{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T16:21:49Z","timestamp":1778602909454,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":62,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,4,19]],"date-time":"2021-04-19T00:00:00Z","timestamp":1618790400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,4,19]]},"DOI":"10.1145\/3442381.3449791","type":"proceedings-article","created":{"date-parts":[[2021,6,3]],"date-time":"2021-06-03T19:02:07Z","timestamp":1622746927000},"page":"2992-3001","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":27,"title":["Future-Aware Diverse Trends Framework for Recommendation"],"prefix":"10.1145","author":[{"given":"Yujie","family":"Lu","sequence":"first","affiliation":[{"name":"Tencent, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Zhejiang University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingxuan","family":"Huang","sequence":"additional","affiliation":[{"name":"Tencent, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luyao","family":"Wang","sequence":"additional","affiliation":[{"name":"Zhejiang University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyao","family":"Yu","sequence":"additional","affiliation":[{"name":"Zhejiang University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhou","family":"Zhao","sequence":"additional","affiliation":[{"name":"Zhejiang University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Wu","sequence":"additional","affiliation":[{"name":"Zhejiang University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,6,3]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2011.15"},{"key":"e_1_3_2_1_2_1","volume-title":"Improved neighborhood-based collaborative filtering. (09","author":"Bell Robert","year":"2007","unstructured":"Robert Bell and Yehuda Koren . 2007. Improved neighborhood-based collaborative filtering. (09 2007 ). Robert Bell and Yehuda Koren. 2007. Improved neighborhood-based collaborative filtering. (09 2007)."},{"key":"e_1_3_2_1_3_1","unstructured":"Homanga Bharadhwaj and Shruti Joshi. 2018. Explanations for Temporal Recommendations. arxiv:1807.06161\u00a0[cs.AI]  Homanga Bharadhwaj and Shruti Joshi. 2018. Explanations for Temporal Recommendations. arxiv:1807.06161\u00a0[cs.AI]"},{"key":"e_1_3_2_1_4_1","unstructured":"Veronika Bogina and Tsvi Kuflik. 2017. Incorporating Dwell Time in Session-Based Recommendations with Recurrent Neural Networks. In RecTemp@RecSys.  Veronika Bogina and Tsvi Kuflik. 2017. Incorporating Dwell Time in Session-Based Recommendations with Recurrent Neural Networks. In RecTemp@RecSys."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/2063576.2063684"},{"key":"e_1_3_2_1_6_1","unstructured":"John\u00a0S. Breese David Heckerman and Carl Kadie. 2013. Empirical Analysis of Predictive Algorithms for Collaborative Filtering. arxiv:1301.7363\u00a0[cs.IR]  John\u00a0S. Breese David Heckerman and Carl Kadie. 2013. Empirical Analysis of Predictive Algorithms for Collaborative Filtering. arxiv:1301.7363\u00a0[cs.IR]"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/1864708.1864756"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Yukuo Cen Jianwei Zhang Xu Zou Chang Zhou Hongxia Yang and Jie Tang. 2020. Controllable Multi-Interest Framework for Recommendation. arxiv:2005.09347\u00a0[cs.IR]  Yukuo Cen Jianwei Zhang Xu Zou Chang Zhou Hongxia Yang and Jie Tang. 2020. Controllable Multi-Interest Framework for Recommendation. arxiv:2005.09347\u00a0[cs.IR]","DOI":"10.1145\/3394486.3403344"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCI.2016.2572539"},{"key":"e_1_3_2_1_10_1","volume-title":"Sequential Recommendation with User Memory Networks(WSDM \u201918)","author":"Chen Xu","unstructured":"Xu Chen , Hongteng Xu , Yongfeng Zhang , Jiaxi Tang , Yixin Cao , Zheng Qin , and Hongyuan Zha . 