00-4865-a19e-9796f3a21c3f","212b8bab-be53-49b1-9ceb-c528868800fc","fb8fd7a7-14d9-4ccc-8ac9-dcf94e7da012","499fd762-4414-45c1-b2b3-cc8d2abe204b","401a5169-272c-4a07-be28-5157c86734fb","SCOPUS__Q1",{"id":129,"indexDatabase":130,"url":102,"indexYears":18,"academicFieldIds":143,"indexDatabaseRanking":18},"3d6f6eee-0b0f-4149-a9e1-1f0ad300c984",{"id":131,"createTime":132,"updateTime":133,"relativeEntities":134,"label":135,"description":137,"key":140,"publicationTags":141,"standard":18},"a4921856-b128-4d9f-8f1f-e80813d3bbd4","2023-05-22T09:59:31.026+00:00","2025-11-21T10:07:52.153+00:00",[],{"EN":136,"VI":136},"ISI\u002FSCIE - Science Citation Index Expanded",{"VI":138,"EN":139},"Cơ sở dữ liệu SCIE","SCIE database","scie",[142,101],"SCIE",[144],"5e7ab733-08f6-4bda-8a2e-55af36fcbd05",{"meta":146,"data":148},{"total":147},"1822",[149,250,348,430,524,618,686,834,938,1021],{"id":150,"createTime":151,"updateTime":151,"relativeEntities":152,"slug":18,"properties":153,"entityType":160,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":161,"fullTextUrl":18,"authors":162,"publicationType":211,"publisherRelationship":212,"citationCount":18,"citationInfo":18,"publishDate":247,"publishYear":248,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":18,"isForceReanalyzing":249},"794a56da-b458-4cf5-b6ee-148b71c60293","2023-12-17T23:59:14.874+00:00",[],{"references":154,"title":156,"doi":158},{"VOID":155},"Allport, 1988, The ticc: Parsing interesting text, 211\nEdmundson, 1964, Problems in automatic abstracting, Communications of the ACM, 7, 259, 10.1145\u002F364005.364088\nJohnson, 1993, The application of linguistic processing to automatic abstract generation, Journal of Documentation and Text Management, 1\nLuhn, 1958, The automatic creation of literature abstracts, IBM Journal of Research and Development, 2, 159, 10.1147\u002Frd.22.0159\nMarsh, 1984, A production rule system for message summarization\nMorris, 1992, The effects and limitations of automated text condensing on reading comprehension performance, Information Systems Research, 3, 17, 10.1287\u002Fisre.3.1.17\nPaice, 1990, Constructing literature abstracts by computer: Techniques and prospects, Information Processing & Management, 26, 171, 10.1016\u002F0306-4573(90)90014-S\nRath, 1961, The formation of abstracts by the selection of sentences, American Documentation, 139, 10.1002\u002Fasi.5090120210\nRau, 1989, Information extraction and text summarization using linguistic knowledge acquisition, Information Processing & Management, 25, 419, 10.1016\u002F0306-4573(89)90069-1\nSalton, 1983",{"EN":157},"Automatic condensation of electronic publications by sentence selection",{"VOID":159},"10.1016\u002F0306-4573(95)00052-i","PUBLICATION","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002F030645739500052I",[163,180,195],{"id":164,"sortIndex":165,"researcher":18,"roles":166,"affiliations":168,"properties":177},"0b924c33-05f4-4b05-b7d9-756d3f161001",2,[167],"AUTHOR",[169],{"id":18,"sortIndex":19,"affiliation":170,"properties":18},{"id":171,"createTime":172,"updateTime":172,"relativeEntities":173,"slug":18,"properties":174,"entityType":71,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"498a2bd8-b94a-4c77-af7e-107a301e57a6","2023-12-17T23:59:14.930+00:00",[],{"title":175},{"VI":176},"SRA Corp., Arlington, VA 22201, U.S.A.",{"title":178},{"VI":179},"Lisa F. Rau",{"id":181,"sortIndex":19,"researcher":18,"roles":182,"affiliations":183,"properties":192},"e73b3e9f-c087-4509-891f-884ddef4735e",[167],[184],{"id":18,"sortIndex":19,"affiliation":185,"properties":18},{"id":186,"createTime":187,"updateTime":187,"relativeEntities":188,"slug":18,"properties":189,"entityType":71,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"5378463a-0893-48ee-b79d-55c62482d777","2023-12-17T23:59:14.890+00:00",[],{"title":190},{"VI":191},"Information Technology Laboratory, GE Corporate Research and Development, Schenectady, NY 12301, U.S.A.",{"title":193},{"VI":194},"Ronald Brandow",{"id":196,"sortIndex":197,"researcher":18,"roles":198,"affiliations":199,"properties":208},"6d525809-d5cf-4e01-a7da-2200b4f09b5e",1,[167],[200],{"id":18,"sortIndex":19,"affiliation":201,"properties":18},{"id":202,"createTime":203,"updateTime":203,"relativeEntities":204,"slug":18,"properties":205,"entityType":71,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"987c4c8b-5b34-45ef-a5f4-f26d91853597","2023-12-17T23:59:14.916+00:00",[],{"title":206},{"VI":207},"3RD Millenium Inc., 1 River Road, Carlisle, MA 01741, U.S.A.",{"title":209},{"VI":210},"Karl Mitze","ARTICLE",{"url":161,"publisher":213,"properties":242},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":214,"slug":10,"properties":215,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":218,"manageAffiliations":219,"indexDatabases":220,"url":18,"thumbnailPath":18,"statistic":18,"gsStatistic":18,"type":18,"analyzePriority":18},[],{"issn":216,"title":217},{"VOID":13},{"EN":15},[],[],[221,228,235],{"id":129,"indexDatabase":222,"url":102,"indexYears":18,"academicFieldIds":227,"indexDatabaseRanking":18},{"id":131,"createTime":132,"updateTime":133,"relativeEntities":223,"label":224,"description":225,"key":140,"publicationTags":226,"standard":18},[],{"EN":136,"VI":136},{"VI":138,"EN":139},[142,101],[144],{"id":87,"indexDatabase":229,"url":102,"indexYears":18,"academicFieldIds":234,"indexDatabaseRanking":18},{"id":89,"createTime":90,"updateTime":91,"relativeEntities":230,"label":231,"description":232,"key":98,"publicationTags":233,"standard":18},[],{"EN":94,"VI":94},{"VI":96,"EN":97},[100,101],[104],{"id":106,"indexDatabase":236,"url":119,"indexYears":120,"academicFieldIds":241,"indexDatabaseRanking":127},{"id":108,"createTime":109,"updateTime":110,"relativeEntities":237,"label":238,"description":239,"key":116,"publicationTags":240,"standard":18},[],{"EN":113,"VI":113},{"EN":113,"VI":115},[118],[122,123,124,125,126],{"volume":243,"pages":245},{"VOID":244},"31",{"VOID":246},"675-685","1995-09-01",1995,false,{"id":251,"createTime":252,"updateTime":253,"relativeEntities":254,"slug":255,"properties":256,"entityType":160,"verifyStatus":263,"verifyTime":253,"verifyNote":264,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":265,"fullTextUrl":18,"authors":266,"publicationType":211,"publisherRelationship":311,"citationCount":18,"citationInfo":18,"publishDate":346,"publishYear":347,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":18,"isForceReanalyzing":249},"2a27e18c-d26a-4fbe-a3dd-7acff03d3155","2024-01-10T04:22:56.586+00:00","2025-01-28T23:59:07.335+00:00",[],"Multilingual-opinion-holder-identification-using-author-and-authority-viewpoints",{"references":257,"title":259,"doi":261},{"VOID":258},"Bautin, M., Vijayarenu, L., & Skiena, S. (2008). International sentiment analysis for news and blogs. In Proceedings of the international conference on weblogs and social media (ICWSM’08). Seattle, WA.\nBloom, K., Garg, N., & Argamon, S. (2006). Extracting appraisal expressions. In Proceedings of the human language technology conference of the North American chapter of the association of computational linguistics (HLT-NAACL 2007) (pp. 308–315). Rochester, New York, USA. \u003Chttp:\u002F\u002Flingcog.iit.edu\u002Farc\u002Fappraisal_lexicon_2007a.tar.gz>.\nBloom, K., Stein, S., & Argamon, S. (2007). Appraisal extraction for news opinion analysis at ntcir-6. In Kando and Evans, 2007 (pp. 279–285).\nBreck, E., Choi, Y., Stoyanov, V., & Cardie, C. (2007). Cornell system description for the ntcir-6 opinion task. In Kando and Evans, 2007 (pp. 286–289).\nChoi, Y., Cardie, C., Riloff, E., & Patwardhan, S. (2005). Identifying sources of opinions with conditional random fields and extraction patterns. In Proceedings of the 2005 human language technology conference and conference on empirical methods in natural language processing (HLT\u002FEMNLP 2005) (pp. 355–362), Vancouver, BC.\nHatzivassiloglou, V., & Wiebe, J. M. (2000). Lists of manually and automatically identified gradable, polar, and dynamic adjectives. gzipped tar file [cited 2005-8-26]. \u003Chttp:\u002F\u002Fwww.cs.pitt.edu\u002Fwiebe\u002Fpubs\u002Fcoling00\u002Fcoling00adjs.tar.gz>.\nKando, N., & Evans, D. K. (Eds.) (2007). In Proceedings of the sixth NTCIR workshop meeting on evaluation of information access technologies: Information retrieval, question answering, and cross-lingual information access. National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan.\nKim, S. M., & Hovy, E. (2006a). Extracting opinions, opinion holders, and topics expressed in online news media text. In Proceedings of workshop on sentiment and subjectivity in text at proceedings of the 21st international conference on computational linguistics\u002Fthe 44th annual meeting of the association for computational linguistics (COLING\u002FACL 2006) (pp. 1–8). Sydney, Australia.\nKim, S.-M., & Hovy, E. (2006b). Identifying and analyzing judgment opinions. In Proceedings of the human language technology conference of the north american chapter of the association of computational linguistics (HLT-NAACL 2006) (pp. 200–207). New York City, USA.\nKim, Y., & Myaeng, S.-H. (2007). Opinion analysis based on lexical clues and their expansion. In Kando and Evans, 2007 (pp. 308–315).\nKudo, T., Matsumoto, Y. (2002). Japanese dependency analysis using cascaded chunking. In Proceedings of the sixth conference on natural language learning (CoNLL 2002) (pp. 63–69). Taipei, Taiwan.\nLin, D. (2005). MINIPAR Home Page [online]. [cited 2005-8-26]. \u003Chttp:\u002F\u002Fwww.cs.ualberta.ca\u002F~lindek\u002Fminipar.htm>.\nLiu, B., Hu, M., & Cheng, J. (2005). Opinion observer: Analyzing and comparing opinions on the web. In Proceedings of the 14th international world wide web conference (WWW 2005) (pp. 342–351). Chiba, Japan.\nNational Institute for Japanese Language (Ed.) (2004). Bunrui Goi Hyo, Vol. 14. DainihonTosho, Tokyo.\nSeki, 2005, Multi-document viewpoint summarization focused on facts, opinion and knowledge, Vol. 20, 317\nSeki, Y., Evans, D. K., Ku, L. W., Chen, H. H., Kando, N., & Lin, C. Y. (2007). Overview of Opinion Analysis Pilot Task at NTCIR-6. In Proceedings of the sixth NTCIR workshop meeting on evaluation of information access technologies: Information retrieval, question answering, and cross-lingual information access (pp. 265–278).\nStone, P. J. (2000). The General-Inquirer [online]. [cited 2005-8-26]. \u003Chttp:\u002F\u002Fwww.wjh.harvard.edu\u002F~inquirer\u002Fspreadsheet_guide.h>.\nStoyanov, V., & Cardie, C. (2006a). Partially supervised coreference resolution for opinion summarization through structured rule learning. In Proceedings of the 2006 conference on empirical methods in natural language processing (EMNLP 2006) (pp. 336–344). Sydney, Austraria.\nStoyanov, V., & Cardie, C. (2006b). Toward opinion summarization: Linking the sources. In Proceedings of workshop on sentiment and subjectivity in text at proceedings of the 21st international conference on computational linguistics\u002F the 44th annual meeting of the association for computational linguistics (COLING\u002FACL 2006) (pp. 9–14). Sydney, Australia.\nWiebe, J. M., Breck, E., Buckley, C., Cardie, C., Davis, P., Fraser, B., et al. 2006. MPQA: Multi-perspective question answering opinion corpus version 1.2. [cited 2007-1-26]. \u003Chttp:\u002F\u002Fwww.cs.pitt.edu\u002Fmpqa\u002Fdatabaserelease\u002F>.\nWiebe, 2004, Learning subjective language, Computational Linguistics, 30, 277, 10.1162\u002F0891201041850885\nWiebe, 2005, Annotating expressions of opinions and emotions in language, Language Resources and Evaluation, 39, 165, 10.1007\u002Fs10579-005-7880-9\nWilson, T., Wiebe, J., & Hoffmann, P. (2005). Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of the 2005 human language technology conference and conference on empirical methods in natural language processing (HLT\u002FEMNLP 2005) (pp. 347–354). Vancouver, BC.",{"EN":260},"Multilingual opinion holder identification using author and authority viewpoints",{"VOID":262},"10.1016\u002Fj.ipm.2008.11.004","VERIFIED","Auto Verify","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0306457308001118",[267,282,299],{"id":268,"sortIndex":19,"researcher":18,"roles":269,"affiliations":270,"properties":279},"3457b020-5664-464e-b01f-7f95d9937855",[167],[271],{"id":18,"sortIndex":19,"affiliation":272,"properties":18},{"id":273,"createTime":274,"updateTime":274,"relativeEntities":275,"slug":18,"properties":276,"entityType":71,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"c15be663-1ad0-4561-a087-7ac5a42b5e11","2024-01-10T04:22:56.614+00:00",[],{"title":277},{"VI":278},"Toyohashi University of Technology, Aichi 441-8580, Japan",{"title":280},{"VI":281},"Yohei Seki",{"id":283,"sortIndex":197,"researcher":18,"roles":284,"affiliations":285,"properties":296},"10052e68-d101-4eef-a841-8a8c423221dd",[167],[286],{"id":18,"sortIndex":19,"affiliation":287,"properties":18},{"id":288,"createTime":289,"updateTime":290,"relativeEntities":291,"slug":292,"properties":293,"entityType":71,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"a8873755-48f2-4718-83b5-2c5afe9cadaa","2023-12-20T01:54:09.918+00:00","2025-01-29T00:32:59.034+00:00",[],"National-Institute-of-Informatics-Tokyo-101-8430-Japan",{"title":294},{"VI":295},"National Institute of Informatics, Tokyo, 101-8430, Japan",{"title":297},{"VI":298},"Noriko Kando",{"id":300,"sortIndex":165,"researcher":18,"roles":301,"affiliations":302,"properties":308},"673db161-4c04-4c93-a1f0-23283f6b57d2",[167],[303],{"id":18,"sortIndex":19,"affiliation":304,"properties":18},{"id":273,"createTime":274,"updateTime":274,"relativeEntities":305,"slug":18,"properties":306,"entityType":71,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":307},{"VI":278},{"title":309},{"VI":310},"Masaki Aono",{"url":265,"publisher":312,"properties":341},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":313,"slug":10,"properties":314,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":317,"manageAffiliations":318,"indexDatabases":319,"url":18,"thumbnailPath":18,"statistic":18,"gsStatistic":18,"type":18,"analyzePriority":18},[],{"issn":315,"title":316},{"VOID":13},{"EN":15},[],[],[320,327,334],{"id":129,"indexDatabase":321,"url":102,"indexYears":18,"academicFieldIds":326,"indexDatabaseRanking":18},{"id":131,"createTime":132,"updateTime":133,"relativeEntities":322,"label":323,"description":324,"key":140,"publicationTags":325,"standard":18},[],{"EN":136,"VI":136},{"VI":138,"EN":139},[142,101],[144],{"id":87,"indexDatabase":328,"url":102,"indexYears":18,"academicFieldIds":333,"indexDatabaseRanking":18},{"id":89,"createTime":90,"updateTime":91,"relativeEntities":329,"label":330,"description":331,"key":98,"publicationTags":332,"standard":18},[],{"EN":94,"VI":94},{"VI":96,"EN":97},[100,101],[104],{"id":106,"indexDatabase":335,"url":119,"indexYears":120,"academicFieldIds":340,"indexDatabaseRanking":127},{"id":108,"createTime":109,"updateTime":110,"relativeEntities":336,"label":337,"description":338,"key":116,"publicationTags":339,"standard":18},[],{"EN":113,"VI":113},{"EN":113,"VI":115},[118],[122,123,124,125,126],{"volume":342,"pages":344},{"VOID":343},"45",{"VOID":345},"189-199","2009-03-01",2009,{"id":349,"createTime":350,"updateTime":351,"relativeEntities":352,"slug":353,"properties":354,"entityType":160,"verifyStatus":263,"verifyTime":351,"verifyNote":264,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":361,"fullTextUrl":18,"authors":362,"publicationType":211,"publisherRelationship":393,"citationCount":18,"citationInfo":18,"publishDate":428,"publishYear":429,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":18,"isForceReanalyzing":249},"99758cb6-4289-428e-98a6-3e55087f2c2f","2024-01-18T14:53:37.518+00:00","2025-01-09T23:58:01.773+00:00",[],"Using-crowdsourcing-for-TREC-relevance-assessment",{"references":355,"title":357,"doi":359},{"VOID":356},"Alonso, O., & Baeza-Yates, R. (2011). Design and implementation of relevance assessments using crowdsourcing. In Proceedings of the European conference on Information Retrieval (ECIR) (pp. 153–164).\nAlonso, O., & Lease, M. (2011). Crowdsourcing for information retrieval: Principles, methods and applications, SIGIR tutorial. In: Proceedings of the 34th ACM SIGIR conference (pp. 1299–1300).\nAlonso, O., & Mizzaro, S. (2009a). Relevance criteria for E-commerce. A crowdsourcing-based experimental analysis. In Proceedings of the 32nd ACM SIGIR conference (pp. 760–761).\nAlonso, O., & Mizzaro, S. (2009b). Can we get rid of TREC assessors? Using mechanical Turk for relevance assessment. In Proceedings of the 32nd ACM SIGIR workshop on the future of IR, evaluation (pp. 15–16).\nAlonso, 2008, Crowdsourcing for relevance evaluation, SIGIR Forum, 42, 9, 10.1145\u002F1480506.1480508\nAlonso, 2010, Crowdsourcing assessments for XML ranked retrieval, Proceedings of the European Conference on Information Retrieval (ECIR), 2010, 623\nAslam, J.A., Pavlu, V., & Yilmaz, E. (2006). A statistical method for system evaluation using incomplete judgments. In Proceedings of the 29th ACM SIGIR conference (pp. 541–548).\nBailey, P., Craswell, N., Soboroff, I., Thomas, P., de Vries, A.P., & Yilmaz, E. (2008). Relevance assessment: Are judges exchangeable and does it matter. In Proceedings of the 31st ACM SIGIR conference (pp. 667–674).\nBradburn, 2004\nCallan, 2007, Meeting of the MINDS: An information retrieval research agenda, SIGIR Forum, 41, 25, 10.1145\u002F1328964.1328967\nCallison-Burch, C. (2009). Fast, cheap, and creative: Evaluating translation quality using Amazon’s mechanical turk. In Proceedings of the 2009 conference on empirical methods in natural language processing (pp. 286–295).\nCarterette, B., & Soboroff, I. (2010). The effect of assessor error on IR system evaluation. In Proceedings of the 33rd ACM SIGIR conference (pp. 539–546).\nCarterette, 2008, Here or there: Preference judgments for relevance, Proceedings of the European Conference on Information Retrieval (ECIR), 2008, 16\nCarterette, B., Pavlu, V., Kanoulas, E., Aslam, J.A., & Allan, J. (2008). Evaluation over thousands of queries. In Proceedings of the 31st ACM SIGIR conference (pp. 651–658).\nCarvalho, V., Lease, M., & Yilmaz, E. (Eds.) (2010). Proceedings of the 32nd ACM SIGIR workshop on crowdsourcing for relevance, evaluation, 2010.\nCohen, 1960, A coefficient for agreement for nominal scales, Education and Psychological Measurement, 20, 37, 10.1177\u002F001316446002000104\nCormack, G.V., Palmer, C.R., & Clarke, C.L.A. (1998). Efficient construction of large test collections. In Proceedings of the 21st ACM SIGIR conference (pp. 282–289).\nFleiss, 1971, Measuring nominal scale agreement among many raters, Psychological Bulletin, 76, 378, 10.1037\u002Fh0031619\nGrady, C., & Lease, M. (2010). Crowdsourcing document relevance assessment with mechanical turk. In Proceedings of the NAACL HLT 2010 workshop on creating speech and language data with Amazon’s mechanical turk (pp. 172–179).\nGuiver, 2009, A few good topics: Experiments in topic set reduction for retrieval evaluation, ACM Transactions on Information Systems, 27, 1, 10.1145\u002F1629096.1629099\nHowe, 2008\nKazai, G., Milic-Frayling, N., & Costello, J. (2009). Towards methods for the collective gathering and quality control of relevance assessments. In Proceedings of the 32nd ACM SIGIR conference (pp: 452–459).\nKazai, G., Kamps, J., Koolen, M., & Milic-Frayling, N. (2011). Crowdsourcing for book search evaluation: Impact of HIT design on comparative system ranking. In Proceedings of the 34th ACM SIGIR conference (pp. 205–214). Beijing, China, ACM.\nKittur, A., Chi, E.H., & Suh, B. (2008). Crowdsourcing user studies with mechanical turk. In CHI ’08: Proceeding of the 26th ACM SIGCHI conference (pp. 453–456).\nKrippendorff, 1970, Estimating the reliability, systematic error, and random error of interval data, Educational and Psychological Measurement, 30, 61, 10.1177\u002F001316447003000105\nLease, M., Sorokin, A., & Yilmaz, E. (Eds.) (2011). Proceedings of the 33rd ACM SIGIR workshop on crowdsourcing for information retrieval.\nLease, 2011\nMcCreadie, R., Macdonald, C., & Ounis, I. (2010). Crowdsourcing a News query classification dataset. In Proceedings of CSE 2010 workshop at SIGIR.\nMcCreadie, R., Macdonald, C., & Ounis, I. (2011). Crowdsourcing blog track top news judgments at TREC. In Proceedings of CSDM workshop at WSDM 2011.\nNielsen, 1993\nNowak, S., Rüger, S. (2010). How reliable are annotations via crowdsourcing? A study about inter-annotator agreement for multi-label image annotation. In Proceedings of the international ACM conference on multimedia, information retrieval (pp. 557–566).\nSanderson, M. & Zobel, J. (2005). Information retrieval system evaluation: Effort, sensitivity, and reliability. In Proceedings of the 28th ACM SIGIR conference (pp. 162–169).\nSanderson, 2010, Test collection based evaluation of information retrieval systems, Foundations and Trends in Information Retrieval, 4, 247, 10.1561\u002F1500000009\nSmucker, M., & Prakash Jethani, C. (2011). Measuring assessor accuracy: A comparison of NIST assessors and user study participants. In: Proceedings of the 34th ACM SIGIR conference (pp. 1231–1232).\nSnow, R., O’Connor, B., Jurafsky, D., & Ng, A.Y. (2008). Cheap and fast but is it good? Evaluating non-expert annotations for natural language tasks. In Conference on empirical methods on natural language processing (pp. 254–263).\nSoboroff, I., Nicholas, C., & Cahan, P. (2001). Ranking retrieval systems without relevance judgments. In Proceedings of the 24th ACM SIGIR conference (pp. 66–73).\nSormunen, E. (2002). Liberal relevance criteria of TREC: Counting on negligible documents? In Proceedings of the 25th ACM SIGIR conference (pp. 324–330).\nStemler, S.E. (2004). A comparison of consensus, consistency, and measurement approaches to estimating interrater reliability. Practical Assessment, Research & Evaluation, 9(4). \u003Chttp:\u002F\u002FPAREonline.net\u002Fgetvn.asp?v=9&n=4> Retrieved 01.10.10.\nVoorhees, 2000, Variations in relevance judgments and the measurement of retrieval effectiveness, Information Processing and Management, 36, 697, 10.1016\u002FS0306-4573(00)00010-8\nVoorhees, E. (2001). The philosophy of information retrieval evaluation. In CLEF ’01 proceedings (pp. 355–370).",{"EN":358},"Using crowdsourcing for TREC relevance assessment",{"VOID":360},"10.1016\u002Fj.ipm.2012.01.004","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0306457312000052",[363,378],{"id":364,"sortIndex":197,"researcher":18,"roles":365,"affiliations":366,"properties":375},"2c4fab0c-5917-4f81-a129-c774d7db9b6f",[167],[367],{"id":18,"sortIndex":19,"affiliation":368,"properties":18},{"id":369,"createTime":370,"updateTime":370,"relativeEntities":371,"slug":18,"properties":372,"entityType":71,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"a02bfd83-8d6c-40e1-b6eb-0a2fef84f264","2024-01-18T14:53:37.552+00:00",[],{"title":373},{"VI":374},"Dept. of Maths and Computer Science, University of Udine, Via delle Scienze, 206, 33100 Udine, Italy",{"title":376},{"VI":377},"Stefano Mizzaro",{"id":379,"sortIndex":19,"researcher":18,"roles":380,"affiliations":381,"properties":390},"f38d8324-09b8-4c18-a5a6-e399e78f30f5",[167],[382],{"id":18,"sortIndex":19,"affiliation":383,"properties":18},{"id":384,"createTime":385,"updateTime":385,"relativeEntities":386,"slug":18,"properties":387,"entityType":71,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"4bd2daa4-dbcd-4680-a74f-4cbe30b4736a","2024-01-18T14:53:37.538+00:00",[],{"title":388},{"VI":389},"Microsoft Corp., 1065 La Avenida, Mountain View, CA 94043, USA",{"title":391},{"VI":392},"Omar Alonso",{"url":361,"publisher":394,"properties":423},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":395,"slug":10,"properties":396,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":399,"manageAffiliations":400,"indexDatabases":401,"url":18,"thumbnailPath":18,"statistic":18,"gsStatistic":18,"type":18,"analyzePriority":18},[],{"issn":397,"title":398},{"VOID":13},{"EN":15},[],[],[402,409,416],{"id":129,"indexDatabase":403,"url":102,"indexYears":18,"academicFieldIds":408,"indexDatabaseRanking":18},{"id":131,"createTime":132,"updateTime":133,"relativeEntities":404,"label":405,"description":406,"key":140,"publicationTags":407,"standard":18},[],{"EN":136,"VI":136},{"VI":138,"EN":139},[142,101],[144],{"id":87,"indexDatabase":410,"url":102,"indexYears":18,"academicFieldIds":415,"indexDatabaseRanking":18},{"id":89,"createTime":90,"updateTime":91,"relativeEntities":411,"label":412,"description":413,"key":98,"publicationTags":414,"standard":18},[],{"EN":94,"VI":94},{"VI":96,"EN":97},[100,101],[104],{"id":106,"indexDatabase":417,"url":119,"indexYears":120,"academicFieldIds":422,"indexDatabaseRanking":127},{"id":108,"createTime":109,"updateTime":110,"relativeEntities":418,"label":419,"description":420,"key":116,"publicationTags":421,"standard":18},[],{"EN":113,"VI":113},{"EN":113,"VI":115},[118],[122,123,124,125,126],{"volume":424,"pages":426},{"VOID":425},"48",{"VOID":427},"1053-1066","2012-11-01",2012,{"id":431,"createTime":432,"updateTime":433,"relativeEntities":434,"slug":435,"properties":436,"entityType":160,"verifyStatus":263,"verifyTime":433,"verifyNote":264,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":443,"fullTextUrl":18,"authors":444,"publicationType":211,"publisherRelationship":487,"citationCount":18,"citationInfo":18,"publishDate":522,"publishYear":523,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":18,"isForceReanalyzing":249},"f5c0a19b-71f8-476d-8da8-16d98838b33a","2024-01-17T15:59:00.054+00:00","2024-12-22T23:56:29.843+00:00",[],"Revealing-the-technology-development-of-natural-language-processing-A-Scientific-entity-centric-perspective",{"references":437,"title":439,"doi":441},{"VOID":438},"Beltagy, 2019, SciBERT: A pretrained language model for scientific text, 3615\nBrooks, 1980, Technology, evolution, and purpose, Daedalus, 109, 65\nChalkidis, 2020, LEGAL-BERT: The Muppets straight out of law school, 2898\nCoccia, 2019, The theory of technological parasitism for the measurement of the evolution of technology and technological forecasting, Technological Forecasting and Social Change, 141, 289, 10.1016\u002Fj.techfore.2018.12.012\nDing, 2013, Entitymetrics: Measuring the impact of entities, PLOS ONE, 8, e71416, 10.1371\u002Fjournal.pone.0071416\nDzau, 2018, Health and societal implications of medical and technological advances, Science Translational Medicine, 10, eaau4778, 10.1126\u002Fscitranslmed.aau4778\nEberts, M., & Ulges, A. (2021). Span-based joint entity and relation extraction with transformer pre-training. arXiv. 10.3233\u002FFAIA200321.