{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,11]],"date-time":"2025-04-11T10:29:20Z","timestamp":1744367360503},"reference-count":24,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. &amp; Syst."],"published-print":{"date-parts":[[2022,2,1]]},"DOI":"10.1587\/transinf.2021edp7034","type":"journal-article","created":{"date-parts":[[2022,1,31]],"date-time":"2022-01-31T22:16:58Z","timestamp":1643667418000},"page":"355-363","source":"Crossref","is-referenced-by-count":2,"title":["Hierarchical Preference Hash Network for News Recommendation"],"prefix":"10.1587","volume":"E105.D","author":[{"given":"Jianyong","family":"DUAN","sequence":"first","affiliation":[{"name":"College of Informatics, North China University of Technology"},{"name":"CNONIX National Standard Application and Promotion Lab"}]},{"given":"Liangcai","family":"LI","sequence":"additional","affiliation":[{"name":"College of Informatics, North China University of Technology"},{"name":"CNONIX National Standard Application and Promotion Lab"}]},{"given":"Mei","family":"ZHANG","sequence":"additional","affiliation":[{"name":"College of Informatics, North China University of Technology"}]},{"given":"Hao","family":"WANG","sequence":"additional","affiliation":[{"name":"College of Informatics, North China University of Technology"},{"name":"CNONIX National Standard Application and Promotion Lab"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"[1] H. Wang, F. Zhang, X. Xie, and M. Guo, \u201cDKN: Deep Knowledge-Aware Network for News Recommendation,\u201d Proc. 2018 World Wide Web Conference, pp.1835-1844, International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 2018. 10.1145\/3178876.3186175","DOI":"10.1145\/3178876.3186175"},{"key":"2","doi-asserted-by":"crossref","unstructured":"[2] S. Ge, C. Wu, F. Wu, T. Qi, and Y. Huang, \u201cGraph Enhanced Representation Learning for News Recommendation,\u201d Proc. Web Conference 2020, pp.2863-2869, Association for Computing Machinery, New York, NY, USA, 2020. 10.1145\/3366423.3380050","DOI":"10.1145\/3366423.3380050"},{"key":"3","doi-asserted-by":"crossref","unstructured":"[3] W. IJntema, F. Goossen, F. Frasincar, and F. Hogenboom, \u201cOntology-based news recommendation,\u201d Proc. 2010 EDBT\/ICDT Workshops, EDBT &apos;10, New York, NY, USA, Association for Computing Machinery, 2010. 10.1145\/1754239.1754257","DOI":"10.1145\/1754239.1754257"},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] S. Okura, Y. Tagami, S. Ono, and A. Tajima, \u201cEmbedding-based news recommendation for millions of users,\u201d the 23rd ACM SIGKDD International Conference, 2017. 10.1145\/3097983.3098108","DOI":"10.1145\/3097983.3098108"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] M. An, F. Wu, C. Wu, K. Zhang, and X. Xie, \u201cNeural news recommendation with long- and short-term user representations,\u201d The 57th Annual Meeting of the Association for Computational Linguistics, 2019.","DOI":"10.18653\/v1\/P19-1033"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] C. Wu, F. Wu, M. An, J. Huang, Y. Huang, and X. Xie, \u201cNeural news recommendation with attentive multi-view learning,\u201d the 28th International Joint Conference on Artificial Intelligence, 2019. 10.24963\/ijcai.2019\/536","DOI":"10.24963\/ijcai.2019\/536"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] C. Wu, F. Wu, S. Ge, T. Qi, and X. Xie, \u201cNeural news recommendation with multi-head self-attention,\u201d Proc. 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 2019.","DOI":"10.18653\/v1\/D19-1671"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] Q. Zhu, X. Zhou, Z. Song, J. Tan, and L. Guo, \u201cDan: Deep attention neural network for news recommendation,\u201d Proc. AAAI Conference on Artificial Intelligence, vol.33, no.01, pp.5973-5980, July 2019. 10.1609\/aaai.v33i01.33015973","DOI":"10.1609\/aaai.v33i01.33015973"},{"key":"9","unstructured":"[9] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, and I. Polosukhin, \u201cAttention is all you need,\u201d Advances in neural information processing systems, pp.5998-6008, 2017."},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] F. Wu, Y. Qiao, J.-H. Chen, C. Wu, T. Qi, J. Lian, D. Liu, X. Xie, J. Gao, W. Wu, and M. Zhou, \u201cMIND: A large-scale dataset for news recommendation,\u201d Proc. 58th Annual Meeting of the Association for Computational Linguistics, Online, pp.3597-3606, Association for Computational Linguistics, July 2020. 10.18653\/v1\/2020.acl-main.331","DOI":"10.18653\/v1\/2020.acl-main.331"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] J. Liu, P. Dolan, and E.R. Pedersen, \u201cPersonalized news recommendation based on click behavior,\u201d International Conference on Intelligent User Interfaces, 2010. 