2018. Sequential Recommendation with User Memory Networks(WSDM \u201918) . Association for Computing Machinery , New York, NY, USA , 108\u2013116. https:\/\/doi.org\/10.1145\/3159652.3159668 Xu Chen, Hongteng Xu, Yongfeng Zhang, Jiaxi Tang, Yixin Cao, Zheng Qin, and Hongyuan Zha. 2018. Sequential Recommendation with User Memory Networks(WSDM \u201918). Association for Computing Machinery, New York, NY, USA, 108\u2013116. https:\/\/doi.org\/10.1145\/3159652.3159668"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959190"},{"key":"e_1_3_2_1_12_1","volume-title":"Long and Short-Term Recommendations with Recurrent Neural Networks(UMAP \u201917)","author":"Devooght Robin","unstructured":"Robin Devooght and Hugues Bersini . 2017. Long and Short-Term Recommendations with Recurrent Neural Networks(UMAP \u201917) . Association for Computing Machinery , New York, NY, USA , 13\u201321. https:\/\/doi.org\/10.1145\/3079628.3079670 Robin Devooght and Hugues Bersini. 2017. Long and Short-Term Recommendations with Recurrent Neural Networks(UMAP \u201917). Association for Computing Machinery, New York, NY, USA, 13\u201321. https:\/\/doi.org\/10.1145\/3079628.3079670"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/2645710.2645774"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3109859.3109877"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"crossref","unstructured":"Xinyu Duan Siliang Tang Sheng-yu Zhang Yin Zhang Zhou Zhao Jian-ru Xue Yueting Zhuang and Fei Wu. 2018. Temporality-enhanced knowledgememory network for factoid question answering.Frontiers Inf. Technol. Electron. Eng.(2018).  Xinyu Duan Siliang Tang Sheng-yu Zhang Yin Zhang Zhou Zhao Jian-ru Xue Yueting Zhuang and Fei Wu. 2018. Temporality-enhanced knowledgememory network for factoid question answering.Frontiers Inf. Technol. Electron. Eng.(2018).","DOI":"10.1631\/FITEE.1700788"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.12171"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741667"},{"key":"e_1_3_2_1_18_1","volume-title":"Graph Neural Networks for Social Recommendation. In The World Wide Web Conference","author":"Fan Wenqi","year":"2019","unstructured":"Wenqi Fan , Yao Ma , Qing Li , Yuan He , Eric Zhao , Jiliang Tang , and Dawei Yin . 2019 . Graph Neural Networks for Social Recommendation. In The World Wide Web Conference ( San Francisco, CA, USA) (WWW \u201919). Association for Computing Machinery, New York, NY, USA, 417\u2013426. https:\/\/doi.org\/10.1145\/3308558.3313488 Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin. 2019. Graph Neural Networks for Social Recommendation. In The World Wide Web Conference (San Francisco, CA, USA) (WWW \u201919). Association for Computing Machinery, New York, NY, USA, 417\u2013426. https:\/\/doi.org\/10.1145\/3308558.3313488"},{"key":"e_1_3_2_1_19_1","volume-title":"Exploiting Social Information in Pairwise Preference Recommender System. Journal of Information and Data Management 7 (08","author":"Felicio Cricia","year":"2016","unstructured":"Cricia Felicio , Kl\u00e9risson Paix\u00e3o , Guilherme Alves , Sandra Amo , and Philippe Preux . 2016. Exploiting Social Information in Pairwise Preference Recommender System. Journal of Information and Data Management 7 (08 2016 ), 99. Cricia Felicio, Kl\u00e9risson Paix\u00e3o, Guilherme Alves, Sandra Amo, and Philippe Preux. 2016. Exploiting Social Information in Pairwise Preference Recommender System. Journal of Information and Data Management 7 (08 2016), 99."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2016.28"},{"key":"e_1_3_2_1_21_1","volume-title":"Intention Modeling from Ordered and Unordered Facets for Sequential Recommendation(WWW \u201920)","author":"Guo Xueliang","unstructured":"Xueliang Guo , Chongyang Shi , and Chuanming Liu . 