\nFunk, 2017, A dynamic network measure of technological change, Management Science, 63, 791, 10.1287\u002Fmnsc.2015.2366\nGeng, 2023, Planarized sentence representation for nested named entity recognition, Information Processing & Management, 60, 10.1016\u002Fj.ipm.2023.103352\nGuimerà, 2007, Classes of complex networks defined by role-to-role connectivity profiles, Nature Physics, 3, 10.1038\u002Fnphys489\nHingmire, S., Li, I., Kawamura, R., Chen, B., Fabbri, A., Tang, X., Liu, Y., George, T., Liao, T., Wong, W. P., Yan, V., Zhou, R., Palshikar, G. K., & Radev, D. (2021). CLICKER: A computational linguistics classification scheme for educational resources. arXiv. http:\u002F\u002Farxiv.org\u002Fabs\u002F2112.08578.\nHou, 2021, TDMSci: a specialized corpus for scientific literature entity tagging of tasks datasets and metrics, 707\nHu, 2022, Study of the effectiveness of 5G mobile internet technology to promote the reform of English teaching in the Universities and Colleges, Computational Intelligence and Neuroscience, 2022, 1\nHuang, 2022, Identification of topic evolution: Network analytics with piecewise linear representation and word embedding, Scientometrics, 127, 5353, 10.1007\u002Fs11192-022-04273-1\nJärvelin, 1993, The evolution of library and information science 1965–1985: A content analysis of journal articles, Information Processing & Management, 29, 129, 10.1016\u002F0306-4573(93)90028-C\nKhan, 2022, Mobile internet technology adoption for sustainable agriculture: Evidence from wheat farmers, Applied Sciences, 12, 4902, 10.3390\u002Fapp12104902\nLee, 2020, BioBERT: A pre-trained biomedical language representation model for biomedical text mining, Bioinformatics, 36, 1234, 10.1093\u002Fbioinformatics\u002Fbtz682\nLi, 2016, Evolutionary features of academic articles co-keyword network and keywords co-occurrence network: Based on two-mode affiliation network, Physica A: Statistical Mechanics and Its Applications, 450, 657, 10.1016\u002Fj.physa.2016.01.017\nLi, 2018, Co-mention network of R packages: Scientific impact and clustering structure, Journal of Informetrics, 12, 87, 10.1016\u002Fj.joi.2017.12.001\nLiu, 2022, Effect of mobile internet technology in health management of heart failure patients guiding cardiac rehabilitation, Journal of Healthcare Engineering, 2022, 1\nLiu, 2020, FinBERT: A pre-trained financial language representation model for financial text mining, 4513\nLuan, 2018, Multi-task identification of entities, relations, and coreference for scientific knowledge graph construction, 3219\nMa, 2023, From “what” to “how”: Extracting the procedural scientific information toward the metric-optimization in AI, Information Processing & Management, 60, 10.1016\u002Fj.ipm.2023.103315\nMao, 2020, Automatic keywords extraction based on co-occurrence and semantic relationships between words, IEEE Access, 8, 117528, 10.1109\u002FACCESS.2020.3004628\nMcHugh, 2012, Interrater reliability: The kappa statistic, Biochemia Medica, 22, 276, 10.11613\u002FBM.2012.031\nMitcham, 1994\nMohammad, S. M. (.2019). The state of NLP literature: A diachronic analysis of the ACL anthology. arXiv. http:\u002F\u002Farxiv.org\u002Fabs\u002F1911.03562.\nParmar, 2020, NLPExplorer: Exploring the universe of NLP papers, 476\nPercia David, 2023, Measuring security development in information technologies: A scientometric framework using arXiv e-prints, Technological Forecasting and Social Change, 188, 10.1016\u002Fj.techfore.2023.122316\nRotolo, 2015, What is an emerging technology?, Research Policy, 44, 1827, 10.1016\u002Fj.respol.2015.06.006\nSigman, 2020, Telemedicine in reproductive medicine—Implications for technology and clinical practice, Fertility and Sterility, 114, 1125, 10.1016\u002Fj.fertnstert.2020.10.042\nSkolnikoff, 1993\nSundheim, 1995, Named entity task definition, version 2.1\nTuomaala, 2014, Evolution of library and information science, 1965-2005: Content analysis of journal articles, Journal of the Association for Information Science and Technology, 65, 1446, 10.1002\u002Fasi.23034\nWang, 2014, Analyzing evolution of research topics with NEViewer: A new method based on dynamic co-word networks, Scientometrics, 101, 1253, 10.1007\u002Fs11192-014-1347-y\nWei, 2020, Don't eclipse your arts due to small discrepancies: Boundary repositioning with a pointer network for aspect extraction, 3678\nWei, 2020, A novel cascade binary tagging framework for relational triple extraction, 1476\nYao, 2023, Exploring developments of the AI field from the perspective of methods, datasets, and metrics, Information Processing & Management, 60, 10.1016\u002Fj.ipm.2022.103157\nYun, 2022, Technological trend mining: Identifying new technology opportunities using patent semantic analysis, Information Processing & Management, 59, 10.1016\u002Fj.ipm.2022.102993\nZaratiana, U., Holat, P., Tomeh, N., & Charnois, T. (2022). Hierarchical transformer model for scientific named entity recognition. arXiv. http:\u002F\u002Farxiv.org\u002Fabs\u002F2203.14710.\nZhang, 2023, Guest editorial: Extraction and evaluation of knowledge entities in the age of artificial intelligence, Aslib Journal of Information Management, 75, 433, 10.1108\u002FAJIM-05-2023-507\nZhang, 2017, Scientific evolutionary pathways: Identifying and visualizing relationships for scientific topics, Journal of the Association for Information Science and Technology, 68, 1925, 10.1002\u002Fasi.23814\nZhong, 2021, A frustratingly easy approach for entity and relation extraction, 50",{"EN":440},"Revealing the technology development of natural language processing: A Scientific entity-centric perspective",{"VOID":442},"10.1016\u002Fj.ipm.2023.103574","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0306457323003114",[445,460,475],{"id":446,"sortIndex":165,"researcher":18,"roles":447,"affiliations":448,"properties":457},"91c518ff-b90e-4151-bca9-203e4ff6812d",[167],[449],{"id":18,"sortIndex":19,"affiliation":450,"properties":18},{"id":451,"createTime":452,"updateTime":452,"relativeEntities":453,"slug":18,"properties":454,"entityType":71,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"a5b498be-346c-40ed-90b8-3c7a40086ca8","2024-01-17T15:59:00.102+00:00",[],{"title":455},{"VI":456},"Department of Management Science and Engineering, School of Management, Anhui University, Hefei 230601, China",{"title":458},{"VI":459},"Yuzhuo Wang",{"id":461,"sortIndex":197,"researcher":18,"roles":462,"affiliations":463,"properties":472},"7cc3e97b-dfca-4317-a490-95f8e9967f38",[167],[464],{"id":18,"sortIndex":19,"affiliation":465,"properties":18},{"id":466,"createTime":467,"updateTime":467,"relativeEntities":468,"slug":18,"properties":469,"entityType":71,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"91135a87-2922-4374-a42b-68290adf22f6","2024-02-14T13:02:31.512+00:00",[],{"title":470},{"VI":471},"Department of Information Management, Nanjing University of Science and Technology, Nanjing 210094, China",{"title":473},{"VI":474},"Chengzhi Zhang",{"id":476,"sortIndex":19,"researcher":18,"roles":477,"affiliations":478,"properties":484},"0656d48e-fda2-43f9-a6d2-48adece72439",[167],[479],{"id":18,"sortIndex":19,"affiliation":480,"properties":18},{"id":466,"createTime":467,"updateTime":467,"relativeEntities":481,"slug":18,"properties":482,"entityType":71,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":483},{"VI":471},{"title":485},{"VI":486},"Heng Zhang",{"url":443,"publisher":488,"properties":517},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":489,"slug":10,"properties":490,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":493,"manageAffiliations":494,"indexDatabases":495,"url":18,"thumbnailPath":18,"statistic":18,"gsStatistic":18,"type":18,"analyzePriority":18},[],{"issn":491,"title":492},{"VOID":13},{"EN":15},[],[],[496,503,510],{"id":129,"indexDatabase":497,"url":102,"indexYears":18,"academicFieldIds":502,"indexDatabaseRanking":18},{"id":131,"createTime":132,"updateTime":133,"relativeEntities":498,"label":499,"description":500,"key":140,"publicationTags":501,"standard":18},[],{"EN":136,"VI":136},{"VI":138,"EN":139},[142,101],[144],{"id":87,"indexDatabase":504,"url":102,"indexYears":18,"academicFieldIds":509,"indexDatabaseRanking":18},{"id":89,"createTime":90,"updateTime":91,"relativeEntities":505,"label":506,"description":507,"key":98,"publicationTags":508,"standard":18},[],{"EN":94,"VI":94},{"VI":96,"EN":97},[100,101],[104],{"id":106,"indexDatabase":511,"url":119,"indexYears":120,"academicFieldIds":516,"indexDatabaseRanking":127},{"id":108,"createTime":109,"updateTime":110,"relativeEntities":512,"label":513,"description":514,"key":116,"publicationTags":515,"standard":18},[],{"EN":113,"VI":113},{"EN":113,"VI":115},[118],[122,123,124,125,126],{"volume":518,"pages":520},{"VOID":519},"61",{"VOID":521},"103574","2024-01-01",2024,{"id":525,"createTime":526,"updateTime":527,"relativeEntities":528,"slug":529,"properties":530,"entityType":160,"verifyStatus":263,"verifyTime":537,"verifyNote":264,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":538,"fullTextUrl":18,"authors":539,"publicationType":211,"publisherRelationship":581,"citationCount":18,"citationInfo":18,"publishDate":616,"publishYear":617,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":18,"isForceReanalyzing":249},"42f1ad8d-4a51-4214-9205-5ae5c23cd2fe","2023-12-06T19:01:08.238+00:00","2024-12-29T23:55:54.374+00:00",[],"Accuracy-diversity-trade-off-in-recommender-systems-via-graph-convolutions",{"references":531,"title":533,"doi":535},{"VOID":532},"Abbassi, 2007, A recommender system based on local random walks and spectral methods, 102\nAdomavicius, 2008, Overcoming accuracy-diversity tradeoff in recommender systems: A variance-based approach, vol. 8\nAdomavicius, 2009, Toward more diverse recommendations: Item re-ranking methods for recommender systems\nAdomavicius, 2011, Improving aggregate recommendation diversity using ranking-based techniques, IEEE Transactions on Knowledge and Data Engineering, 24, 896, 10.1109\u002FTKDE.2011.15\nAggarwal, 2016, vol. 1\nAnelli, 2019, The importance of being dissimilar in recommendation, 816\nBerg, R. v. d., Kipf, T. N., & Welling, M. (2017). Graph convolutional matrix completion. arXiv:1706.02263.\nBoyd, 2004\nBradley, 2001, Improving recommendation diversity, 85\nBridge, 2006, Ways of computing diverse collaborative recommendations, 41\nChen, 2020, Revisiting graph based collaborative filtering: A linear residual graph convolutional network approach, vol. 34, 27\nChen, 2019, PCT: Large-scale 3D point cloud representations via graph inception networks with applications to autonomous driving, 4395\nDehmamy, 2019, Understanding the representation power of graph neural networks in learning graph topology, 15387\nEskandanian, 2020, Using stable matching to optimize the balance between accuracy and diversity in recommendation, 71\nGama, 2020, From graph filters to graph neural networks, IEEE Signal Processing Magazine, 10.1109\u002FMSP.2020.3016143\nGan, 2013, Improving accuracy and diversity of personalized recommendation through power law adjustments of user similarities, Decision Support Systems, 55, 811, 10.1016\u002Fj.dss.2013.03.006\nGogna, 2017, Balancing accuracy and diversity in recommendations using matrix completion framework, Knowledge-Based Systems, 125, 83, 10.1016\u002Fj.knosys.2017.03.023\nGoodfellow, 2016\nHamedani, 2019, Recommending the long tail items through personalized diversification, Knowledge-Based Systems, 164, 348, 10.1016\u002Fj.knosys.2018.11.004\nHarper, 2015, The movielens datasets: History and context, ACM Transactions on Interactive Intelligent Systems (TIIS), 5, 1\nHerlocker, 2004, Evaluating collaborative filtering recommender systems, ACM Transactions on Information Systems (TOIS), 22, 5, 10.1145\u002F963770.963772\nHua, 2019, Learning combination of graph filters for graph signal modeling, IEEE Signal Processing Letters, 10.1109\u002FLSP.2019.2954981\nHuang, 2017, Collaborative filtering via graph signal processing, 1094\nHuang, 2018, Rating prediction via graph signal processing, IEEE Transactions on Signal Processing, 66, 5066, 10.1109\u002FTSP.2018.2864654\nHurley, 2011, Novelty and diversity in top-n recommendation–analysis and evaluation, ACM Transactions on Internet Technology (TOIT), 10, 1, 10.1145\u002F1944339.1944341\nHurley, 2013, Personalised ranking with diversity, 379\nIoannidis, 2019, A recurrent graph neural network for multi-relational data, 8157\nJamali, 2010, A matrix factorization technique with trust propagation for recommendation in social networks, 135\nJavari, 2015, A probabilistic model to resolve diversity–accuracy challenge of recommendation systems, Knowledge and Information Systems, 44, 609, 10.1007\u002Fs10115-014-0779-2\nKaminskas, 2016, Diversity, serendipity, novelty, and coverage: Asurvey and empirical analysis of beyond-accuracy objectives in recommender systems, ACM Transactions on Interactive Intelligent Systems (TIIS), 7, 1\nKarypis, 2001, Evaluation of item-based top-n recommendation algorithms, 247\nKingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv:1412.6980.