A hybrid recommender system for patron driven library acquisition and weeding
Journal of King Saud University - Computer and Information Sciences - Tập 34 - Trang 2809-2819 - 2022
Tài liệu tham khảo
Ferguson, 2007
Johnson, 2018, Fundamentals of collection development and management, Am. Lib. Assoc.
Kao, 2003, Decision support for the academic library acquisition budget allocation via circulation database mining, Inf. Process. Manage., 39, 133, 10.1016/S0306-4573(02)00019-5
Evans, 1995
Dilevko, 2003, Weed to achieve: a fundamental part of the public library mission?, Lib. Collect., Acquisit., Tech. Serv., 27, 73, 10.1080/14649055.2003.10765897
J.M. Maness, Library 2.0 theory: Web 2.0 and its implications for libraries, Webology 3 (2).
Curran, 2006, Involving the user through library 2.0, New Rev. Inf. Network., 12, 47, 10.1080/13614570601136263
Reinhalter, 2014, The library: big data’s boomtown, Serials Lib., 67, 363, 10.1080/0361526X.2014.915605
Affelt, A., 2017. Big Data, Big Opportunity for Librarians and Information Professionals, Emerald Publishing Limited, Ch. 32, pp. 761–790
Resnick, 1997, Recommender systems, Commun. ACM, 40, 56, 10.1145/245108.245121
Burke, 2002, Hybrid recommender systems: survey and experiments, User Model. User-Adapt. Interact., 12, 331, 10.1023/A:1021240730564
Adomavicius, 2005, Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions, IEEE Trans. Knowl. Data Eng., 17, 734, 10.1109/TKDE.2005.99
Mooney, R.J., Roy, L., 2000. Content-based book recommending using learning for text categorization, in: Proceedings of the Fifth ACM Conference on Digital Libraries, DL ’00, ACM, New York, NY, USA, pp. 195–204.
Pazzani, 2007, 325
Lops, 2011, Content-based recommender systems: State of the art and trends, 73
Linden, 2003, Amazon. com recommendations: Item-to-item collaborative filtering, IEEE Internet Comput., 76, 10.1109/MIC.2003.1167344
Y. Hu, Y. Koren, C. Volinsky, Collaborative filtering for implicit feedback datasets, in: 2008 Eighth IEEE International Conference on Data Mining, Ieee, 2008, pp. 263–272.
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.-S., 2017. Neural collaborative filtering, in: Proceedings of the 26th International Conference on World Wide Web, WWW ’17, International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, pp. 173–182.
Wang, Y., Chan, S.C.-F., Ngai, G., 2012. Applicability of demographic recommender system to tourist attractions: a case study on trip advisor, in: 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, vol. 3, IEEE, pp. 97–101
Zhao, 2014, We know what you want to buy: a demographic-based system for product recommendation on microblogs, 1935
Al-Shamri, 2016, User profiling approaches for demographic recommender systems, Knowl.-Based Syst., 100, 175, 10.1016/j.knosys.2016.03.006
Huang, 2011, Designing utility-based recommender systems for e-commerce: evaluation of preference-elicitation methods, Electron. Commerce Res. Appl., 10, 398, 10.1016/j.elerap.2010.11.003
Zheng, 2019, Utility-based multi-criteria recommender systems, 2529
Zihayat, 2019, A utility-based news recommendation system, Decis. Support Syst., 117, 14, 10.1016/j.dss.2018.12.001
Towle, B., Quinn, C., 2000. Knowledge based recommender systems using explicit user models, in: Proceedings of the AAAI Workshop on Knowledge-Based Electronic Markets, pp. 74–77.
Martínez, 2008, A knowledge based recommender system with multigranular linguistic information, Int. J. Comput. Intell. Syst., 1, 225, 10.1080/18756891.2008.9727620
Aggarwal, C.C., 2016. Knowledge-based recommender systems, in: Recommender Systems, Springer, pp. 167–197.
Balabanović, 1997, Fab: content-based, collaborative recommendation, Commun. ACM, 40, 66, 10.1145/245108.245124
Burke, 2007, 377
Strub, F., Gaudel, R., Mary, J., 2016. Hybrid recommender system based on autoencoders, in: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, DLRS 2016, ACM, New York, NY, USA, pp. 11–16.
Reitz, 2004, Dictionary for library and information science, Lib. Unlimit.
Saponaro, M.Z., Evans, G.E., 2019. Collection Management Basics, ABC-CLIO, 2019.