2020. Intention Modeling from Ordered and Unordered Facets for Sequential Recommendation(WWW \u201920) . Association for Computing Machinery , New York, NY, USA , 1127\u20131137. https:\/\/doi.org\/10.1145\/3366423.3380190 Xueliang Guo, Chongyang Shi, and Chuanming Liu. 2020. Intention Modeling from Ordered and Unordered Facets for Sequential Recommendation(WWW \u201920). Association for Computing Machinery, New York, NY, USA, 1127\u20131137. https:\/\/doi.org\/10.1145\/3366423.3380190"},{"key":"e_1_3_2_1_22_1","article-title":"The MovieLens Datasets","volume":"5","author":"Harper M.","year":"2015","unstructured":"F.\u00a0 M. Harper and J. Konstan . 2015 . The MovieLens Datasets : History and Context. ACM Trans. Interact. Intell. Syst. 5 (2015), 19:1\u201319:19. F.\u00a0M. Harper and J. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Trans. Interact. Intell. Syst. 5 (2015), 19:1\u201319:19.","journal-title":"History and Context. ACM Trans. Interact. Intell. Syst."},{"key":"e_1_3_2_1_23_1","unstructured":"Kaiming He Haoqi Fan Yuxin Wu Saining Xie and Ross\u00a0B. Girshick. 2019. Momentum Contrast for Unsupervised Visual Representation Learning. CoRR abs\/1911.05722(2019). arxiv:1911.05722http:\/\/arxiv.org\/abs\/1911.05722  Kaiming He Haoqi Fan Yuxin Wu Saining Xie and Ross\u00a0B. Girshick. 2019. Momentum Contrast for Unsupervised Visual Representation Learning. CoRR abs\/1911.05722(2019). arxiv:1911.05722http:\/\/arxiv.org\/abs\/1911.05722"},{"key":"e_1_3_2_1_24_1","volume-title":"Vista: A Visually, Socially, and Temporally-aware Model for Artistic Recommendation. CoRR abs\/1607.04373(2016). arxiv:1607.04373http:\/\/arxiv.org\/abs\/1607.04373","author":"He Ruining","year":"2016","unstructured":"Ruining He , Chen Fang , Zhaowen Wang , and Julian\u00a0 J. McAuley . 2016 . Vista: A Visually, Socially, and Temporally-aware Model for Artistic Recommendation. CoRR abs\/1607.04373(2016). arxiv:1607.04373http:\/\/arxiv.org\/abs\/1607.04373 Ruining He, Chen Fang, Zhaowen Wang, and Julian\u00a0J. McAuley. 2016. Vista: A Visually, Socially, and Temporally-aware Model for Artistic Recommendation. CoRR abs\/1607.04373(2016). arxiv:1607.04373http:\/\/arxiv.org\/abs\/1607.04373"},{"key":"e_1_3_2_1_25_1","unstructured":"Ruining He Wang-Cheng Kang and Julian\u00a0J. McAuley. 2017. Translation-based Recommendation. CoRR abs\/1707.02410(2017). arxiv:1707.02410http:\/\/arxiv.org\/abs\/1707.02410  Ruining He Wang-Cheng Kang and Julian\u00a0J. McAuley. 2017. Translation-based Recommendation. CoRR abs\/1707.02410(2017). arxiv:1707.02410http:\/\/arxiv.org\/abs\/1707.02410"},{"key":"e_1_3_2_1_26_1","volume-title":"An Empirical Analysis of Design Choices in Neighborhood-Based Collaborative Filtering Algorithms. Information Retrieval 5 (01","author":"Herlocker Jon","year":"2002","unstructured":"Jon Herlocker , Joseph Konstan , and John Riedl . 2002. An Empirical Analysis of Design Choices in Neighborhood-Based Collaborative Filtering Algorithms. Information Retrieval 5 (01 2002 ), 287\u2013310. https:\/\/doi.org\/10.1023\/A:1020443909834 Jon Herlocker, Joseph Konstan, and John Riedl. 2002. An Empirical Analysis of Design Choices in Neighborhood-Based Collaborative Filtering Algorithms. Information Retrieval 5 (01 2002), 287\u2013310. https:\/\/doi.org\/10.1023\/A:1020443909834"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3271761"},{"key":"e_1_3_2_1_28_1","unstructured":"Bal\u00e1zs Hidasi Alexandros Karatzoglou Linas Baltrunas and Domonkos Tikk. 2015. Session-based Recommendations with Recurrent Neural Networks. arxiv:1511.06939\u00a0[cs.LG]  Bal\u00e1zs Hidasi Alexandros Karatzoglou Linas Baltrunas and Domonkos Tikk. 2015. Session-based Recommendations with Recurrent Neural Networks. arxiv:1511.06939\u00a0[cs.LG]"},{"key":"e_1_3_2_1_29_1","volume-title":"Parallel Recurrent Neural Network Architectures for Feature-Rich Session-Based Recommendations(RecSys \u201916)","author":"Hidasi Bal\u00e1zs","unstructured":"Bal\u00e1zs Hidasi , Massimo Quadrana , Alexandros Karatzoglou , and Domonkos Tikk . 