\nKunaver, 2015, Using latent features to measure the diversity of recommendation lists, 1230\nKunaver, 2017, Diversity in recommender systems–a survey, Knowledge-Based Systems, 123, 154, 10.1016\u002Fj.knosys.2017.02.009\nLipovetsky, 2015, Analytical closed-form solution for binary logit regression by categorical predictors, Journal of Applied Statistics, 42, 37, 10.1080\u002F02664763.2014.932760\nLiu, 2012, Solving the accuracy-diversity dilemma via directed random walks, Physical Review E, 85, 016118, 10.1103\u002FPhysRevE.85.016118\nMa, 2011, Recommender systems with social regularization, 287\nMateos, 2019, Connecting the dots: Identifying network structure via graph signal processing, IEEE Signal Processing Magazine, 36, 16, 10.1109\u002FMSP.2018.2890143\nMazumder, 2010, Spectral regularization algorithms for learning large incomplete matrices, Journal of Machine Learning Research, 11, 2287\nMonti, 2017, Geometric matrix completion with recurrent multi-graph neural networks, 3697\nNiemann, 2013, A new collaborative filtering approach for increasing the aggregate diversity of recommender systems, 955\nNikolakopoulos, 2019, Personalized diffusions for top-n recommendation, 260\nOrtega, 2018, Graph signal processing: Overview, challenges, and applications, Proceedings of the IEEE, 106, 808, 10.1109\u002FJPROC.2018.2820126\nPanniello, 2014, Comparing context-aware recommender systems in terms of accuracy and diversity, User Modeling and User-Adapted Interaction, 24, 35, 10.1007\u002Fs11257-012-9135-y\nRendle, 2009, BPR: Bayesian personalized ranking from implicit feedback, 452\nSaid, 2013, User-centric evaluation of a k-furthest neighbor collaborative filtering recommender algorithm, 1399\nSaid, 2012, Increasing diversity through furthest neighbor-based recommendation, Proceedings of the WSDM, 12\nSandryhaila, 2013, Discrete signal processing on graphs, IEEE Transactions on Signal Processing, 61, 1644, 10.1109\u002FTSP.2013.2238935\nSandryhaila, 2014, Discrete signal processing on graphs: Frequency analysis, IEEE Transactions on Signal Processing, 62, 3042, 10.1109\u002FTSP.2014.2321121\nSevi, H., Rilling, G., & Borgnat, P. (2018). Harmonic analysis on directed graphs and applications: From fourier analysis to wavelets. arXiv:1811.11636.\nShuman, 2013, The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains, IEEE Signal Processing Magazine, 30, 83, 10.1109\u002FMSP.2012.2235192\nSmyth, 2001, Similarity vs. diversity, 347\nSun, 2019, Multi-graph convolution collaborative filtering, 1306\nValcarce, 2018, On the robustness and discriminative power of information retrieval metrics for top-n recommendation, 260\nWang, 2019, Knowledge-aware graph neural networks with label smoothness regularization for recommender systems, 968\nWang, 2013, Combining user-based and item-based collaborative filtering techniques to improve recommendation diversity, 661\nWang, 2019, Neural graph collaborative filtering, 165\nWasilewski, 2016, Incorporating diversity in a learning to rank recommender system\nWu, Q., Liu, Y., Miao, C., Zhao, Y., Guan, L., & Tang, H. (2019). Recent advances in diversified recommendation. arXiv:1905.06589.\nWu, 2020, A comprehensive survey on graph neural networks, IEEE Transactions on Neural Networks and Learning Systems\nYang, 2019, AliGraph: A comprehensive graph neural network platform, 3165\nYing, 2018, Graph convolutional neural networks for web-scale recommender systems, 974\nZeng, 2010, Can dissimilar users contribute to accuracy and diversity of personalized recommendation?, International Journal of Modern Physics C, 21, 1217, 10.1142\u002FS0129183110015786\nZhang, 2008, Avoiding monotony: improving the diversity of recommendation lists, 123\nZhong, 2020, Hybrid graph convolutional networks with multi-head attention for location recommendation, World Wide Web, 1\nZhou, 2010, Solving the apparent diversity-accuracy dilemma of recommender systems, Proceedings of the National Academy of Sciences, 107, 4511, 10.1073\u002Fpnas.1000488107\nZiegler, 2005, Improving recommendation lists through topic diversification, 22",{"EN":534},"Accuracy-diversity trade-off in recommender systems via graph convolutions",{"VOID":536},"10.1016\u002Fj.ipm.2020.102459","2024-12-29T23:55:54.373+00:00","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0306457320309511",[540,557,569],{"id":541,"sortIndex":19,"researcher":18,"roles":542,"affiliations":543,"properties":554},"41810df0-8ae2-4876-8434-c6d95f8d98ed",[167],[544],{"id":18,"sortIndex":19,"affiliation":545,"properties":18},{"id":546,"createTime":547,"updateTime":548,"relativeEntities":549,"slug":550,"properties":551,"entityType":71,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"5e0c2777-a7d6-423a-b44d-82e109b9220b","2024-01-11T19:47:40.970+00:00","2024-12-19T02:27:46.180+00:00",[],"Faculty-of-Electrical-Engineering-Mathematics-and-Computer-Science-Delft-University-of-Technology-Delft-The-Netherlands",{"title":552},{"VI":553},"Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands",{"title":555},{"VI":556},"Elvin Isufi",{"id":558,"sortIndex":197,"researcher":18,"roles":559,"affiliations":560,"properties":566},"76ae3959-c3a5-463b-9488-6fe2e968bf49",[167],[561],{"id":18,"sortIndex":19,"affiliation":562,"properties":18},{"id":546,"createTime":547,"updateTime":548,"relativeEntities":563,"slug":550,"properties":564,"entityType":71,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":565},{"VI":553},{"title":567},{"VI":568},"Matteo Pocchiari",{"id":570,"sortIndex":165,"researcher":18,"roles":571,"affiliations":572,"properties":578},"49780325-d213-4355-8ce4-3eaede39419a",[167],[573],{"id":18,"sortIndex":19,"affiliation":574,"properties":18},{"id":546,"createTime":547,"updateTime":548,"relativeEntities":575,"slug":550,"properties":576,"entityType":71,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":577},{"VI":553},{"title":579},{"VI":580},"Alan Hanjalic",{"url":538,"publisher":582,"properties":611},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":583,"slug":10,"properties":584,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":587,"manageAffiliations":588,"indexDatabases":589,"url":18,"thumbnailPath":18,"statistic":18,"gsStatistic":18,"type":18,"analyzePriority":18},[],{"issn":585,"title":586},{"VOID":13},{"EN":15},[],[],[590,597,604],{"id":129,"indexDatabase":591,"url":102,"indexYears":18,"academicFieldIds":596,"indexDatabaseRanking":18},{"id":131,"createTime":132,"updateTime":133,"relativeEntities":592,"label":593,"description":594,"key":140,"publicationTags":595,"standard":18},[],{"EN":136,"VI":136},{"VI":138,"EN":139},[142,101],[144],{"id":87,"indexDatabase":598,"url":102,"indexYears":18,"academicFieldIds":603,"indexDatabaseRanking":18},{"id":89,"createTime":90,"updateTime":91,"relativeEntities":599,"label":600,"description":601,"key":98,"publicationTags":602,"standard":18},[],{"EN":94,"VI":94},{"VI":96,"EN":97},[100,101],[104],{"id":106,"indexDatabase":605,"url":119,"indexYears":120,"academicFieldIds":610,"indexDatabaseRanking":127},{"id":108,"createTime":109,"updateTime":110,"relativeEntities":606,"label":607,"description":608,"key":116,"publicationTags":609,"standard":18},[],{"EN":113,"VI":113},{"EN":113,"VI":115},[118],[122,123,124,125,126],{"volume":612,"pages":614},{"VOID":613},"58",{"VOID":615},"102459","2021-03-01",2021,{"id":619,"createTime":620,"updateTime":621,"relativeEntities":622,"slug":623,"properties":624,"entityType":160,"verifyStatus":263,"verifyTime":631,"verifyNote":264,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":632,"fullTextUrl":18,"authors":633,"publicationType":211,"publisherRelationship":649,"citationCount":18,"citationInfo":18,"publishDate":684,"publishYear":685,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":18,"isForceReanalyzing":249},"8afe5527-174c-4eff-8f74-9d39da4b940d","2024-01-24T14:35:35.027+00:00","2024-12-25T23:55:48.460+00:00",[],"Extended-co-citation-search-Graph-based-document-retrieval-on-a-co-citation-network-containing-citation-context-information",{"references":625,"title":627,"doi":629},{"VOID":626},"Abu-Jbara, 2013, Purpose and polarity of citation: Towards NLP-based bibliometrics, 596\nBhagavatula, 2018, Content-based citation recommendation, 1, 238\nBradshaw, 2003, Reference directed indexing: Redeeming relevance for subject search in citation indexes, 499\nBoyack, 2010, Co‐citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?, Journal of the American Society for Information Science and Technology, 61, 2389, 10.1002\u002Fasi.21419\nBoyack, 2013, Improving the accuracy of co-citation clustering using full text, Journal of the American Society for Information Science and Technology, 64, 1759, 10.1002\u002Fasi.22896\nBrin, 1998, The anatomy of a large-scale hypertextual web search engine, Computer Networks and ISDN Systems, 30, 107, 10.1016\u002FS0169-7552(98)00110-X\nChen, 2013, ASCOS: An asymmetric network structure context similarity measure, 442\nChu, 2016, Knowledge flow of biomedical informatics domain: Position-based co-citation analysis approach, 1119\nColavizza, 2018, The closer the better: similarity of publication pairs at different co-citation levels, Journal of the Association for Information Science and Technology, 69, 600, 10.1002\u002Fasi.23981\nEto, 2012, u co-citation relationship as a measure for document retrieval, 7\nEto, 2013, Evaluations of context-based co-citation searching, Scientometrics, 94, 651, 10.1007\u002Fs11192-012-0756-z\nEto, 2014, Document retrieval method using random walk with restart on weighted co-citation network\nEto, 2015, Combination effects of word-based and extended co-citation search algorithms, 245\nEl-Arini, 2011, Beyond keyword search: Discovering relevant scientific literature, 439\nFouss, 2007, Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation, IEEE Transactions on Knowledge and Data Engineering, 19, 355, 10.1109\u002FTKDE.2007.46\nFujiwara, 2012, Fast and exact top-k search for random walk with restart, Proceedings of the VLDB Endowment, 5, 442, 10.14778\u002F2140436.2140441\nGarzone, 2000, Towards an automated citation classifier, 337\nGanguly, 2017, Paper2vec: Combining graph and text information for scientific paper representation, 383\nGipp, 2009, Citation proximity analysis (CPA) - a new approach for identifying related work based on co-citation analysis, 2, 571\nGipp, 2015, CITREC: An evaluation framework for citation-based similarity measures based on TREC genomics and PubMed central, 2015\nGori, 2006, Research paper recommender systems: A random-walk based approach, 778\nGou, 2010, Social network document ranking, 313\nGrover, 2016, node2vec: Scalable feature learning for networks, 855\nHaveliwala, 2002, Topic-sensitive pagerank, 517\nHsiao, 2017, Yet another method for author co‐citation analysis: A new approach based on paragraph similarity, 54, 170\nJärvelin, 2002, Cumulated gain-based evaluation of IR techniques, ACM Transactions on Information Systems, 20, 422, 10.1145\u002F582415.582418\nJeh, 2002, SimRank: A measure of structural-context similarity, 538\nJia, 2017, An analysis of citation recommender systems: Beyond the obvious, 216\nJiang, 2015, Chronological citation recommendation with information-need shifting, 1291\nKatz, 1953, A new status index derived from sociometric analysis, Psychometrika, 18, 39, 10.1007\u002FBF02289026\nKessler, 1963, Bibliographic coupling between scientific papers, American Documentation, 14, 10, 10.1002\u002Fasi.5090140103\nKim, 2016, Content- and proximity-based author co-citation analysis using citation sentences, Journal of Informetrics, 10, 954, 10.1016\u002Fj.joi.2016.07.007\nKleinberg, 1999, Authoritative sources in a hyperlinked environment, Journal of the ACM, 46, 604, 10.1145\u002F324133.324140\nKobayashi, 2018, Citation recommendation using distributed representation of discourse facets in scientific articles, 243\nKonstas, 2009, On social networks and collaborative recommendation, 195\nKüçüktunç, 2012, Direction awareness in citation recommendation\nLe, 2006, Detecting citation types using finite-state machines, 265\nLiang, 2011, Finding relevant papers based on citation relations, 403\nLiben-Nowell, 2007, The link-prediction problem for social networks, Journal of the Association for Information Science and Technology, 58, 1019\nLin, 1998, An information-theoretic definition of similarity, 296\nLiu, 2012, The proximity of co-citation, Scientometrics, 91, 495, 10.1007\u002Fs11192-011-0575-7\nLiu, 2014, Meta-path-based ranking with pseudo relevance feedback on heterogeneous graph for citation recommendation, 121\nLu, 2007, Node similarity in the citation graph, Knowledge and Information Systems, 11, 105, 10.1007\u002Fs10115-006-0023-9\nLuong, 2012, Logical structure recovery in scholarly articles with rich document features, 270\nMacRoberts, 1989, Problems of citation analysis: A critical review, Journal of the American Society for Information Science, 40, 342, 10.1002\u002F(SICI)1097-4571(198909)40:5\u003C342::AID-ASI7>3.0.CO;2-U\nMeng, 2013, A unified graph model for personalized query-oriented reference paper recommendation., 1509\nMikolov, 2013, Distributed representations of words and phrases and their compositionality, 3111\nNanba, 2000, Classification of research papers using citation links and citation types: Towards automatic review article generation, 117\nPage, 1999\nPao, 1993, Term and citation retrieval: A field study, Information Processing and Management, 29, 95, 10.1016\u002F0306-4573(93)90026-A\nPham, 2003, A new approach for scientific citation classification using cue phrases, 759\nQasemiZadeh, 2010, Developing a dataset for technology structure mining, 32\nRitchie, 2008, Using terms from citations for IR: Some first results, 211\nSchwarzer, 2016, Evaluating link-based recommendations for Wikipedia, 191\nSmall, 1973, Co-citation in the scientific literature: A new measure of the relationship between two documents, Journal of