Explainable multi-task convolutional neural network framework for electronic petition tag recommendation
Tài liệu tham khảo
Abu-Shanab, 2019, E-government research insights: Text mining analysis, Electron. Commer. Res. Appl., 38, 10.1016/j.elerap.2019.100892
Alathur, 2012, Citizen participation and effectiveness of e-petition: Sutharyakeralam-India, Transf. Govern.: People Process Policy, 6, 392, 10.1108/17506161211267536
Aljazzaf, 2020, E-participation model for Kuwait e-government, (IJACSA) Int. J. Adv. Comput. Sci. Appl., 11
Alshibly, 2015, Customer empowerment: Does it influence electronic government success? A citizen-centric perspective, Electron. Commer. Res. Appl., 14, 393, 10.1016/j.elerap.2015.05.003
Anduiza, 2010, Online political participation in Spain: The impact of traditional and internet resources, J. Inf. Technol. Politics, 7, 356, 10.1080/19331681003791891
Ayachi, 2016, Proactive and reactive e-government services recommendation, Univ. Access Inf. Soc., 15, 681, 10.1007/s10209-015-0442-z
Bach, 2015, On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation, PLoS One, 10, 10.1371/journal.pone.0130140
Baldi, 2013, Understanding dropout, Adv. Neural Inf. Process. Syst., 26
Batista, 2004, A study of the behavior of several methods for balancing machine learning training data, ACM SIGKDD Explor. Newsl., 6, 20, 10.1145/1007730.1007735
Belém, 2017, A survey on tag recommendation methods, J. Assoc. Inf. Sci. Technol., 68, 830, 10.1002/asi.23736
Bellini, 2018, Knowledge-aware autoencoders for explainable recommender systems, 24
Bershadskaya, 2014, Measurement techniques for e-participation assessment: Case of Russian e-petitions portal, 395
Bershadskaya, 2013, E-participation development: A comparative study of the Russian, USA and UK e-petition initiatives, 73
Bobadilla, 2013, Recommender systems survey, Knowl.-Based Syst., 46, 109, 10.1016/j.knosys.2013.03.012
Chen, 2019, Semi-supervised learning based tag recommendation for Docker repositories, J. Comput. Sci. Tech., 34, 957, 10.1007/s11390-019-1954-4
Colesca, 2008, Adoption and use of e-government services: The case of Romania, J. Appl. Res. Technol., 6, 204, 10.22201/icat.16656423.2008.6.03.526
Cruickshank, 2010, Signing an e-petition as a transition from lurking to participation, 275
Dominguez, 2019, The effect of explanations and algorithmic accuracy on visual recommender systems of artistic images, 408
Dumas, 2015, Examining political mobilization of online communities through e-petitioning behavior in We the people, Big Data Soc., 2, 10.1177/2053951715598170
Dyczkowski, 2012, A recommender system with uncertainty on the example of political elections, 441
Estevez, 2013, Electronic governance for sustainable development — conceptual framework and state of research, Gov. Inf. Q., 30, S94, 10.1016/j.giq.2012.11.001
Gao, 2020, State-led digital governance in contemporary china, 29
Gao, 2016, Accurate segmentation of CT male pelvic organs via regression-based deformable models and multi-task random forests, IEEE Trans. Med. Imaging, 35, 1532, 10.1109/TMI.2016.2519264
Gibreel, 2017, A holistic analysis approach to social, technical, and socio-technical aspect of E-government development, Sustainability, 9, 2181, 10.3390/su9122181
Glorot, 2011, Deep sparse rectifier neural networks, 315
Grave, E., Bojanowski, P., Gupta, P., Joulin, A., Mikolov, T., 2018. Learning Word Vectors for 157 Languages. In: Proceedings of the International Conference on Language Resources and Evaluation. LREC 2018.
Gupta, 2010, Survey on social tagging techniques, ACM SIGKDD Expl. Newslett., 12, 58, 10.1145/1882471.1882480
Guy, 2010, Social media recommendation based on people and tags, 194
Hagen, 2018, Content analysis of e-petitions with topic modeling: How to train and evaluate LDA models?, Inf. Process. Manage., 54, 1292, 10.1016/j.ipm.2018.05.006
Hagen, 2018, Data analytics for policy informatics: The case of E-petitioning, 205
Hagen, 2016, E-petition popularity: Do linguistic and semantic factors matter?, Gov. Inf. Q., 33, 783, 10.1016/j.giq.2016.07.006
Hajiramezanali, E., Dadaneh, S.Z., Karbalayghareh, A., Zhou, M., Qian, X., 2018. Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data. In: Advances in Neural Information Processing Systems. pp. 9115–9124.