10.1145\/1719970.1719976","DOI":"10.1145\/1719970.1719976"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] J.-W. Son, A.-Y. Kim, and S.-B. Park, \u201cA location-based news article recommendation with explicit localized semantic analysis,\u201d International Acm Sigir Conference on Research Development in Information Retrieval, 2013. 10.1145\/2484028.2484064","DOI":"10.1145\/2484028.2484064"},{"key":"13","unstructured":"[13] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, \u201cDropout: A simple way to prevent neural networks from overfitting,\u201d Journal of Machine Learning Research, vol.15, no.1, pp.1929-1958, 2014."},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] S. Shi, W. Ma, M. Zhang, Y. Zhang, and S. Ma, \u201cBeyond user embedding matrix: Learning to hash for modeling large-scale users in recommendation,\u201d SIGIR &apos;20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval, 2020.","DOI":"10.1145\/3397271.3401119"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] P.-S. Huang, X. He, J. Gao, L. Deng, A. Acero, and L. Heck, \u201cLearning deep structured semantic models for web search using clickthrough data,\u201d Proc. 22nd ACM International Conference on Information &amp; Knowledge Management, CIKM &apos;13, New York, NY, USA, pp.2333-2338, Association for Computing Machinery, 2013. 10.1145\/2505515.2505665","DOI":"10.1145\/2505515.2505665"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] J. Pennington, R. Socher, and C. Manning, \u201cGlove: Global vectors for word representation,\u201d Conference on Empirical Methods in Natural Language Processing, 2014. 10.3115\/v1\/d14-1162","DOI":"10.3115\/v1\/D14-1162"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] C. Wu, F. Wu, M. An, J. Huang, Y. Huang, and X. Xie, \u201cNPA: Neural News Recommendation with Personalized Attention,\u201d Proc. 25th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining, pp.2576-2584, Association for Computing Machinery, New York, NY, USA, 2019. 10.1145\/3292500.3330665","DOI":"10.1145\/3292500.3330665"},{"key":"18","unstructured":"[18] D. Kingma and J. Ba, \u201cAdam: A method for stochastic optimization,\u201d Computer Science, 2014."},{"key":"19","doi-asserted-by":"publisher","unstructured":"[19] S. Rendle, \u201cFactorization machines with libfm,\u201d ACM Transactions on Intelligent Systems and Technology, vol.3, no.3, pp.1-22, 2012. 10.1145\/2168752.2168771","DOI":"10.1145\/2168752.2168771"},{"key":"20","doi-asserted-by":"crossref","unstructured":"[20] P.-S. Huang, X. He, J. Gao, L. Deng, A. Acero, and L. Heck, \u201cLearning deep structured semantic models for web search using clickthrough data,\u201d Proc. 22nd ACM International Conference on Information &amp; Knowledge Management, CIKM &apos;13, New York, NY, USA, pp.2333-2338, Association for Computing Machinery, 2013. 10.1145\/2505515.2505665","DOI":"10.1145\/2505515.2505665"},{"key":"21","doi-asserted-by":"crossref","unstructured":"[21] H.T. Cheng, L. Koc, J. Harmsen, T. Shaked, T. Chandra, H.Aradhye, G. Anderson, G. Corrado, W. Chai, and M.a. Ispir, \u201cWide deep learning for recommender systems,\u201d Proc. 1st Workshop on Deep Learning for Recommender Systems, 2016.","DOI":"10.1145\/2988450.2988454"},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] H. Guo, R. Tang, Y. Ye, Z. Li, and X. He, \u201cDeepfm: A factorization-machine based neural network for ctr prediction,\u201d Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017. 10.24963\/ijcai.2017\/239","DOI":"10.24963\/ijcai.2017\/239"},{"key":"23","unstructured":"[23] J. Devlin, M.W. Chang, K. Lee, and K. Toutanova, \u201cBERT: Pre-training of deep bidirectional transformers for language understanding,\u201d Proc. 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol.1 (Long and Short Papers), Minneapolis, Minnesota, pp.4171-4186, June 2019. 10.18653\/v1\/n19-1423"},{"key":"24","unstructured":"[24] Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov, \u201cRoberta: A robustly optimized bert pretraining approach,\u201d arXiv preprint arXiv:1907.11692, 2019."}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E105.D\/2\/E105.D_2021EDP7034\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,9]],"date-time":"2024-05-09T04:55:40Z","timestamp":1715230540000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E105.D\/2\/E105.D_2021EDP7034\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,1]]},"references-count":24,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2021edp7034","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,1]]},"article-number":"2021EDP7034"}}