the American Society for Information Science, 24, 265, 10.1002\u002Fasi.4630240406\nSmall, 1982, Citation context analysis, 3, 287\nSmall, 1974, The structure of scientific literatures I: Identifying and graphing specialties, Science Studies, 4, 17, 10.1177\u002F030631277400400102\nStrohman, 2007, Recommending citations for academic papers, 705\nSugiyama, 2010, Scholarly paper recommendation via user's recent research interests, 29\nSun, 2011, Pathsim: Meta path-based top-k similarity search in heterogeneous information networks, Proceedings of the VLDB Endowment, 4, 992, 10.14778\u002F3402707.3402736\nTang, 2015, LINE: Large-scale information network embedding, 1067\nTeufel, 2006, Automatic classification of citation function, 103\nTotti, 2016, A query-oriented approach for relevance in citation networks, 401\nValenzuela, 2015, Identifying meaningful citations, 21\nWhite, 2016, Bag of Works Retrieval: TF* IDF Weighting of Co-cited Works, 63\nYoon, 2016, C-Rank: A link-based similarity measure for scientific literature databases, Information Sciences, 326, 25, 10.1016\u002Fj.ins.2015.07.036\nZhao, 2009, P-Rank: A comprehensive structural similarity measure over information networks, 553\nZhou, 2008, Learning multiple graphs for document recommendations, 141",{"EN":628},"Extended co-citation search: Graph-based document retrieval on a co-citation network containing citation context information",{"VOID":630},"10.1016\u002Fj.ipm.2019.05.007","2024-12-25T23:55:48.459+00:00","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0306457318303637",[634],{"id":635,"sortIndex":19,"researcher":18,"roles":636,"affiliations":637,"properties":646},"e6ea0edb-69f9-416d-8bcb-24fef37723d5",[167],[638],{"id":18,"sortIndex":19,"affiliation":639,"properties":18},{"id":640,"createTime":641,"updateTime":641,"relativeEntities":642,"slug":18,"properties":643,"entityType":71,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"8cd759ee-7861-429b-9cb7-dd58a87f876d","2024-01-24T14:35:35.037+00:00",[],{"title":644},{"VI":645},"Gakushuin Women's College, 3-20-1 Toyama, Shinjuku-ku, Tokyo 1628650 Japan",{"title":647},{"VI":648},"Masaki Eto",{"url":632,"publisher":650,"properties":679},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":651,"slug":10,"properties":652,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":655,"manageAffiliations":656,"indexDatabases":657,"url":18,"thumbnailPath":18,"statistic":18,"gsStatistic":18,"type":18,"analyzePriority":18},[],{"issn":653,"title":654},{"VOID":13},{"EN":15},[],[],[658,665,672],{"id":129,"indexDatabase":659,"url":102,"indexYears":18,"academicFieldIds":664,"indexDatabaseRanking":18},{"id":131,"createTime":132,"updateTime":133,"relativeEntities":660,"label":661,"description":662,"key":140,"publicationTags":663,"standard":18},[],{"EN":136,"VI":136},{"VI":138,"EN":139},[142,101],[144],{"id":87,"indexDatabase":666,"url":102,"indexYears":18,"academicFieldIds":671,"indexDatabaseRanking":18},{"id":89,"createTime":90,"updateTime":91,"relativeEntities":667,"label":668,"description":669,"key":98,"publicationTags":670,"standard":18},[],{"EN":94,"VI":94},{"VI":96,"EN":97},[100,101],[104],{"id":106,"indexDatabase":673,"url":119,"indexYears":120,"academicFieldIds":678,"indexDatabaseRanking":127},{"id":108,"createTime":109,"updateTime":110,"relativeEntities":674,"label":675,"description":676,"key":116,"publicationTags":677,"standard":18},[],{"EN":113,"VI":113},{"EN":113,"VI":115},[118],[122,123,124,125,126],{"volume":680,"pages":682},{"VOID":681},"56",{"VOID":683},"102046","2019-11-01",2019,{"id":687,"createTime":688,"updateTime":689,"relativeEntities":690,"slug":691,"properties":692,"entityType":160,"verifyStatus":263,"verifyTime":689,"verifyNote":264,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":699,"fullTextUrl":18,"authors":700,"publicationType":211,"publisherRelationship":797,"citationCount":18,"citationInfo":18,"publishDate":832,"publishYear":833,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":18,"isForceReanalyzing":249},"27ecf378-6638-4696-98a6-1604d5fa463b","2024-02-14T01:33:53.907+00:00","2025-01-09T23:53:53.573+00:00",[],"Informed-Patch-Enhanced-HyperGCN-for-skeleton-based-action-recognition",{"references":693,"title":695,"doi":697},{"VOID":694},"Aggarwal, 2011, Human activity analysis: A review, ACM Computing Surveys, 43, 16, 10.1145\u002F1922649.1922653\nBai, 2018, Regularized diffusion process on bidirectional context for object retrieval, IEEE Transactions on Pattern Analysis and Machine Intelligence, 41, 1213, 10.1109\u002FTPAMI.2018.2828815\nBai, 2021, Hypergraph convolution and hypergraph attention, Pattern Recognition, 110, 10.1016\u002Fj.patcog.2020.107637\nBai, S., Zhou, Z., Wang, J., Bai, X., Jan Latecki, L., & Tian, Q. (2017). Ensemble diffusion for retrieval. In Proceedings of the IEEE international conference on computer vision (pp. 774–783).\nCai, 2021, JOLO-GCN: Mining joint-centered light-weight information for skeleton-based action recognition\nCao, 2016\nCarreira, 2017, Quo vadis, action recognition? A new model and the kinetics dataset\nCheng, 2020, Decoupling GCN with DropGraph module for skeleton-based action recognition\nCheng, K., Zhang, Y., He, X., Chen, W., Cheng, J., & Lu, H. (2020). Skeleton-based action recognition with shift graph convolutional network. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR).\nDefferrard, 2016, Convolutional neural networks on graphs with fast localized spectral filtering, 3844\nDu, Y., Wang, W., & Wang, L. (2015). Hierarchical recurrent neural network for skeleton based action recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1110–1118).\nDuan, 2021, Revisiting skeleton-based action recognition, Computer Vision and Pattern Recognition\nDuhme, 2021, Fusion-GCN: Multimodal action recognition using graph convolutional networks, 265\nDuvenaud, 2015, Convolutional networks on graphs for learning molecular fingerprints, 2224\nFeng, 2019, Hypergraph neural networks\nGao, 2012, 3-D object retrieval and recognition with hypergraph analysis, IEEE Transactions on Image Processing, 21, 4290, 10.1109\u002FTIP.2012.2199502\nHamilton, 2017, Inductive representation learning on large graphs, 1024\nHao, 2021, Hypergraph neural network for skeleton-based action recognition, IEEE Transactions on Image Processing, 30, 2263, 10.1109\u002FTIP.2021.3051495\nHenaff, 2015\nHu, 2018, Deep bilinear learning for RGB-d action recognition\nHuang, 2009, Video object segmentation by hypergraph cut\nHuang, 2010, Image retrieval via probabilistic hypergraph ranking\nJianan, 2020, Temporal graph modeling for skeleton-based action recognition, Computer Vision and Pattern Recognition\nKe, Q., Bennamoun, M., An, S., Sohel, F., & Boussaid, F. (2017). A new representation of skeleton sequences for 3d action recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3288–3297).\nKim, 2017, Interpretable 3d human action analysis with temporal convolutional networks, 1623\nKipf, 2018\nKipf, 2016\nLi, 2021, Decoupled pose and similarity based graph neural network for video person re-identification, IEEE Signal Processing Letters\nLi, 2019, Node-sensitive graph fusion via topo-correlation for image retrieval, IEEE Transactions on Circuits and Systems for Video Technology, 30, 3777, 10.1109\u002FTCSVT.2019.2944009\nLi, S., Li, W., Cook, C., Zhu, C., & Gao, Y. (2018). Independently recurrent neural network (indrnn): Building a longer and deeper rnn. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 5457–5466).\nLi, M., Siheng, C., Xu, C., Ya, Z., Yanfeng, W., & Qi, T. (2019). Actional-structural graph convolutional networks for skeleton-based action recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3595–3603).\nLi, 2017, Skeleton-based action recognition with convolutional neural networks, 597\nLi, Y., Zhou, H., Yin, Y., & Gao, J. (2021). Multi-label pattern image retrieval via attention mechanism driven graph convolutional network. In Proceedings of the 29th ACM international conference on multimedia (pp. 300–308).\nLiu, 2017, Enhanced skeleton visualization for view invariant human action recognition, Pattern Recognition, 68, 346, 10.1016\u002Fj.patcog.2017.02.030\nLiu, 2019, Ntu rgb+ d 120: A large-scale benchmark for 3d human activity understanding, IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 2684, 10.1109\u002FTPAMI.2019.2916873\nLiu, 2016, Spatio-temporal lstm with trust gates for 3d human action recognition, 816\nLiu, 2018, Skeleton-based human action recognition with global context-aware attention LSTM networks, IEEE Transactions on Image Processing, 27, 1586, 10.1109\u002FTIP.2017.2785279\nLiu, 2018, Recognizing human actions as the evolution of pose estimation maps\nLiu, Z., Zhang, H., Chen, Z., Wang, Z., & Ouyang, W. (2020). Disentangling and unifying graph convolutions for skeleton-based action recognition. In Proceedings of the IEEE\u002FCVF conference on computer vision and pattern recognition (CVPR).\nLong, 2019, Semantic graph convolutional networks for 3D human pose regression\nLuvizon, 2018, 2D\u002F3D pose estimation and action recognition using multitask deep learning\nNiepert, 2016, Learning convolutional neural networks for graphs, 2014\nPoppe, 2010, A survey on vision-based human action recognition, Image and Vision Computing, 28, 976, 10.1016\u002Fj.imavis.2009.11.014\nShahroudy, A., Liu, J., Ng, T.-T., & Wang, G. (2016). Ntu rgb+ d: A large scale dataset for 3d human activity analysis. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1010–1019).\nShi, L., Zhang, Y., Cheng, J., & Lu, H. (2019a). Skeleton-based action recognition with directed graph neural networks. In Proceedings of the IEEE\u002FCVF conference on computer vision and pattern recognition (pp. 7912–7921).\nShi, L., Zhang, Y., Cheng, J., & Lu, H. (2019b). Two-stream adaptive graph convolutional networks for skeleton-based action recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 12026–12035).\nShuman, 2013, The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains, IEEE Signal Processing Magazine, 30, 83, 10.1109\u002FMSP.2012.2235192\nSi, 2019, An attention enhanced graph convolutional LSTM network for skeleton-based action recognition\nTang, 2020\nWang, X., & Gupta, A. (2018). Videos as space-time region graphs. In Proceedings of the European conference on computer vision (ECCV) (pp. 399–417).\nWeinland, 2011, A survey of vision-based methods for action representation, segmentation and recognition, Computer Vision and Image Understanding, 115, 224, 10.1016\u002Fj.cviu.2010.10.002\nYan, 2018, Spatial temporal graph convolutional networks for skeleton-based action recognition\nYao, T., Pan, Y., Li, Y., & Mei, T. (2018). Exploring visual relationship for image captioning. In Proceedings of the European conference on computer vision (ECCV) (pp. 684–699).\nZhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., & Zheng, N. (2017). View adaptive recurrent neural networks for high performance human action recognition from skeleton data. In Proceedings of the IEEE international conference on computer vision (pp. 2117–2126).\nZhang, Z., Shi, Y., Yuan, C., Li, B., Wang, P., & Hu, W., et al. (2020). Object relational graph with teacher-recommended learning for video captioning. In Proceedings of the IEEE\u002FCVF conference on computer vision and pattern recognition (pp. 13278–13288).\nZhou, 2019, HEMlets pose: Learning part-centric heatmap triplets for accurate 3D human pose estimation\nZolfaghari, 2017, Chained multi-stream networks exploiting pose, motion, and appearance for action classification and detection",{"EN":696},"Informed Patch Enhanced HyperGCN for skeleton-based action recognition",{"VOID":698},"10.1016\u002Fj.ipm.2022.102950","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0306457322000723",[701,718,731,753,769,784],{"id":702,"sortIndex":19,"researcher":18,"roles":703,"affiliations":704,"properties":715},"747886d2-029d-4878-a553-d0040a537d2e",[167],[705],{"id":18,"sortIndex":19,"affiliation":706,"properties":18},{"id":707,"createTime":708,"updateTime":709,"relativeEntities":710,"slug":711,"properties":712,"entityType":71,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"091dbd1f-7865-4ef3-a503-092ecbcf4e69","2023-12-27T16:14:14.502+00:00","2025-06-11T22:04:41.217+00:00",[],"School-of-Electronic-Information-and-Electrical-Engineering-Shanghai-Jiao-Tong-University-Shanghai-200240-China",{"title":713},{"VI":714},"School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China",{"title":716},{"VI":717},"Yanjun Chen",{"id":719,"sortIndex":720,"researcher":18,"roles":721,"affiliations":722,"properties":728},"f19e9be2-2c1b-4ac4-a245-c749819de4d0",4,[167],[723],{"id":18,"sortIndex":19,"affiliation":724,"properties":18},{"id":707,"createTime":708,"updateTime":709,"relativeEntities":725,"slug":711,"properties":726,"entityType":71,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":727},{"VI":714},{"title":729},{"VI":730},"Yan Luo",{"id":732,"sortIndex":165,"researcher":18,"roles":733,"affiliations":734,"properties":750},"2eec983f-c26f-4bfc-b01c-93ab13d17d7e",[167],[735,745],{"id":736,"sortIndex":197,"affiliation":737,"properties":744},"40e984ff-c8fb-496b-9e66-5b3deec14e47",{"id":738,"createTime":739,"updateTime":739,"relativeEntities":740,"slug":18,"properties":741,"entityType":71,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"b34b011a-9e65-4bb4-a9c8-d6eeba6bba79","2023-12-29T12:56:31.814+00:00",[],{"title":742},{"VI":743},"MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai 200240, China",{},{"id":18,"sortIndex":19,"affiliation":746,"properties":18},{"id":707,"createTime":708,"updateTime":709,"relativeEntities":747,"slug":711,"properties":748,"entityType":71,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":749},{"VI":714},{"title":751},{"VI":752},"Chongyang Zhang",{"id":754,"sortIndex":755,"researcher":18,"roles":756,"affiliations":757,"properties":766},"76256bc0-85bf-4340-9fde-3b577bee7415",5,[167],[758],{"id":18,"sortIndex":19,"affiliation":759,"properties":18},{"id":760,"createTime":761,"updateTime":761,"relativeEntities":762,"slug":18,"properties":763,"entityType":71,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"2f7a7354-1c6f-4800-84fa-3b8477cc6131","2024-01-15T21:45:50.123+00:00",[],{"title":764},{"VI":765},"School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou 450001, China",{"title":767},{"VI":768},"Chuanping Hu",{"id":770,"sortIndex":197,"researcher":18,"roles":771,"affiliations":772,"properties":781},"027f8d72-04cd-4c7d-bf37-60017991a65f",[167],[773],{"id":18,"sortIndex":19,"affiliation":774,"properties":18},{"id":775,"createTime":776,"updateTime":776,"relativeEntities":777,"slug":18,"properties":778,"entityType":71,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"6c751b11-ddbd-4693-81cd-15b96f1d0596","2023-12-27T23:45:52.812+00:00",[],{"title":779},{"VI":780},"School of Computer and Electronic Information, Nanjing Normal University, Nanjing 210023, China",{"title":782},{"VI":783},"Ying Li",{"id":785,"sortIndex":786,"researcher":18,"roles":787,"affiliations":788,"properties":794},"30eb6fa6-7a56-4c2a-bed2-2244aad39186",3,[167],[789],{"id":18,"sortIndex":19,"affiliation":790,"properties":18},{"id":707,"createTime":708,"updateTime":709,"relativeEntities":791,"slug":711,"properties":792,"entityType":71,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":793},{"VI":714},{"title":795},{"VI":796},"Hao Zhou",{"url":699,"publisher":798,"properties":827},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":799,"slug":10,"properties":800,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":803,"manageAffiliations":804,"indexDatabases":805,"url":18,"thumbnailPath":18,"statistic":18,"gsStatistic":18,"type":18,"analyzePriority":18},[],{"issn":801,"title":802},{"VOID":13},{"EN":15},[],[],[806,813,820],{"id":129,"indexDatabase":807,"url":102,"indexYears":18,"academicFieldIds":812,"indexDatabaseRanking":18},{"id":131,"createTime":132,"updateTime":133,"relativeEntities":808,"label":809,"description":810,"key":140,"publicationTags":811,"standard":18},[],{"EN":136,"VI":136},{"VI":138,"EN":139},[142,101],[144],{"id":87,"indexDatabase":814,"url":102,"indexYears":18,"academicFieldIds":819,"indexDatabaseRanking":18},{"id":89,"createTime":90,"updateTime":91,"relativeEntities":815,"label":816,"description":817,"key":98,"publicationTags":818,"standard":18},[],{"EN":94,"VI":94},{"VI":96,"EN":97},[100,101],[104],{"id":106,"indexDatabase":821,"url":119,"indexYears":120,"academicFieldIds":826,"indexDatabaseRanking":127},{"id":108,"createTime":109,"updateTime":110,"relativeEntities":822,"label":823,"description":824,"key":116,"publicationTags":825,"standard":18},[],{"EN":113,"VI":113},{"EN":113,"VI":115},[118],[122,123,124,125,126],{"volume":828,"pages":830},{"VOID":829},"59",{"VOID":831},"102950","2022-07-01",2022,{"id":835,"createTime":836,"updateTime":836,"relativeEntities":837,"slug":18,"properties":838,"entityType":160,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":845,"fullTextUrl":18,"authors":846,"publicationType":211,"publisherRelationship":901,"citationCount":18,"citationInfo":18,"publishDate":936,"publishYear":937,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":18,"isForceReanalyzing":249},"5ceb4789-436c-4820-b817-d914c0054e4c","2023-12-06T23:53:14.097+00:00",[],{"references":839,"title":841,"doi":843},{"VOID":840},"Anisya, 2017, Implementation of haversine formula and best first search method in searching of tsunami evacuation route, IOP Conference Series: Earth and Environmental Science, 97\nBagci, 2015, Random walk based context-aware activity recommendation for location based social networks, 1\nBao, 2013, Location-based and preference-aware recommendation using sparse geo-social networking data, 199\nBao, 2015, Recommendations in location-based social networks: A survey, GeoInformatica, 19, 525, 10.1007\u002Fs10707-014-0220-8\nBaral, 2016, GeoTeCS: Exploiting geographical, temporal, categorical and social aspects for personalized poi recommendation, 94\nBawa-cavia, 2009, Sensing the urban: Using location-based social network data in urban analysis, 1\nBhargava, 2015, Who, what, when, and where: Multi-dimensional collaborative recommendations using tensor factorization on sparse user-generated data, 130\nBumrungkit, 2018, Statistical analysis of separation distance between equatorial plasma bubbles near suvarnabhumi international airport, Thailand, Journal of Geophysical Research-Space Physics, 123, 7858, 10.1029\u002F2018JA025612\nCelik, 2018, Discovering socially similar users in social media datasets based on their socially important locations, Information Processing and Management, 54, 1154, 10.1016\u002Fj.ipm.2018.08.004\nChen, 2015, On information coverage for location category based point-of-interest recommendation, 37\nCheng, 2016, A unified point-of-interest recommendation framework in location-based social networks, ACM Transactions on Intelligent Systems and Technology, 8, 1, 10.1145\u002F2901299\nCho, 2011, Friendship and mobility: User movement in location-based social networks, 1082\nDing, 2018, RecNet: A deep neural network for personalized POI recommendation in location-based social networks, International Journal of Geographical Information Science, 32, 1631, 10.1080\u002F13658816.2018.1447671\nDuan, 2019, Integrating geographical and temporal influences into location recommendation: A method based on check-ins, Information Technology and Management, 20, 73, 10.1007\u002Fs10799-018-0293-4\nDucheneaut, 2009, Collaborative filtering is not enough? Experiments with a mixed-model recommender for leisure activities, 295\nFerence, 2013, Location recommendation for out-of-town users in location-based social networks, 721\nGao, 2014, Addressing the cold-start problem in location recommendation using geo-social correlations, Data Mining and Knowledge Discovery, 29, 299, 10.1007\u002Fs10618-014-0343-4\nGao, 2018, A personalized point-of-interest recommendation model via fusion of geo-social information, Neurocomputing, 273, 159, 10.1016\u002Fj.neucom.2017.08.020\nGeng, 2019, A two-step personalized location recommendation based on multi-objective immune algorithm, Information Sciences, 475, 161, 10.1016\u002Fj.ins.2018.09.068\nHorozov, 2006, Using location for personalized POI recommendations in mobile environments, 6\nHu, 2014, Your neighbors affect your ratings: On geographical neighborhood influence to rating prediction, 345\nHuang, 2015, Point-of-interest recommendation in location-based social networks with personalized geo-social influence, China Communications, 12, 21, 10.1109\u002FCC.2015.7385525\nJamali, 2010, A matrix factorization technique with trust propagation for recommendation in social networks, 135\nKaramshuk, 2013, Geo-Spotting: Mining online location-based services for optimal retail store placement, 793\nLeung, 2011, CLR : A collaborative location recommendation framework based on co-clustering categories and subject descriptors, 305\nLin, 1998, An information-theoretic definition of similarity, ICML, 98, 296\nLiu, 2013, Learning geographical preferences for point-of-interest recommendation, 1043\nLiu, 2014, Personalized geo-specific tag recommendation for photos on social websites, IEEE Transactions on Multimedia, 16, 588, 10.1109\u002FTMM.2014.2302732\nLiu, 2013, Bayesian probabilistic matrix factorization with social relations and item contents for recommendation, Decision Support Systems, 55, 838, 10.1016\u002Fj.dss.2013.04.002\nLogesh, 2017, A reliable point of interest recommendation based on trust relevancy between users, Wireless Personal Communications, 97, 2751, 10.1007\u002Fs11277-017-4633-1\nLu, 2015, Exploiting geo-spatial preference for personalized expert recommendation, 67\nMajid, 2013, A context-aware personalized travel recommendation system based on geotagged social media data mining, International Journal of Geographical Information Science, 27, 662, 10.1080\u002F13658816.2012.696649\nMehmood, 2019, Design and development of a real-time optimal route recommendation system using big data for tourists in jeju island, Electronics, 8, 22, 10.3390\u002Felectronics8050506\nNoulas, 2012, A random walk around the city: New venue recommendation in location-based social networks, 144\nPednekar, 2018, Mapping pharmacy deserts and determining accessibility to community pharmacy services for elderly enrolled in a state pharmaceutical assistance program, PloS One, 13, 19, 10.1371\u002Fjournal.pone.0198173\nRehman, 2016, A comparative study of location-based recommendation systems, Knowledge Engineering Review, 32\nSarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. WWW, 1, 285–295. 10.1145\u002F371920.372071.\nScellato, 2011, Exploiting place features in link prediction on location-based social networks, 1046\nShin, 2019, Toward fair, accountable, and transparent algorithms: Case studies on algorithm initiatives in Korea and China, Javnost: The Public, 26, 274, 10.1080\u002F13183222.2019.1589249\nShin, 2019, How do technological properties influence user affordance of wearable technologies?, Interaction Studies, 20, 307, 10.1075\u002Fis.16024.shi\nShin, 2019, Computers in human behavior role of fairness, accountability, and transparency in algorithmic affordance, Computers in Human Behavior, 98, 277, 10.1016\u002Fj.chb.2019.04.019\nSpinsanti, 2010, Where you stop is who you are: Understanding people’s activities\nTobler, 2011, Cellular geography, 379\nValverde-Rebaza, 2018, The role of location and social strength for friendship prediction in location-based social networks, Information Processing and Management, 54, 475, 10.1016\u002Fj.ipm.2018.02.004\nWang, 2014, Location recommendation in location-based social networks using user check-in data, 374\nWang, 2017, Spatial-aware hierarchical collaborative deep learning for POI recommendation, IEEE Transactions on Knowledge and Data Engineering, 29, 2537, 10.1109\u002FTKDE.2017.2741484\nWinarno, 2017, Location based service for presence system using haversine method, 1\nWu, 2015, Location-aware service applied to mobile short message advertising: Design, development, and evaluation, Information Processing and Management, 51, 625, 10.1016\u002Fj.ipm.2015.06.001\nYe, 2011, Exploiting geographical influence for collaborative point-of-interest recommendation, 325\nYing, 2014, Semantic trajectory-based high utility item recommendation system, Expert Systems with Applications, 41, 4762, 10.1016\u002Fj.eswa.2014.01.042\nYu, 2015, Friend recommendation with content spread enhancement in social networks, Information Sciences, 309, 102, 10.1016\u002Fj.ins.2015.03.012\nYuan, 2013, Time-aware point-of-interest recommendation, 363\nZhang, 2015, GeoSoCa: Exploiting geographical, social and categorical correlations for point-of-interest recommendation, 443\nZhang, 2015, ORec: An opinion-based point-of-interest recommendation framework, 1641\nZhang, 2015, User preferences-based and time-sensitive location recommendation using check-in data, Journal of Computer and Communications, 03, 18, 10.4236\u002Fjcc.2015.39003\nZhao, 2013, Capturing geographical influence in POI recommendations, International Conference on Neural Information Processing, 530, 10.1007\u002F978-3-642-42042-9_66\nZhao, S., King, I., & Lyu, M.R. (.2016). A survey of point-of-interest recommendation in location-based social networks. ArXiv Preprint ArXiv:1607.00647.