{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:30:10Z","timestamp":1750221010159,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":25,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,7,25]],"date-time":"2019-07-25T00:00:00Z","timestamp":1564012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Beijing National Research Center for Information Science and Technology","award":["20031887521"],"award-info":[{"award-number":["20031887521"]}]},{"name":"the National Nature Science Foundation of China","award":["61861136003, 61621091, 61673237"],"award-info":[{"award-number":["61861136003, 61621091, 61673237"]}]},{"name":"research fund of Tsinghua University - Tencent Joint Laboratory for Internet Innovation Technology"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,7,25]]},"DOI":"10.1145\/3292500.3330828","type":"proceedings-article","created":{"date-parts":[[2019,7,26]],"date-time":"2019-07-26T13:17:26Z","timestamp":1564147046000},"page":"1549-1559","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["State-Sharing Sparse Hidden Markov Models for Personalized Sequences"],"prefix":"10.1145","author":[{"given":"Hongzhi","family":"Shi","sequence":"first","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"given":"Chao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology, Atlanta, GA, USA"}]},{"given":"Quanming","family":"Yao","sequence":"additional","affiliation":[{"name":"4Paradigm Inc., Beijing, China"}]},{"given":"Yong","family":"Li","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"given":"Funing","family":"Sun","sequence":"additional","affiliation":[{"name":"Tencent Inc., Beijing, China"}]},{"given":"Depeng","family":"Jin","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2019,7,25]]},"reference":[{"volume-title":"A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. The annals of mathematical statistics","year":"1970","author":"Baum Leonard E","key":"e_1_3_2_1_1_1"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","unstructured":"\u00d2scar Celma Herrada. 2009. Music recommendation and discovery in the long tail. (2009).   \u00d2scar Celma Herrada. 2009. Music recommendation and discovery in the long tail. (2009).","DOI":"10.1007\/978-3-642-13287-2"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2015.07.001"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"crossref","unstructured":"Zhiyong Cheng Jialie Shen Lei Zhu Mohan S Kankanhalli and Liqiang Nie. 2017. Exploiting Music Play Sequence for Music Recommendation. In IJCAI.   Zhiyong Cheng Jialie Shen Lei Zhu Mohan S Kankanhalli and Liqiang Nie. 2017. Exploiting Music Play Sequence for Music Recommendation. In IJCAI.","DOI":"10.24963\/ijcai.2017\/511"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1864349.1864380"},{"key":"e_1_3_2_1_6_1","unstructured":"Budhaditya Deb and Prithwish Basu. 2015. Discovering latent semantic structure in human mobility traces. In EWSN.  Budhaditya Deb and Prithwish Basu. 2015. Discovering latent semantic structure in human mobility traces. In EWSN."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186058"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2788627"},{"volume-title":"Music sequence prediction with mixture hidden markov models. arXiv preprint arXiv:1809.00842","year":"2018","author":"Li Tao","key":"e_1_3_2_1_9_1"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","unstructured":"Dongliang Liao Weiqing Liu Yuan Zhong Jing Li and Guowei Wang. 2018. Predicting Activity and Location with Multi-task Context Aware Recurrent Neural Network. In IJCAI.   Dongliang Liao Weiqing Liu Yuan Zhong Jing Li and Guowei Wang. 2018. Predicting Activity and Location with Multi-task Context Aware Recurrent Neural Network. In IJCAI.","DOI":"10.24963\/ijcai.2018\/477"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/2370216.2370421"},{"key":"e_1_3_2_1_12_1","unstructured":"Thomas Minka. 1998. Expectation-Maximization as lower bound maximization.  Thomas Minka. 1998. Expectation-Maximization as lower bound maximization."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557091"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1117\/1.2819119"},{"key":"e_1_3_2_1_15_1","unstructured":"Hongzhi Shi Hancheng Cao Xiangxin Zhou Yong Li Chao Zhang Vassilis Kostakos Funing Sun and Fanchao Meng. 2019. Semantics-Aware Hidden Markov Model for Human Mobility. In SDM.  Hongzhi Shi Hancheng Cao Xiangxin Zhou Yong Li Chao Zhang Vassilis Kostakos Funing Sun and Fanchao Meng. 2019. Semantics-Aware Hidden Markov Model for Human Mobility. In SDM."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2018.2799945"},{"key":"e_1_3_2_1_17_1","unstructured":"Pham Dinh Tao etal 2005. The DC (difference of convex functions) programming and DCA revisited with DC models of real world nonconvex optimization problems. Annals of operations research (2005).  Pham Dinh Tao et al. 2005. The DC (difference of convex functions) programming and DCA revisited with DC models of real world nonconvex optimization problems. Annals of operations research (2005)."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219900"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313453"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3133056"},{"volume-title":"Efficient learning with a family of nonconvex regularizers by redistributing nonconvexity. Journal of Machine Learning Research","year":"2017","author":"Yao Quanming","key":"e_1_3_2_1_21_1"},{"volume-title":"Large-Scale Low-Rank Matrix Learning with Nonconvex Regularizers","year":"2018","author":"Yao Quanming","key":"e_1_3_2_1_22_1"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/2911451.2914683"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1162\/08997660360581958"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939793"}],"event":{"name":"KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"],"location":"Anchorage AK USA","acronym":"KDD '19"},"container-title":["Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3292500.3330828","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3292500.3330828","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:26:02Z","timestamp":1750206362000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3292500.3330828"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,25]]},"references-count":25,"alternative-id":["10.1145\/3292500.3330828","10.1145\/3292500"],"URL":"https:\/\/doi.org\/10.1145\/3292500.3330828","relation":{},"subject":[],"published":{"date-parts":[[2019,7,25]]},"assertion":[{"value":"2019-07-25","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}