Jia, 2011, Patron driven acquisitions (pda): new library acquisition practices and related issues, J. Lib. Sci. China, 2, 36
Coghill, 2019, Patron-driven acquisition (pda), J. Electron. Resour. Med. Libr., 16, 25, 10.1080/15424065.2019.1596776
Goedeken, 2015, The past, present, and future of demand-driven acquisitions in academic libraries, College Res. Lib., 76, 205, 10.5860/crl.76.2.205
Huang, Z., Chung, W., Ong, T.-H., Chen, H., 2002. A graph-based recommender system for digital library, in: Proceedings of the 2nd ACM/IEEE-CS Joint conference on Digital libraries, ACM, pp. 65–73
Liao, 2009, Pore: a personal ontology recommender system for digital libraries, Electron. Lib., 27, 496, 10.1108/02640470910966925
Wakeling, S., 2012. The user-centered design of a recommender system for a universal library catalogue, in: Proceedings of the Sixth ACM Conference on Recommender Systems, ACM, pp. 337–340
Yousfi, 2019, Mixed-profiling recommender systems for big data environment, 79
Simovic, 2018, A big data smart library recommender system for an educational institution, Lib. Hi Tech, 36, 498, 10.1108/LHT-06-2017-0131
Porcel, 2009, A multi-disciplinar recommender system to advice research resources in university digital libraries, Expert Syst. Appl., 36, 12520, 10.1016/j.eswa.2009.04.038
Serrano-Guerrero, 2011, A google wave-based fuzzy recommender system to disseminate information in university digital libraries 2.0, Inf. Sci., 181, 1503, 10.1016/j.ins.2011.01.012
Kun, 2012, Research of personalized book recommender system of university library based on collaborative filter, Data Anal. Knowl. Discovery, 44
Jomsri, P., 2014. Book recommendation system for digital library based on user profiles by using association rule, in: 2014 Fourth International Conference on Innovative Computing Technology (INTECH), IEEE, pp. 130–134
Subramaniyaswamy, 2017, Adaptive knn based recommender system through mining of user preferences, Wireless Personal Commun., 97, 2229, 10.1007/s11277-017-4605-5
Lee, 2017, Improving personalized recommendations using community membership information, Inf. Process. Manage., 53, 1201, 10.1016/j.ipm.2017.05.005
Zhou, 2019, Personalized recommendation via user preference matching, Inf. Process. Manage., 56, 955, 10.1016/j.ipm.2019.02.002
Chen, 2015, Recommender systems based on user reviews: the state of the art, User Model. User-Adapt. Interact., 25, 99, 10.1007/s11257-015-9155-5
Guo, 2016, A novel recommendation model regularized with user trust and item ratings, IEEE Trans. Knowl. Data Eng., 28, 1607, 10.1109/TKDE.2016.2528249
Rubens, 2015, 809
Y. Bu, K. Small, Active learning in recommendation systems with multi-level user preferences, arXiv preprint arXiv:1811.12591.
Ma, 2017, Finding users preferences from large-scale online reviews for personalized recommendation, Electron. Commerce Res., 17, 3, 10.1007/s10660-016-9240-9
Zheng, L., Noroozi, V., Yu, P.S., 2017. Joint deep modeling of users and items using reviews for recommendation, in: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, ACM, pp. 425–434
Musto, C., de Gemmis, M., Semeraro, G., Lops, P., 2017. A multi-criteria recommender system exploiting aspect-based sentiment analysis of users’ reviews, in: Proceedings of the Eleventh ACM Conference on Recommender Systems, RecSys ’17, ACM, New York, NY, USA, pp. 321–325.
Lu, Y., Dong, R., Smyth, B., 2018. Coevolutionary recommendation model: Mutual learning between ratings and reviews, in: Proceedings of the 2018 World Wide Web Conference, International World Wide Web Conferences Steering Committee, pp. 773–782.
Tejeda-Lorente, 2014, A quality based recommender system to disseminate information in a university digital library, Inf. Sci., 261, 52, 10.1016/j.ins.2013.10.036
Morawski, 2017, A fuzzy recommender system for public library catalogs, Int. J. Intell. Syst., 32, 1062, 10.1002/int.21884
Yang, 2012, A model for book inquiry history analysis and book-acquisition recommendation of libraries, Libr. Collect., Acquisit., Tech. Serv., 36, 127, 10.1016/j.lcats.2012.05.001
Wu, 2017, Developing a novel recommender network-based ranking mechanism for library book acquisition, Electron. Libr., 35, 50, 10.1108/EL-06-2015-0094
Giri, R., Sen, B.K., Mahesh, G. Collection development in indian academic libraries: an empirical approach to determine the number of copies for acquisition, DESIDOC J. Libr. Inf. Technol. 35 (3).
Cabrerizo, 2015, A decision support system to develop a quality management in academic digital libraries, Inf. Sci., 323, 48, 10.1016/j.ins.2015.06.022
Cabrerizo, 2017, A fuzzy linguistic extended libqual+ model to assess service quality in academic libraries, Int. J. Inf. Technol. Decis. Mak., 16, 225, 10.1142/S0219622015500406
Lee, 2000, What is a collection?, J. Am. Soc. Inf. Sci., 51, 1106, 10.1002/1097-4571(2000)9999:9999<::AID-ASI1018>3.0.CO;2-T
Dresselhaus, 2016, Literature of acquisitions in review, 2012–13, Libr. Resour. Tech. Serv., 60, 169
Jan, S., Ganiae, S.A., 2019. Trends in collection & collection development practices in university libraries with a particular reference to india and other developing countries: a review of literature, Libr. Philos. Pract. 0_1–17.
Terzi, 2011, Free text in user reviews: their role in recommender systems, 45
AL-Sharuee, M.T., Liu, F., Pratama, M., 2018. Sentiment analysis: an automatic contextual analysis and ensemble clustering approach and comparison, Data Knowl. Eng. 115, 194–213.
Rhanoui, 2019, A cnn-bilstm model for document-level sentiment analysis, Mach. Learn. Knowl. Extract., 1, 832, 10.3390/make1030048
Massa, 2007, Trust-aware recommender systems, 17
Rafailidis, 2017, Learning to rank with trust and distrust in recommender systems, 5
Hassan, 2019, Trust and trustworthiness in social recommender systems, Companion Proceedings of The 2019 World Wide Web Conference, 529, 10.1145/3308560.3317596
Ma, 2011, Recommender systems with social regularization, 287
Guo, 2014, From ratings to trust: an empirical study of implicit trust in recommender systems, 248
Massa, 2009, 259
Victor, 2011, 645
Benesty, 2009, 1
Settles, B., 2009. Active learning literature survey, Tech. rep., University of Wisconsin-Madison Department of Computer Sciences