\nZhu, 2014, Understanding the adoption of location-based recommendation agents among active users of social networking sites, Information Processing and Management, 50, 675, 10.1016\u002Fj.ipm.2014.04.010",{"EN":842},"Location recommendation by combining geographical, categorical, and social preferences with location popularity",{"VOID":844},"10.1016\u002Fj.ipm.2020.102251","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0306457319306235",[847,862,874,886],{"id":848,"sortIndex":19,"researcher":18,"roles":849,"affiliations":850,"properties":859},"8fb68586-3e28-4659-8a99-080af0e1a29b",[167],[851],{"id":18,"sortIndex":19,"affiliation":852,"properties":18},{"id":853,"createTime":854,"updateTime":854,"relativeEntities":855,"slug":18,"properties":856,"entityType":71,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"3f82e755-b58c-4b18-8578-d001732a9bca","2023-12-06T23:53:14.112+00:00",[],{"title":857},{"VI":858},"Center for Studies of Information Resources, Wuhan University, Bayi Rd 20299, Wuhan 430072, China",{"title":860},{"VI":861},"Yaxue Ma",{"id":863,"sortIndex":786,"researcher":18,"roles":864,"affiliations":865,"properties":871},"cb8fbb8f-e088-4e1f-a0fc-5c0a88b6bedb",[167],[866],{"id":18,"sortIndex":19,"affiliation":867,"properties":18},{"id":853,"createTime":854,"updateTime":854,"relativeEntities":868,"slug":18,"properties":869,"entityType":71,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":870},{"VI":858},{"title":872},{"VI":873},"Gang Li",{"id":875,"sortIndex":197,"researcher":18,"roles":876,"affiliations":877,"properties":883},"7250e12b-7b43-44d7-8b43-4816f46e4aa8",[167],[878],{"id":18,"sortIndex":19,"affiliation":879,"properties":18},{"id":853,"createTime":854,"updateTime":854,"relativeEntities":880,"slug":18,"properties":881,"entityType":71,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":882},{"VI":858},{"title":884},{"VI":885},"Jin Mao",{"id":887,"sortIndex":165,"researcher":18,"roles":888,"affiliations":889,"properties":898},"06a01007-ec55-4398-8d44-447711899ae0",[167],[890],{"id":18,"sortIndex":19,"affiliation":891,"properties":18},{"id":892,"createTime":893,"updateTime":893,"relativeEntities":894,"slug":18,"properties":895,"entityType":71,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"451d3ab2-8046-452a-aebb-cb1c0426083d","2023-12-06T23:53:14.140+00:00",[],{"title":896},{"VI":897},"Department of Information Management, Nanjing University of Science and Technology, Xiaolingwei St. 200, Nanjing 210094, China",{"title":899},{"VI":900},"Zhichao Ba",{"url":845,"publisher":902,"properties":931},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":903,"slug":10,"properties":904,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":907,"manageAffiliations":908,"indexDatabases":909,"url":18,"thumbnailPath":18,"statistic":18,"gsStatistic":18,"type":18,"analyzePriority":18},[],{"issn":905,"title":906},{"VOID":13},{"EN":15},[],[],[910,917,924],{"id":129,"indexDatabase":911,"url":102,"indexYears":18,"academicFieldIds":916,"indexDatabaseRanking":18},{"id":131,"createTime":132,"updateTime":133,"relativeEntities":912,"label":913,"description":914,"key":140,"publicationTags":915,"standard":18},[],{"EN":136,"VI":136},{"VI":138,"EN":139},[142,101],[144],{"id":87,"indexDatabase":918,"url":102,"indexYears":18,"academicFieldIds":923,"indexDatabaseRanking":18},{"id":89,"createTime":90,"updateTime":91,"relativeEntities":919,"label":920,"description":921,"key":98,"publicationTags":922,"standard":18},[],{"EN":94,"VI":94},{"VI":96,"EN":97},[100,101],[104],{"id":106,"indexDatabase":925,"url":119,"indexYears":120,"academicFieldIds":930,"indexDatabaseRanking":127},{"id":108,"createTime":109,"updateTime":110,"relativeEntities":926,"label":927,"description":928,"key":116,"publicationTags":929,"standard":18},[],{"EN":113,"VI":113},{"EN":113,"VI":115},[118],[122,123,124,125,126],{"volume":932,"pages":934},{"VOID":933},"57",{"VOID":935},"102251","2020-07-01",2020,{"id":939,"createTime":940,"updateTime":941,"relativeEntities":942,"slug":943,"properties":944,"entityType":160,"verifyStatus":263,"verifyTime":951,"verifyNote":264,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":952,"fullTextUrl":18,"authors":953,"publicationType":211,"publisherRelationship":984,"citationCount":18,"citationInfo":18,"publishDate":1019,"publishYear":1020,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":18,"isForceReanalyzing":249},"1c550c7e-b9e1-47e7-98a4-e2a9d5ad2f7d","2024-01-12T01:25:00.193+00:00","2025-01-09T23:50:48.268+00:00",[],"The-use-of-query-suggestions-during-information-search",{"references":945,"title":947,"doi":949},{"VOID":946},"Allen, 1991, Topic knowledge and online catalog search formulation, Library Quarterly, 61, 188, 10.1086\u002F602333\nAula, 2010\nBates, 1977, Factors affecting subject catalog search success, Journal of the American Society for Information Science, 28, 161, 10.1002\u002Fasi.4630280304\nBates, 1979, Information search tactics, Journal of the American Society for Information Science, 30, 205, 10.1002\u002Fasi.4630300406\nBates, 1979, Idea tactics, Journal of the American Society for Information Science, 30, 280, 10.1002\u002Fasi.4630300507\nBell, D. & Ruthven, I. (2004). Searcher’s assessments of task complexity for Web searching. In Proceedings of the 26th European Conference on Information Retrieval (ECIR ‘04) (pp. 57–71). Sunderland, UK, April 5–7, 2004.\nByström, 2005, Conceptual framework for tasks in information studies, Journal of the American Society for Information Science and Technology, 56, 1050, 10.1002\u002Fasi.20197\nByström, 1995, Task complexity affects information seeking and use, Information Processing and Management, 31, 191, 10.1016\u002F0306-4573(94)00041-Z\nChapman, 1981, A state transition analysis of online information-seeking behavior, Journal of the American Society for Information Science, 32, 325, 10.1002\u002Fasi.4630320503\nChen, 1991, Cognitive process as a basis for intelligent retrieval systems design, Information Processing and Management: An International Journal, 27, 405, 10.1016\u002F0306-4573(91)90060-Y\nChen, 2001, Using clustering techniques to detect usage patterns in a Web-based information system, Journal of the American Society for Information Science and Technology, 52, 888, 10.1002\u002Fasi.1159\nChen, 2002, Stochastic modeling of usage patterns in a web-based information system, Journal of the American Society for Information Science and Technology, 53, 536, 10.1002\u002Fasi.10076\nDebowski, 2001, The impact of guided exploration and enactive exploration on self-regulatory mechanisms and information acquisition through electronic enquiry, Journal of Applied Psychology, 86, 1129, 10.1037\u002F0021-9010.86.6.1129\nFidel, 1983, Factors affecting online bibliographic retrieval: A conceptual framework for research, Journal of the American Society for Information Science, 34, 163, 10.1002\u002Fasi.4630340302\nFidel, 1985, Moves in online searching. Online, Review, 9, 61\nFreyne, J., Farzan, R., Brusilovsky, P., Smyth, B., & Coyle, M. (2007). Collecting community wisdom: Integrating social search & social navigation. In Proceedings of the 12th International Conference on Intelligent User Interfaces (IUI ‘07) (pp. 52–61). January 28–31, 2007.\nGlance, N. S. (2001). Community search assistant. In Proceedings of the 6th International Conference on Intelligent User Interfaces (IUI ‘01) (pp. 91–96). Santa Fe, NM, January 14–17, 2001.\nGwizdka, J. (2008). Revisiting search task difficulty: Behavioral and individual difference measures. In Proceedings of the 71st Annual Meeting of the American Society for Information Science & Technology (ASIST ‘08). Columbus, OH, October 24–29, 2008.\nGwizdka, J. & Spence, I. (2006). What can searching behavior tell us about the difficulty of information tasks? A study of Web navigation. In Proceedings of the 69th Annual Meeting of the American Society for Information Science & Technology (ASIST ‘06), 43. Austin, TX, November 3–8, 2006.\nHembrooke, 2005, The effects of expertise and feedback on search term selection and subsequent learning, Journal of the American Society for Information Science and Technology, 56, 861, 10.1002\u002Fasi.20180\nHoelscher, 2000, Web search behavior of Internet experts and newbies, Computer Networks, 33, 337, 10.1016\u002FS1389-1286(00)00031-1\nHsieh-Yee, 1993, Effects of search experience and subject knowledge on the search tactics of novice and experienced searchers, Journal of the American Society for Information Science, 44, 161, 10.1002\u002F(SICI)1097-4571(199304)44:3\u003C161::AID-ASI5>3.0.CO;2-8\nIngwersen, 2005\nJansen, 2009, Patterns of query reformulation during Web search, Journal of the American Society for Information Science and Technology, 60, 1358, 10.1002\u002Fasi.21071\nKellar, 2007, A field study characterizing Web-based information-seeking tasks, Journal of the American Society for Information Science and Technology, 58, 999, 10.1002\u002Fasi.20590\nKelly, D., Cushing, A., Dostert, M., Niu, X., & Gyllstom, K. (2010). Effects of popularity and quality on the usage of query suggestions during information search. In Proceeding of the 28th ACM Conference on Human Factors in Computing Systems (CHI ‘10) (pp. 45–54). Atlanta, GA, April 10–15, 2010.\nKelly, D., Gyllstrom, K., & Bailey, E. W. (2009). A comparison of term and query suggestion features for interactive searching. In Proceedings of the 32th Annual ACM International Conference on Research and Development in Information Retrieval (SIGIR ‘09) (pp. 371–378). Boston, MA, July 19–23, 2009.\nKim, J. (2006). Task difficulty as a predictor and indicator of web searching interaction. In Proceedings of the 24th ACM Conference on Human Factors in Computing Systems (CHI ‘06) (pp. 959–964). Montreal, Quebec, Canada, April 22–27, 2006.\nKim, S. & Soergel, D. (2005). Selecting and measuring task characteristics as independent variables. In Proceedings of the 68th Annual Meeting of the American Society for Information Science & Technology (ASIST ‘05), 42. Charlotte, NC, October 28–November 2, 2005.\nKuhlthau, 1994\nLazonder, 2000, Differences between novice and experienced users in searching information on the World Wide Web, Journal of the American Society for Information Science & Technology, 51, 576, 10.1002\u002F(SICI)1097-4571(2000)51:6\u003C576::AID-ASI9>3.0.CO;2-7\nLemur IR Toolkit. \u003Chttp:\u002F\u002Fwww.lemurproject.org\u002F>.\nLi, 2008, A faceted approach to conceptualizing tasks in information seeking, Information Processing & Management, 44, 1822, 10.1016\u002Fj.ipm.2008.07.005\nLi, 2010, An exploration of the relationship between work task and interactive information search behavior, Journal of the American Society for Information Science and Technology, 61, 1771, 10.1002\u002Fasi.21359\nLiu, J., Gwizdka, J., Liu, C., & Belkin, N. J. (2010). Predicting task difficulty for different task types. In Proceedings of the 73rd Annual Meeting of the American Society for Information Science & Technology (ASIST ‘10), 47. Pittsburg, PA, October 22–27, 2010.\nMoore, 2007, The search experience variable in information behavior research, Journal of the American Society for Information Science & Technology, 58, 1529, 10.1002\u002Fasi.20635\nO’Day, V. L., & Jeffries, R. (1993). Orienteering in an Information Landscape: How Information Seekers Get From Here to There. In Proceedings of the 11th ACM Conference on Human Factors in Computing Systems (CHI ‘93) (pp. 438–445). Amsterdam, The Netherlands, April 24–29, 1993.\nQiu, 1993, Markov models of search state patterns in a hypertext information retrieval system, Journal of the American Society for Information Science, 44, 413, 10.1002\u002F(SICI)1097-4571(199308)44:7\u003C413::AID-ASI7>3.0.CO;2-D\nRieh, 2006, Analysis of multiple query reformulations on the Web: The interactive information retrieval context, Information Processing & Management, 42, 751, 10.1016\u002Fj.ipm.2005.05.005\nShiri, 2003, The effects of topic complexity and familiarity on cognitive and physical moves in a thesaurus-enhanced search environment, Journal of Information Science, 29, 517, 10.1177\u002F0165551503296008\nShute, 1993, Knowledge-based search tactics, Information Processing and Management, 29, 29, 10.1016\u002F0306-4573(93)90021-5\nSihvonen, A. & Vakkari, P. (2004). Subject knowledge, thesaurus-assisted query expansion and search success. In Proceedings of the 7th International Conference on Computer-Assisted Information Retrieval (RIAO ‘04) (pp. 393–404). Avignon, France, April 26–28, 2004.\nSilvestri, 2010, Mining query logs: Turning search usage data into knowledge, Foundations and Trends in Information Retrieval, 4, 1, 10.1561\u002F1500000013\nSmith, C. (2008). Searcher adaptation: A response to topic difficulty. In Proceedings of the 71rd Annual Meeting of the American Society for Information Science & Technology (ASIST ‘08), 45. Columbus, OH, October 24–29, 2008.\nSmyth, 2004, Exploiting query repetition and regularity in an adaptive community-based Web search Engine, User Modeling and User-Adapted Interaction, 14, 382, 10.1007\u002Fs11257-004-5270-4\nSutcliffe, 2000, Empirical studies of end-user information searching, Journal of the American Society for Information Science, 51, 1211, 10.1002\u002F1097-4571(2000)9999:9999\u003C::AID-ASI1033>3.0.CO;2-5\nTeevan, J., Alvarado, C., Ackerman, M. S., & Krager, D. R. (2004). The perfect search engine is not enough: A study of orienteering behavior in directed search. In Proceedings of the Conference on Human Factors in Computing Systems (CHI ‘04) (pp. 415–422). Vienna, Austria, April 24–29, 2004.\nToms, 2011, Task-based information searching and retrieval\nVakkari, 2001, Changes in search tactics and relevance judgments when preparing a research proposal: A summary of the findings of a longitudinal study, Information Retrieval, 4, 295, 10.1023\u002FA:1016089224008\nVakkari, 2003, Task-based information searching, Annual Review of Information Science and Technology, 37, 413, 10.1002\u002Faris.1440370110\nVakkari, 2003, Changes of search terms and tactics while writing a research proposal: A longitudinal case study, Information Processing and Management, 39, 445, 10.1016\u002FS0306-4573(02)00031-6\nVoorhees, E. M. (2006). Overview of the TREC 2005 Robust Retrieval Track. In Proceedings of 14th Annual Text Retrieval Conference, (TREC-14). Gaithersburg, MD, November 16, 2005.\nWang, 1997\nWhite, R. W., Bilenko, M., & Cucerzan, S. (2007). Studying the use of popular destinations to enhance web search interaction. In Proceedings of the 30th Annual ACM International Conference on Research and Development in Information Retrieval (SIGIR ‘07) (pp. 159–166). Amsterdam, The Netherlands, July 23–27, 2007.\nWhite, R. W., Dumais, S. T., & Teevan, J. (2009). Characterizing the influence of domain expertise on Web search behavior. In Proceedings of the 2nd International Conference on Web Search and Data Mining (WSDM ‘09) (pp. 132–141). Barcelona, Spain, February 9–12, 2009.