2016. Parallel Recurrent Neural Network Architectures for Feature-Rich Session-Based Recommendations(RecSys \u201916) . Association for Computing Machinery , New York, NY, USA , 241\u2013248. https:\/\/doi.org\/10.1145\/2959100.2959167 Bal\u00e1zs Hidasi, Massimo Quadrana, Alexandros Karatzoglou, and Domonkos Tikk. 2016. Parallel Recurrent Neural Network Architectures for Feature-Rich Session-Based Recommendations(RecSys \u201916). Association for Computing Machinery, New York, NY, USA, 241\u2013248. https:\/\/doi.org\/10.1145\/2959100.2959167"},{"key":"e_1_3_2_1_30_1","volume-title":"When Recurrent Neural Networks Meet the Neighborhood for Session-Based Recommendation(RecSys \u201917)","author":"Jannach Dietmar","year":"2017","unstructured":"Dietmar Jannach and Malte Ludewig . 2017 . When Recurrent Neural Networks Meet the Neighborhood for Session-Based Recommendation(RecSys \u201917) . Association for Computing Machinery, New York, NY, USA, 306\u2013310. https:\/\/doi.org\/10.1145\/3109859.3109872 Dietmar Jannach and Malte Ludewig. 2017. When Recurrent Neural Networks Meet the Neighborhood for Session-Based Recommendation(RecSys \u201917). Association for Computing Machinery, New York, NY, USA, 306\u2013310. https:\/\/doi.org\/10.1145\/3109859.3109872"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"crossref","unstructured":"Santosh Kabbur Xia Ning and George Karypis. 2013. FISM: factored item similarity models for top-N recommender systems. 659\u2013667. https:\/\/doi.org\/10.1145\/2487575.2487589  Santosh Kabbur Xia Ning and George Karypis. 2013. FISM: factored item similarity models for top-N recommender systems. 659\u2013667. https:\/\/doi.org\/10.1145\/2487575.2487589","DOI":"10.1145\/2487575.2487589"},{"key":"e_1_3_2_1_32_1","unstructured":"Chao Li Zhiyuan Liu Mengmeng Wu Yuchi Xu Pipei Huang Huan Zhao Guoliang Kang Qiwei Chen Wei Li and Dik\u00a0Lun Lee. 2019. Multi-Interest Network with Dynamic Routing for Recommendation at Tmall. arxiv:1904.08030\u00a0[cs.IR]  Chao Li Zhiyuan Liu Mengmeng Wu Yuchi Xu Pipei Huang Huan Zhao Guoliang Kang Qiwei Chen Wei Li and Dik\u00a0Lun Lee. 2019. Multi-Interest Network with Dynamic Routing for Recommendation at Tmall. arxiv:1904.08030\u00a0[cs.IR]"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"crossref","unstructured":"Hao Ma. 2013. An experimental study on implicit social recommendation. 73\u201382. https:\/\/doi.org\/10.1145\/2484028.2484059  Hao Ma. 2013. An experimental study on implicit social recommendation. 73\u201382. https:\/\/doi.org\/10.1145\/2484028.2484059","DOI":"10.1145\/2484028.2484059"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"crossref","unstructured":"Subhabrata Mukherjee and Stephan Guennemann. 2019. GhostLink: Latent Network Inference for Influence-aware Recommendation. arxiv:1905.05955\u00a0[cs.SI]  Subhabrata Mukherjee and Stephan Guennemann. 2019. GhostLink: Latent Network Inference for Influence-aware Recommendation. arxiv:1905.05955\u00a0[cs.SI]","DOI":"10.1145\/3308558.3313449"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMPSACW.2013.68"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3109859.3109896"},{"key":"e_1_3_2_1_37_1","volume-title":"Context-Aware Sequential Recommendations WithStacked Recurrent Neural Networks. In The World Wide Web Conference","author":"Rakkappan Lakshmanan","year":"2019","unstructured":"Lakshmanan Rakkappan and Vaibhav Rajan . 