{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T08:39:26Z","timestamp":1774255166562,"version":"3.50.1"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030893620","type":"print"},{"value":"9783030893637","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-89363-7_37","type":"book-chapter","created":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T01:02:59Z","timestamp":1635728579000},"page":"487-500","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["LoCo-VAE: Modeling Short-Term Preference as Joint Effect of Long-Term Preference and Context-Aware Impact in Recommendation"],"prefix":"10.1007","author":[{"given":"Jianping","family":"Liu","sequence":"first","affiliation":[]},{"given":"Bo","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Ruifang","family":"He","sequence":"additional","affiliation":[]},{"given":"Bin","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Shuo","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yuexian","family":"Hou","sequence":"additional","affiliation":[]},{"given":"Qinxue","family":"Jiang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,11,1]]},"reference":[{"issue":"6","key":"37_CR1","doi-asserted-by":"publisher","first-page":"734","DOI":"10.1109\/TKDE.2005.99","volume":"17","author":"G Adomavicius","year":"2005","unstructured":"Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734\u2013749 (2005)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"37_CR2","doi-asserted-by":"crossref","unstructured":"Chen, X., Zhang, Y., Qin, Z.: Dynamic explainable recommendation based on neural attentive models. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 53\u201360 (2019)","DOI":"10.1609\/aaai.v33i01.330153"},{"key":"37_CR3","unstructured":"Gan, M., Ma, Y., Xiao, K.: CDMF: a deep learning model based on convolutional and dense-layer matrix factorization for context-aware recommendation"},{"key":"37_CR4","doi-asserted-by":"crossref","unstructured":"Hansen, C., et al.: Contextual and sequential user embeddings for large-scale music recommendation. In: Fourteenth ACM Conference on Recommender Systems, RecSys 2020, pp. 53\u201362. Association for Computing Machinery, New York (2020)","DOI":"10.1145\/3383313.3412248"},{"key":"37_CR5","doi-asserted-by":"crossref","unstructured":"He, X., Liao, L., Zhang, H., Nie, L., Chua, T.S.: Neural collaborative filtering (2017)","DOI":"10.1145\/3038912.3052569"},{"key":"37_CR6","doi-asserted-by":"crossref","unstructured":"Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), Pisa, 15\u201319 December 2008","DOI":"10.1109\/ICDM.2008.22"},{"key":"37_CR7","doi-asserted-by":"crossref","unstructured":"Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: Eighth IEEE International Conference on Data Mining (2009)","DOI":"10.1109\/ICDM.2008.22"},{"issue":"2","key":"37_CR8","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1023\/A:1007665907178","volume":"37","author":"MI Jordan","year":"1999","unstructured":"Jordan, M.I., Ghahramani, Z., Jaakkola, T.S., Saul, L.K.: An introduction to variational methods for graphical models. Mach. Learn. 37(2), 183\u2013233 (1999)","journal-title":"Mach. Learn."},{"key":"37_CR9","doi-asserted-by":"crossref","unstructured":"Karamanolakis, G., Cherian, K.R., Narayan, A.R., Yuan, J., Tang, D., Jebara, T.: Item recommendation with variational autoencoders and heterogeneous priors. In: Proceedings of the 3rd Workshop on Deep Learning for Recommender Systems, DLRS 2018, pp. 10\u201314. Association for Computing Machinery, New York (2018)","DOI":"10.1145\/3270323.3270329"},{"key":"37_CR10","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes (2014)"},{"key":"37_CR11","doi-asserted-by":"publisher","unstructured":"Li, R., Shen, Y., Zhu, Y.: Next point-of-interest recommendation with temporal and multi-level context attention. In: 2018 IEEE International Conference on Data Mining (ICDM), pp. 1110\u20131115 (2018). https:\/\/doi.org\/10.1109\/ICDM.2018.00144","DOI":"10.1109\/ICDM.2018.00144"},{"key":"37_CR12","doi-asserted-by":"crossref","unstructured":"Lian, D., Wu, Y., Ge, Y., Xie, X., Chen, E.: Geography-aware sequential location recommendation. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining KDD 2020, pp. 2009\u20132019. Association for Computing Machinery, New York (2020)","DOI":"10.1145\/3394486.3403252"},{"key":"37_CR13","doi-asserted-by":"crossref","unstructured":"Liang, D., Krishnan, R.G., Hoffman, M.D., Jebara, T.: Variational autoencoders for collaborative filtering. In: Proceedings of the 2018 World Wide Web Conference WWW 2018, pp. 689\u2013698. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva (2018)","DOI":"10.1145\/3178876.3186150"},{"key":"37_CR14","doi-asserted-by":"publisher","unstructured":"Liu, Q., Wu, S., Wang, D., Li, Z., Wang, L.: Context-aware sequential recommendation. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), pp. 1053\u20131058 (2016). https:\/\/doi.org\/10.1109\/ICDM.2016.0135","DOI":"10.1109\/ICDM.2016.0135"},{"key":"37_CR15","doi-asserted-by":"crossref","unstructured":"Ma, C., Ma, L., Zhang, Y., Sun, J., Coates, M.: Memory augmented graph neural networks for sequential recommendation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 4, pp. 5045\u20135052 (2020)","DOI":"10.1609\/aaai.v34i04.5945"},{"key":"37_CR16","doi-asserted-by":"crossref","unstructured":"Ma, C., Zhang, Y., Wang, Q., Liu, X.: Point-of-interest recommendation: Exploiting self-attentive autoencoders with neighbor-aware influence. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management CIKM 2018, p. 697\u2013706. Association for Computing Machinery, New York (2018)","DOI":"10.1145\/3269206.3271733"},{"key":"37_CR17","unstructured":"Ma, J., Zhou, C., Cui, P., Yang, H., Zhu, W.: Learning disentangled representations for recommendation. In: NeurIPS (2019)"},{"key":"37_CR18","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: ICLR (2013)"},{"key":"37_CR19","unstructured":"Nguyen, H., Haddawy, P.: Diva: applying decision theory to collaborative filtering (1999)"},{"key":"37_CR20","doi-asserted-by":"crossref","unstructured":"Pan, R., et al.: One-class collaborative filtering. In: Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, ICDM 2008, pp. 502\u2013511. IEEE Computer Society (2008)","DOI":"10.1109\/ICDM.2008.16"},{"key":"37_CR21","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1038\/s41562-018-0508-z","volume":"3","author":"M Park","year":"2019","unstructured":"Park, M., Thom, J., Mennicken, S., et al.: Global music streaming data reveal diurnal and seasonal patterns of affective preference. Nat. Hum. Behav. 3, 230\u2013236 (2019). https:\/\/doi.org\/10.1038\/s41562-018-0508-z","journal-title":"Nat. Hum. Behav."},{"key":"37_CR22","unstructured":"Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: Bpr: Bayesian personalized ranking from implicit feedback. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI 2009, pp. 452\u2013461. AUAI Press, Arlington (2009)"},{"key":"37_CR23","doi-asserted-by":"crossref","unstructured":"Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Factorizing personalized markov chains for next-basket recommendation. In: Proceedings of the 19th International Conference on World Wide Web, WWW 2010, pp. 811\u2013820. Association for Computing Machinery, New York (2010)","DOI":"10.1145\/1772690.1772773"},{"key":"37_CR24","unstructured":"Rezende, D.J., Mohamed, S., Wierstra, D.: Stochastic backpropagation and approximate inference in deep generative models (2014)"},{"key":"37_CR25","doi-asserted-by":"crossref","unstructured":"Wu, Y., DuBois, C., Zheng, A.X., Ester, M.: Collaborative denoising auto-encoders for top-n recommender systems. In: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, WSDM 2016, pp. 153\u2013162. Association for Computing Machinery, New York (2016)","DOI":"10.1145\/2835776.2835837"},{"key":"37_CR26","doi-asserted-by":"crossref","unstructured":"Xin, X., Chen, B., He, X., Wang, D., Ding, Y., Jose, J.: Cfm: Convolutional factorization machines for context-aware recommendation. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19, pp. 3926\u20133932. International Joint Conferences on Artificial Intelligence Organization (2019)","DOI":"10.24963\/ijcai.2019\/545"},{"key":"37_CR27","doi-asserted-by":"crossref","unstructured":"Yu, Z., Lian, J., Mahmoody, A., Liu, G., Xie, X.: Adaptive user modeling with long and short-term preferences for personalized recommendation. In: Twenty-Eighth International Joint Conference on Artificial Intelligence IJCAI-19 (2019)","DOI":"10.24963\/ijcai.2019\/585"},{"key":"37_CR28","first-page":"1","volume":"99","author":"P Zhao","year":"2020","unstructured":"Zhao, P., Luo, A., Liu, Y., Zhuang, F., Zhou, X.: Where to go next: a spatio-temporal gated network for next poi recommendation. IEEE Trans. Knowl. Data Eng. 99, 1 (2020)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"37_CR29","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Li, H., Liao, Y., Wang, B., Cai, D.: What to do next: modeling user behaviors by time-lstm. In: Twenty-Sixth International Joint Conference on Artificial Intelligence (2017)","DOI":"10.24963\/ijcai.2017\/504"}],"container-title":["Lecture Notes in Computer Science","PRICAI 2021: Trends in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-89363-7_37","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T01:21:34Z","timestamp":1635729694000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-89363-7_37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030893620","9783030893637"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-89363-7_37","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"1 November 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific Rim International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hanoi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 November 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pricai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pricai.org\/2021","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-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":"382","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":"93","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":"28","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":"24% - 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":"5","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)"}}]}}