\nWhite, R. W. & Morris, D. (2007). Investigating the querying and browsing behavior of advanced search engine users. In Proceedings of the 30th Annual ACM International Conference on Research and Development in Information Retrieval (SIGIR ‘07) (pp. 255–262). Amsterdam, The Netherlands, July 23–27, 2007.\nWhite, R. W., Ruthven, I. & Jose, J. M. (2005). A study of factors affecting the utility of implicit relevance feedback. In Proceedings of the 28th Annual ACM International Conference on Research and Development in Information Retrieval (SIGIR ‘05) (pp. 35–42). Salvador, Brazil, August 15–19, 2005.\nWildemuth, 2004, The effects of domain knowledge on search tactic formulation, Journal of the American Society for Information Science and Technology, 55, 246, 10.1002\u002Fasi.10367\nWildemuth, 2009\nWildemuth, B. M. & Freund, L. (2009) Search tasks and their role in studies of search behaviors. In Proceedings of the 3rd Annual Workshop on Human Computer Interaction and Information Retrieval (HCIR ‘09), (pp. 17–21). Washington D.C., October 23, 2009.\nXie, 2008\nXie, 2000, Shifts of interactive intentions and information-seeking strategies in interactive information retrieval, Journal of the American Society for Information Science, 51, 841, 10.1002\u002F(SICI)1097-4571(2000)51:9\u003C841::AID-ASI70>3.0.CO;2-0\nXie, 2010, Transitions in search tactics during the Web-based search process, Journal of the American Society for Information Science & Technology, 61, 2188, 10.1002\u002Fasi.21391\nYuan, 1997, End-user searching behavior in information retrieval: A longitudinal study, Journal of the American Society for Information Science, 43, 218, 10.1002\u002F(SICI)1097-4571(199703)48:3\u003C218::AID-ASI4>3.0.CO;2-#",{"EN":948},"The use of query suggestions during information search",{"VOID":950},"10.1016\u002Fj.ipm.2013.09.002","2025-01-09T23:50:48.267+00:00","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS030645731300099X",[954,969],{"id":955,"sortIndex":19,"researcher":18,"roles":956,"affiliations":957,"properties":966},"702b21d0-7b6e-44d7-bd8c-9fe96d223986",[167],[958],{"id":18,"sortIndex":19,"affiliation":959,"properties":18},{"id":960,"createTime":961,"updateTime":961,"relativeEntities":962,"slug":18,"properties":963,"entityType":71,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"4a0ec15a-c0fe-4896-85b1-5b6afad92b31","2024-01-12T01:25:00.203+00:00",[],{"title":964},{"VI":965},"School of Informatics and Computing, Indiana University at Indianapolis, Indianapolis, IN 46202, USA",{"title":967},{"VI":968},"Xi Niu",{"id":970,"sortIndex":197,"researcher":18,"roles":971,"affiliations":972,"properties":981},"257fd258-b27e-41ef-aaac-6223d9fcbbb0",[167],[973],{"id":18,"sortIndex":19,"affiliation":974,"properties":18},{"id":975,"createTime":976,"updateTime":976,"relativeEntities":977,"slug":18,"properties":978,"entityType":71,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"e0206a58-5156-4453-9d61-34805742ce1e","2024-02-08T22:11:47.203+00:00",[],{"title":979},{"VI":980},"School of Information and Library Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-3360, USA",{"title":982},{"VI":983},"Diane Kelly",{"url":952,"publisher":985,"properties":1014},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":986,"slug":10,"properties":987,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":990,"manageAffiliations":991,"indexDatabases":992,"url":18,"thumbnailPath":18,"statistic":18,"gsStatistic":18,"type":18,"analyzePriority":18},[],{"issn":988,"title":989},{"VOID":13},{"EN":15},[],[],[993,1000,1007],{"id":129,"indexDatabase":994,"url":102,"indexYears":18,"academicFieldIds":999,"indexDatabaseRanking":18},{"id":131,"createTime":132,"updateTime":133,"relativeEntities":995,"label":996,"description":997,"key":140,"publicationTags":998,"standard":18},[],{"EN":136,"VI":136},{"VI":138,"EN":139},[142,101],[144],{"id":87,"indexDatabase":1001,"url":102,"indexYears":18,"academicFieldIds":1006,"indexDatabaseRanking":18},{"id":89,"createTime":90,"updateTime":91,"relativeEntities":1002,"label":1003,"description":1004,"key":98,"publicationTags":1005,"standard":18},[],{"EN":94,"VI":94},{"VI":96,"EN":97},[100,101],[104],{"id":106,"indexDatabase":1008,"url":119,"indexYears":120,"academicFieldIds":1013,"indexDatabaseRanking":127},{"id":108,"createTime":109,"updateTime":110,"relativeEntities":1009,"label":1010,"description":1011,"key":116,"publicationTags":1012,"standard":18},[],{"EN":113,"VI":113},{"EN":113,"VI":115},[118],[122,123,124,125,126],{"volume":1015,"pages":1017},{"VOID":1016},"50",{"VOID":1018},"218-234","2014-01-01",2014,{"id":1022,"createTime":1023,"updateTime":1024,"relativeEntities":1025,"slug":1026,"properties":1027,"entityType":160,"verifyStatus":263,"verifyTime":1024,"verifyNote":264,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":1034,"fullTextUrl":18,"authors":1035,"publicationType":211,"publisherRelationship":1089,"citationCount":18,"citationInfo":18,"publishDate":1123,"publishYear":685,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":18,"isForceReanalyzing":249},"07da060d-ee3b-4691-8c82-e744e155e891","2023-12-12T06:10:50.199+00:00","2024-12-20T23:50:11.526+00:00",[],"Item-diversified-recommendation-based-on-influence-diffusion",{"references":1028,"title":1030,"doi":1032},{"VOID":1029},"Adamopoulos, 2014, On unexpectedness in recommender systems: Or how to better expect the unexpected, ACM Transactions on Intelligent Systems and Technology, 5, 1, 10.1145\u002F2559952\nAdomavicius, 2012, Improving aggregate recommendation diversity using ranking-based techniques, IEEE Transactions on Knowledge and Data Engineering, 24, 896, 10.1109\u002FTKDE.2011.15\nAgrawal, 2009, Diversifying search results, 5\nBarbieri, 2012, Topic-aware social influence propagation models, 81\nBelém, 2013, Topic diversity in tag recommendation, 141\nBi, 2014, Scalable topic-specific influence analysis on microblogs, 513\nBlei, 2003, Latent dirichlet allocation, Journal of Machine Learning Research, 3, 993\nCarbonell, 1998, The use of mmr, diversity-based reranking for reordering documents and producing summaries, 335\nCha, 2012, Social-network analysis using topic models, 565\nChatterjee, 1990, The innovation diffusion process in a heterogeneous population: A micromodeling approach, Management Science, 36, 1057, 10.1287\u002Fmnsc.36.9.1057\nChen, 2012, Time-critical influence maximization in social networks with time-delayed diffusion process, 592\nClarke, 2008, Novelty and diversity in information retrieval evaluation, 659\nDang, 2012, Diversity by proportionality: an election-based approach to search result diversification, 65\nFang, 2016, Effective community search for large attributed graphs, Proceedings of the VLDB Endowment, 9, 1233, 10.14778\u002F2994509.2994538\nGan, 2013, Constructing a user similarity network to remove adverse influence of popular objects for personalized recommendation, Expert Systems with Applications, 40, 4044, 10.1016\u002Fj.eswa.2013.01.004\nGollapudi, S., & Sharma, A. (2009). An axiomatic approach for result diversification. In www.acm (pp. 381–390).\nGuo, 2015, Social-relational topic model for social networks, 1731\nHeinrich, 2005\nHu, 2015, Community level diffusion extraction, 1555\nIwata, 2010, Online multiscale dynamic topic models, 663\nKalish, 1985, A new product adoption model with price, advertising, and uncertainty, Management Science, 31, 1569, 10.1287\u002Fmnsc.31.12.1569\nLee, 2015, Escaping your comfort zone: A graph-based recommender system for finding novel recommendations among relevant items, Expert Systems with Applications, 42, 4851, 10.1016\u002Fj.eswa.2014.07.024\nLi, 2017, Toward time-evolving feature selection on dynamic networks, 1003\nLiang, 2017, Collaborative user clustering for short text streams, 3504\nLiang, 2016, Dynamic clustering of streaming short documents, 995\nLiu, 2010, Mining topic-level influecne in heterogeneous networks, 199\nQin, 2013, Promoting diversity in recommendation by entropy regularizer, 2698\nRen, 2017, Social collaborative viewpoint regression with explainable recommendations, 485\nSantos, R. L. T., Macdonald, C., & Ounis, I. (2010). Exploiting query reformulations for web search result diversification. In www.acm (pp. 881–890).\nSu, 2015, Quadratic program-based modularity maximization for fuzzy community detection in social networks, IEEE Transactions on Fuzzy Systems, 23, 1356, 10.1109\u002FTFUZZ.2014.2360723\nWu, 2016, Relevance meets coverage:a unified framework to generate diversified recommendations, ACM Transactions on Intelligent Systems and Technology, 7, 1, 10.1145\u002F2700496\nYan, X., Guo, J., Lan, Y., & Cheng, X. (2013). A biterm topic model for short texts. In www.acm (pp. 1445–1456).\nYang, 2013, Community detection in networks with node attributes, 1151\nYang, 2015, Parametric and non-parametric user-aware sentiment topic models, 413\nYin, 2014, Lcars: A spatial item recommender system, ACM Transactions on Information Systems, 32, 11, 10.1145\u002F2629461\nYin, 2014, A dirichlet multinomial mixture model-based approach for short text clustering, 233\nZhai, 2015, Beyond independent relevance: Methods and evaluation metrics for subtopic retrieval, ACM SIGIR Forum, 49, 2, 10.1145\u002F2795403.2795405\nZhang, 2017, Hierarchical community-level information diffusion modeling in social networks, 753\nZhao, 2016, Explainable user clustering in short text streams, 155\nZiegler, C. N., Mcnee, S. M., Konstan, J. A., & Lausen, G. (2005). Improving recommendation lists through topic diversification. In www.acm (pp. 22–32).",{"EN":1031},"Item diversified recommendation based on influence diffusion",{"VOID":1033},"10.1016\u002Fj.ipm.2019.01.006","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0306457318306988",[1036,1065,1077],{"id":1037,"sortIndex":197,"researcher":18,"roles":1038,"affiliations":1039,"properties":1062},"af9013b9-adb8-4654-b4da-a8021455fd3a",[167],[1040,1052],{"id":1041,"sortIndex":197,"affiliation":1042,"properties":1051},"3a9f67ea-27d7-408b-a712-ec59f57f9b39",{"id":1043,"createTime":1044,"updateTime":1045,"relativeEntities":1046,"slug":1047,"properties":1048,"entityType":71,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"00864420-200d-48b8-856c-ec8ed437410f","2023-12-07T07:11:02.655+00:00","2025-06-11T14:01:18.851+00:00",[],"School-of-Computer-Science-University-of-Adelaide-Adelaide-Australia",{"title":1049},{"VI":1050},"School of Computer Science, University of Adelaide, Adelaide, Australia",{},{"id":18,"sortIndex":19,"affiliation":1053,"properties":18},{"id":1054,"createTime":1055,"updateTime":1056,"relativeEntities":1057,"slug":1058,"properties":1059,"entityType":71,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"17fc01b7-bb88-4282-945e-6f779f556e7b","2023-12-13T18:13:59.643+00:00","2025-02-05T15:47:33.355+00:00",[],"School-of-Data-and-Computer-Science-Sun-Yat-Sen-University-Guangzhou-China",{"title":1060},{"VI":1061},"School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, China",{"title":1063},{"VI":1064},"Hong Shen",{"id":1066,"sortIndex":165,"researcher":18,"roles":1067,"affiliations":1068,"properties":1074},"8fb9cddc-f1c3-4edd-aaef-bde9f8a67179",[167],[1069],{"id":18,"sortIndex":19,"affiliation":1070,"properties":18},{"id":1054,"createTime":1055,"updateTime":1056,"relativeEntities":1071,"slug":1058,"properties":1072,"entityType":71,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":1073},{"VI":1061},{"title":1075},{"VI":1076},"Zaiqiao Meng",{"id":1078,"sortIndex":19,"researcher":18,"roles":1079,"affiliations":1080,"properties":1086},"35d8f942-6b82-49e5-81ce-dd82ca83d046",[167],[1081],{"id":18,"sortIndex":19,"affiliation":1082,"properties":18},{"id":1054,"createTime":1055,"updateTime":1056,"relativeEntities":1083,"slug":1058,"properties":1084,"entityType":71,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":1085},{"VI":1061},{"title":1087},{"VI":1088},"Huimin Huang",{"url":1034,"publisher":1090,"properties":1119},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1091,"slug":10,"properties":1092,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":1095,"manageAffiliations":1096,"indexDatabases":1097,"url":18,"thumbnailPath":18,"statistic":18,"gsStatistic":18,"type":18,"analyzePriority":18},[],{"issn":1093,"title":1094},{"VOID":13},{"EN":15},[],[],[1098,1105,1112],{"id":129,"indexDatabase":1099,"url":102,"indexYears":18,"academicFieldIds":1104,"indexDatabaseRanking":18},{"id":131,"createTime":132,"updateTime":133,"relativeEntities":1100,"label":1101,"description":1102,"key":140,"publicationTags":1103,"standard":18},[],{"EN":136,"VI":136},{"VI":138,"EN":139},[142,101],[144],{"id":87,"indexDatabase":1106,"url":102,"indexYears":18,"academicFieldIds":1111,"indexDatabaseRanking":18},{"id":89,"createTime":90,"updateTime":91,"relativeEntities":1107,"label":1108,"description":1109,"key":98,"publicationTags":1110,"standard":18},[],{"EN":94,"VI":94},{"VI":96,"EN":97},[100,101],[104],{"id":106,"indexDatabase":1113,"url":119,"indexYears":120,"academicFieldIds":1118,"indexDatabaseRanking":127},{"id":108,"createTime":109,"updateTime":110,"relativeEntities":1114,"label":1115,"description":1116,"key":116,"publicationTags":1117,"standard":18},[],{"EN":113,"VI":113},{"EN":113,"VI":115},[118],[122,123,124,125,126],{"volume":1120,"pages":1121},{"VOID":681},{"VOID":1122},"939-954","2019-05-01"]