{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T14:03:38Z","timestamp":1743084218958,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031109850"},{"type":"electronic","value":"9783031109867"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-10986-7_5","type":"book-chapter","created":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T22:30:36Z","timestamp":1658183436000},"page":"57-69","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep User Multi-interest Network for\u00a0Click-Through Rate Prediction"],"prefix":"10.1007","author":[{"given":"Ming","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junqian","family":"Xing","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shanxiong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,7,19]]},"reference":[{"issue":"2","key":"5_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2579993","volume":"8","author":"A Bellogin","year":"2014","unstructured":"Bellogin, A., Castells, P., Cantador, I.: Neighbor selection and weighting in user-based collaborative filtering: a performance prediction approach. ACM Trans. Web 8(2), 1\u201330 (2014)","journal-title":"ACM Trans. Web"},{"key":"5_CR2","doi-asserted-by":"crossref","unstructured":"Cheng, H.T., et al.: Wide & deep learning for recommender systems. In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, pp. 7\u201310 (2016)","DOI":"10.1145\/2988450.2988454"},{"key":"5_CR3","unstructured":"Chung, J., Gulcehre, C., Cho, K.H., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)"},{"key":"5_CR4","doi-asserted-by":"crossref","unstructured":"Covington, P., Adams, J., Sargin, E.: Deep neural networks for Youtube recommendations. In: Proceedings of the 10th Conference on Recommender Systems, pp. 191\u2013198 (2016)","DOI":"10.1145\/2959100.2959190"},{"key":"5_CR5","doi-asserted-by":"crossref","unstructured":"Guo, H., Tang, R., Ye, Y., Li, Z., He, X.: DeepFM: a factorization-machine based neural network for CTR prediction. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp. 1725\u20131731 (2017)","DOI":"10.24963\/ijcai.2017\/239"},{"key":"5_CR6","doi-asserted-by":"crossref","unstructured":"He, X., Chua, T.S.: Neural factorization machines for sparse predictive analytics. In: Proceedings of the 40th International Conference on Research on Development in Information Retrieval, pp. 355\u2013364 (2017)","DOI":"10.1145\/3077136.3080777"},{"key":"5_CR7","unstructured":"Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. In: Proceedings of the 4th International Conference on Learning Representations (2016)"},{"key":"5_CR8","doi-asserted-by":"crossref","unstructured":"Huang, Z., Tao, M., Zhang, B.: Deep user match network for click-through rate prediction. In: Proceedings of the 44th International Conference on Research and Development in Information Retrieval, pp. 1890\u20131894 (2021)","DOI":"10.1145\/3404835.3463078"},{"key":"5_CR9","doi-asserted-by":"crossref","unstructured":"Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th International Conference on Knowledge Discovery and Data Mining, pp. 426\u2013434 (2008)","DOI":"10.1145\/1401890.1401944"},{"key":"5_CR10","doi-asserted-by":"crossref","unstructured":"Li, X., Wang, C., Tong, B., Tan, J., Zeng, X., Zhuang, T.: Deep time-aware item evolution network for click-through rate prediction. In: Proceedings of the 29th International Conference on Information and Knowledge Management, pp. 785\u2013794 (2020)","DOI":"10.1145\/3340531.3411952"},{"key":"5_CR11","doi-asserted-by":"crossref","unstructured":"Lyu, Z., Dong, Y., Huo, C., Ren, W.: Deep match to rank model for personalized click-through rate prediction. In: Proceedings of the 34th Conference on Artificial Intelligence, pp. 156\u2013163 (2020)","DOI":"10.1609\/aaai.v34i01.5346"},{"key":"5_CR12","doi-asserted-by":"crossref","unstructured":"McMahan, H.B., et al.: Ad click prediction: a view from the trenches. In: Proceedings of the 19th International Conference on Knowledge Discovery and Data Mining, pp. 1222\u20131230 (2013)","DOI":"10.1145\/2487575.2488200"},{"key":"5_CR13","doi-asserted-by":"crossref","unstructured":"Qu, Y., et al.: Product based neural networks for user response prediction. In: Proceedings of the 16th International Conference on Data Mining, pp. 1149\u20131154 (2016)","DOI":"10.1109\/ICDM.2016.0151"},{"key":"5_CR14","doi-asserted-by":"crossref","unstructured":"Rendle, S.: Factorization machines. In: Proceedings of the 10th International Conference on Data Mining, pp. 995\u20131000 (2010)","DOI":"10.1109\/ICDM.2010.127"},{"key":"5_CR15","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998\u20136008 (2017)"},{"key":"5_CR16","doi-asserted-by":"crossref","unstructured":"Xiao, Z., Yang, L., Jiang, W., Wei, Y., Hu, Y., Wang, H.: Deep multi-interest network for click-through rate prediction. In: Proceedings of the 29th International Conference on Information and Knowledge Management, pp. 2265\u20132268 (2020)","DOI":"10.1145\/3340531.3412092"},{"key":"5_CR17","doi-asserted-by":"crossref","unstructured":"Xu, Z., et al.: Agile and accurate CTR prediction model training for massive-scale online advertising systems. In: Proceedings of the 2021 International Conference on Management of Data, pp. 2404\u20132409 (2021)","DOI":"10.1145\/3448016.3457236"},{"key":"5_CR18","doi-asserted-by":"crossref","unstructured":"Zhou, G., et al.: Deep interest evolution network for click-through rate prediction. In: Proceedings of the 33th Conference on Artificial Intelligence, pp. 5941\u20135948 (2019)","DOI":"10.1609\/aaai.v33i01.33015941"},{"key":"5_CR19","doi-asserted-by":"crossref","unstructured":"Zhou, G., et al.: Deep interest network for click-through rate prediction. In: Proceedings of the 24th International Conference on Knowledge Discovery and Data Mining, pp. 1059\u20131068 (2018)","DOI":"10.1145\/3219819.3219823"},{"issue":"7","key":"5_CR20","doi-asserted-by":"publisher","first-page":"4560","DOI":"10.1109\/TITS.2020.3032882","volume":"22","author":"H Qiu","year":"2020","unstructured":"Qiu, H., Zheng, Q., Msahli, M., Memmi, G., Qiu, M., Lu, J.: Topological graph convolutional network-based urban traffic flow and density prediction. IEEE Trans. Intell. Transp. Syst. 22(7), 4560\u20134569 (2020)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"5_CR21","doi-asserted-by":"crossref","unstructured":"Cao, W., Yang, P., Ming, Z., Cai, S., Zhang, J.: An improved fuzziness based random vector functional link network for liver disease detection. In: Proceedings of the 6th International Conference on Big Data Security on Cloud, pp. 42\u201348 (2020)","DOI":"10.1109\/BigDataSecurity-HPSC-IDS49724.2020.00019"}],"container-title":["Lecture Notes in Computer Science","Knowledge Science, Engineering and Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-10986-7_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T22:31:52Z","timestamp":1658183512000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-10986-7_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031109850","9783031109867"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-10986-7_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"19 July 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"KSEM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Knowledge Science, Engineering and Management","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 August 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 August 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ksem2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ksem22.smart-conf.net\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"498","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"169","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"34% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"10","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}