Hale, 2013, Petition growth and success rates on the UK No. 10 downing street website, 132
Harbin Institute of Technology, 2022
Hassan, 2018, Semantic-based tag recommendation in scientific bookmarking systems, 465
Heitin, 2016, What is digital literacy, Educ. Week, 36, 5
Hernon, 2002
Hessel, M., Modayil, J., Van Hasselt, H., Schaul, T., Ostrovski, G., Dabney, W., Horgan, D., Piot, B., Azar, M., Silver, D., 2018. Rainbow: Combining improvements in deep reinforcement learning. In: Thirty-Second AAAI Conference on Artificial Intelligence. http://dx.doi.org/10.1609/aaai.v32i1.11796.
Hinton, 2012
Ho, 1995, Random decision forests, 278
Hoerl, 2000, Ridge regression: Biased estimation for nonorthogonal problems, Technometrics, 42, 80, 10.1080/00401706.2000.10485983
Hossain, 2011, Impacts of organizational assimilation of e-government systems on business value creation: A structuration theory approach, Electron. Commer. Res. Appl., 10, 576, 10.1016/j.elerap.2010.12.003
Jaeger, 2003, The endless wire: E-government as global phenomenon, Gov. Inf. Q., 4, 323, 10.1016/j.giq.2003.08.003
Jiang, 2019, From internet to social safety net: The policy consequences of online participation in China, Governance, 32, 531, 10.1111/gove.12391
Jiang, 2009, Exploring online structures on Chinese government portals: Citizen political participation and government legitimation, Soc. Sci. Comput. Rev., 27, 174, 10.1177/0894439308327313
Jieba, 2022
Jochumsen, 2012, The four spaces – a new model for the public library, New library world, 113, 586, 10.1108/03074801211282948
Jungherr, 2010, The political click: Political participation through E-petitions in Germany, Policy Internet, 2, 131, 10.2202/1944-2866.1084
Kenny, 2021, Explaining black-box classifiers using post-hoc explanations-by-example: The effect of explanations and error-rates in XAI user studies, Artificial Intelligence, 10.1016/j.artint.2021.103459
Kim, 2020, Sentiment digitization modeling for recommendation system, Sustainability, 12, 5191, 10.3390/su12125191
Kraus, 2019, Forecasting remaining useful life: Interpretable deep learning approach via variational Bayesian inferences, Decis. Support Syst., 125, 10.1016/j.dss.2019.113100
Krestel, 2009, Latent Dirichlet allocation for tag recommendation, 61
Lee, 2020, A multi-period product recommender system in online food market based on recurrent neural networks, Sustainability, 12, 969, 10.3390/su12030969
Li, 2019, The object selection and behavior characteristics of citizen’s interest claim through Internet — The big data analysis based on a national E-government platform (in Chinese), Explore, 6, 91
Li, 2008, Tag-based social interest discovery, 675
Li, 2019, Discursive strategy of opinion expression and government response in China: Text analysis based on online petitions, Telemat. Inform., 42, 10.1016/j.tele.2019.06.001
Li, 2019, The responsive trap of digital government governance — Based on the investigation of ’local leadership message boards’ in the three eastern provinces of China (in Chinese), E-Government, 3, 72
Lindner, 2009, Electronic petitions and institutional modernization. International parliamentary e-petition systems in comparative perspective, JeDEM - EJ. EDemocracy Open Govern., 1, 1, 10.29379/jedem.v1i1.3
Lindner, 2011, Broadening participation through e-petitions? An empirical study of petitions to the German parliament, Policy Internet, 3, 1, 10.2202/1944-2866.1083
Liu, 2016, “Province-Managing-County” fiscal reform, land expansion, and urban growth in China, J. Hous. Econ., 33, 82, 10.1016/j.jhe.2016.05.002
Liu, 2009, Multi-task feature learning via efficient l2, 1-norm minimization, 339
Liu, 2017, A survey of deep neural network architectures and their applications, Neurocomputing, 234, 11, 10.1016/j.neucom.2016.12.038
Ma, 2020, Mapping the evolution of the central government apparatus in China, Int. Rev. Administrat. Sci., 86, 80, 10.1177/0020852317749025
McDonald, 2009, Ridge regression, Wiley Interdiscip. Rev. Comput. Stat., 1, 93, 10.1002/wics.14
Meng, 2020, Variety of responsive institutions and quality of responsiveness in cyber China, China Rev., 20, 13
Message Board for Leaders, 2022
Messina, 2019, Content-based artwork recommendation: Integrating painting metadata with neural and manually-engineered visual features, User Model. User-Adapt. Interact., 29, 251, 10.1007/s11257-018-9206-9
Mohammed, 2020, Machine learning with oversampling and undersampling techniques: Overview study and experimental results, 243
Montavon, 2019, Layer-wise relevance propagation: An overview, 193
Montavon, 2017, Explaining nonlinear classification decisions with deep Taylor decomposition, Pattern Recognit., 65, 211, 10.1016/j.patcog.2016.11.008