2019 . Context-Aware Sequential Recommendations WithStacked Recurrent Neural Networks. In The World Wide Web Conference ( San Francisco, CA, USA) (WWW \u201919). Association for Computing Machinery, New York, NY, USA, 3172\u20133178. https:\/\/doi.org\/10.1145\/3308558.3313567 Lakshmanan Rakkappan and Vaibhav Rajan. 2019. Context-Aware Sequential Recommendations WithStacked Recurrent Neural Networks. In The World Wide Web Conference (San Francisco, CA, USA) (WWW \u201919). Association for Computing Machinery, New York, NY, USA, 3172\u20133178. https:\/\/doi.org\/10.1145\/3308558.3313567"},{"key":"e_1_3_2_1_38_1","volume-title":"Factorizing Personalized Markov Chains for Next-Basket Recommendation(WWW \u201910)","author":"Rendle Steffen","unstructured":"Steffen Rendle , Christoph Freudenthaler , and Lars Schmidt-Thieme . 2010. Factorizing Personalized Markov Chains for Next-Basket Recommendation(WWW \u201910) . Association for Computing Machinery , New York, NY, USA , 811\u2013820. https:\/\/doi.org\/10.1145\/1772690.1772773 Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010. Factorizing Personalized Markov Chains for Next-Basket Recommendation(WWW \u201910). Association for Computing Machinery, New York, NY, USA, 811\u2013820. https:\/\/doi.org\/10.1145\/1772690.1772773"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/2009916.2010002"},{"key":"e_1_3_2_1_40_1","volume-title":"Ole Steinar\u00a0Lillest\u00f8l Skrede, and Helge Langseth","author":"Ruocco Massimiliano","year":"2017","unstructured":"Massimiliano Ruocco , Ole Steinar\u00a0Lillest\u00f8l Skrede, and Helge Langseth . 2017 . Inter-Session Modeling for Session-Based Recommendation. CoRR abs\/1706.07506(2017). arxiv:1706.07506http:\/\/arxiv.org\/abs\/1706.07506 Massimiliano Ruocco, Ole Steinar\u00a0Lillest\u00f8l Skrede, and Helge Langseth. 2017. Inter-Session Modeling for Session-Based Recommendation. CoRR abs\/1706.07506(2017). arxiv:1706.07506http:\/\/arxiv.org\/abs\/1706.07506"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/371920.372071"},{"key":"e_1_3_2_1_42_1","unstructured":"Yong\u00a0Kiam Tan Xinxing Xu and Yong Liu. 2016. Improved Recurrent Neural Networks for Session-based Recommendations. CoRR abs\/1606.08117(2016). arxiv:1606.08117http:\/\/arxiv.org\/abs\/1606.08117  Yong\u00a0Kiam Tan Xinxing Xu and Yong Liu. 2016. Improved Recurrent Neural Networks for Session-based Recommendations. CoRR abs\/1606.08117(2016). arxiv:1606.08117http:\/\/arxiv.org\/abs\/1606.08117"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219869"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/2766462.2767694"},{"key":"e_1_3_2_1_45_1","volume-title":"Recurrent Recommender Networks(WSDM \u201917)","author":"Wu Chao-Yuan","year":"1866","unstructured":"Chao-Yuan Wu , Amr Ahmed , Alex Beutel , Alexander\u00a0 J. Smola , and How Jing . 2017. Recurrent Recommender Networks(WSDM \u201917) . Association for Computing Machinery , New York, NY, USA , 495\u2013503. https:\/\/doi.org\/10.1145\/30 1866 1.3018689 Chao-Yuan Wu, Amr Ahmed, Alex Beutel, Alexander\u00a0J. Smola, and How Jing. 2017. Recurrent Recommender Networks(WSDM \u201917). Association for Computing Machinery, New York, NY, USA, 495\u2013503. https:\/\/doi.org\/10.1145\/3018661.3018689"},{"key":"e_1_3_2_1_46_1","volume-title":"D\u00e9J\u00e0 vu: A Contextualized Temporal Attention Mechanism for Sequential Recommendation(WWW \u201920)","author":"Wu Jibang","unstructured":"Jibang Wu , Renqin Cai , and Hongning Wang . 