Nguyen, 2017, Personalized deep learning for tag recommendation, 186
Noble, 2006, What is a support vector machine?, Nature Biotechnol., 24, 1565, 10.1038/nbt1206-1565
Panagiotopoulos, 2011, Do social networking groups support online petitions?, Transf. Govern.: People, Process Policy, 5, 20, 10.1108/17506161111114626
Preisach, 2010, Semi-supervised tag recommendation - Using untagged resources to mitigate cold-start problems, 348
Reusens, 2017, A note on explicit versus implicit information for job recommendation, Decis. Support Syst., 98, 26, 10.1016/j.dss.2017.04.002
Rodriguez-Hevía, 2020, Citizens’ involvement in E-government in the European Union: The rising importance of the digital skills, Sustainability, 12, 6807, 10.3390/su12176807
Sabucedo, 2012, A hybrid semantic driven recommender for services in the egovernment domain, 409
Sæbø, 2008, The shape of eParticipation: Characterizing an emerging research area, Gov. Inf. Q., 25, 400, 10.1016/j.giq.2007.04.007
Santamaría-Philco, 2018, Trust in e-participation: An empirical research on the influencing factors, 1
Scherer, 2014, Trust in e-participation: Literature review and emerging research needs, 61
Shao, 2020, Pendulum response: An explanation of insufficient responsiveness based on the message board of local leaders in S city (in Chinese), Comparison Econ. Soc. Syst., 1, 114
Shi, 2017, Correlation-aware multi-label active learning for web service tag recommendation, 229
Song, 2011, Automatic tag recommendation algorithms for social recommender systems, ACM Trans. Web (TWEB), 5, 1, 10.1145/1921591.1921595
Steinbach, 2020, E-participation on the local level – A census survey approach for researching its implementation, J. Inf. Technol. Politics, 17, 12, 10.1080/19331681.2019.1676361
Su, 2016, Selective responsiveness: Online public demands and government responsiveness in authoritarian China, Soc. Sci. Res., 59, 52, 10.1016/j.ssresearch.2016.04.017
Sun, 2011, Towards more accurate retrieval of duplicate bug reports, 253
Tai, 2020, Can e-participation stimulate offline citizen participation: An empirical test with practical implications, Public Manag. Rev., 22, 278, 10.1080/14719037.2019.1584233
Tajbakhsh, 2016, Microblogging hash tag recommendation system based on semantic TF-IDF: Twitter use case, 252
Terán, 2019, Dynamic profiles using sentiment analysis and twitter data for voting advice applications, Gov. Inf. Q., 36, 520, 10.1016/j.giq.2019.03.003
Tibshirani, 1996, Regression shrinkage and selection via the Lasso, J. R. Stat. Soc. Ser. B Stat. Methodol., 58, 267, 10.1111/j.2517-6161.1996.tb02080.x
UK Government and Parliament, 2022
Verkijika, 2018, E-government adoption in sub-Saharan Africa, Electron. Commer. Res. Appl., 30, 83, 10.1016/j.elerap.2018.05.012
van der Waa, 2020, Interpretable confidence measures for decision support systems, Int. J. Hum.-Comput. Stud., 144
Wang, Y., Wang, S., Tang, J., Qi, G., Liu, H., Li, B., 2017. CLARE: A joint approach to label classification and tag recommendation. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence. pp. 210–216. http://dx.doi.org/10.1609/aaai.v31i1.10479.
Wang, 2019, What were residents’ petitions in Beijing-based on text mining, J. Urban Manag., 9, 228, 10.1016/j.jum.2019.11.006
We the People, 2020
Xu, 2019, An adaptive wordpiece language model for learning Chinese word embeddings, 812
Yang, 2007, How do Chinese civic associations respond to the internet? Findings from a survey, China Q., 189, 122, 10.1017/S030574100600083X
Yang, 2020, Causally denoise word embeddings using half-sibling regression, 9426
Yao, 2015, Subnational leaders and economic growth: Evidence from Chinese cities, J. Econ. Growth, 20, 405, 10.1007/s10887-015-9116-1
Zhang, 2018, Improved Adam optimizer for deep neural networks, 1
Zhang, 2020, Explainable recommendation: A survey and new perspectives, Found. Trends® Inf. Retrieval, 14, 1, 10.1561/1500000066
Zhang, Y., Liu, Y., Zhu, J., Zheng, Z., Liu, X., Wang, W., Chen, Z., Zhai, S., 2019. Learning Chinese word embeddings from stroke, structure and pinyin of characters. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management. pp. 1011–1020. http://dx.doi.org/10.1145/3357384.3358005.
Zhong, 2017, Topic representation: A novel method of tag recommendation for text, 671
Zhou, 2019, Is deep learning better than traditional approaches in tag recommendation for software information sites?, Inf. Softw. Technol., 109, 1, 10.1016/j.infsof.2019.01.002
Zhou, 2017, Scalable tag recommendation for software information sites, 272
Zolotov, 2018, E-participation adoption models research in the last 17 years: A weight and meta-analytical review, Comput. Hum. Behav., 81, 350, 10.1016/j.chb.2017.12.031