2020. D\u00e9J\u00e0 vu: A Contextualized Temporal Attention Mechanism for Sequential Recommendation(WWW \u201920) . Association for Computing Machinery , New York, NY, USA , 11\u00a0pages. https:\/\/doi.org\/10.1145\/3366423.3380285 Jibang Wu, Renqin Cai, and Hongning Wang. 2020. D\u00e9J\u00e0 vu: A Contextualized Temporal Attention Mechanism for Sequential Recommendation(WWW \u201920). Association for Computing Machinery, New York, NY, USA, 11\u00a0pages. https:\/\/doi.org\/10.1145\/3366423.3380285"},{"key":"e_1_3_2_1_47_1","volume-title":"Hierarchical Neural Variational Model for Personalized Sequential Recommendation. In The World Wide Web Conference","author":"Xiao Teng","year":"2019","unstructured":"Teng Xiao , Shangsong Liang , and Zaiqiao Meng . 2019 . Hierarchical Neural Variational Model for Personalized Sequential Recommendation. In The World Wide Web Conference ( San Francisco, CA, USA) (WWW \u201919). Association for Computing Machinery, New York, NY, USA, 3377\u20133383. https:\/\/doi.org\/10.1145\/3308558.3313603 Teng Xiao, Shangsong Liang, and Zaiqiao Meng. 2019. Hierarchical Neural Variational Model for Personalized Sequential Recommendation. In The World Wide Web Conference (San Francisco, CA, USA) (WWW \u201919). Association for Computing Machinery, New York, NY, USA, 3377\u20133383. https:\/\/doi.org\/10.1145\/3308558.3313603"},{"key":"e_1_3_2_1_48_1","volume-title":"Recurrent Convolutional Neural Network for Sequential Recommendation(WWW \u201919)","author":"Xu Chengfeng","unstructured":"Chengfeng Xu , Pengpeng Zhao , Yanchi Liu , Jiajie Xu , Victor\u00a0 S. Sheng S. Sheng , Zhiming Cui , Xiaofang Zhou , and Hui Xiong . 2019. Recurrent Convolutional Neural Network for Sequential Recommendation(WWW \u201919) . Association for Computing Machinery , New York, NY, USA , 3398\u20133404. https:\/\/doi.org\/10.1145\/3308558.3313408 Chengfeng Xu, Pengpeng Zhao, Yanchi Liu, Jiajie Xu, Victor\u00a0S.Sheng S.Sheng, Zhiming Cui, Xiaofang Zhou, and Hui Xiong. 2019. Recurrent Convolutional Neural Network for Sequential Recommendation(WWW \u201919). Association for Computing Machinery, New York, NY, USA, 3398\u20133404. https:\/\/doi.org\/10.1145\/3308558.3313408"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"crossref","unstructured":"Dong Yao Shengyu Zhang Zhou Zhao Wenyan Fan Jieming Zhu Xiuqiang He and Fei Wu. 2021. Modeling High-order Interactions across Multi-interests for Micro-video Recommendation.. In AAAI.  Dong Yao Shengyu Zhang Zhou Zhao Wenyan Fan Jieming Zhu Xiuqiang He and Fei Wu. 2021. Modeling High-order Interactions across Multi-interests for Micro-video Recommendation.. In AAAI.","DOI":"10.1609\/aaai.v35i18.17969"},{"key":"e_1_3_2_1_50_1","volume-title":"Article 10 (March","author":"Yin Hongzhi","year":"2015","unstructured":"Hongzhi Yin , Bin Cui , Ling Chen , Zhiting Hu , and Xiaofang Zhou . 2015. Dynamic User Modeling in Social Media Systems. 33, 3 , Article 10 (March 2015 ), 44\u00a0pages. https:\/\/doi.org\/10.1145\/2699670 Hongzhi Yin, Bin Cui, Ling Chen, Zhiting Hu, and Xiaofang Zhou. 2015. Dynamic User Modeling in Social Media Systems. 33, 3, Article 10 (March 2015), 44\u00a0pages. https:\/\/doi.org\/10.1145\/2699670"},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/2911451.2914683"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380116"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"crossref","unstructured":"Fajie Yuan Alexandros Karatzoglou Ioannis Arapakis Joemon\u00a0M Jose and Xiangnan He. 2018. A Simple Convolutional Generative Network for Next Item Recommendation. arxiv:1808.05163\u00a0[cs.IR]  Fajie Yuan Alexandros Karatzoglou Ioannis Arapakis Joemon\u00a0M Jose and Xiangnan He. 2018. A Simple Convolutional Generative Network for Next Item Recommendation. arxiv:1808.05163\u00a0[cs.IR]","DOI":"10.1145\/3289600.3290975"},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2936461"},{"key":"e_1_3_2_1_55_1","volume-title":"Hefei","author":"Zhang Shengyu","year":"2018","unstructured":"Shengyu Zhang , Hao Dong , Wei Hu , Yike Guo , Chao Wu , Di Xie , and Fei Wu . 2018 . Text-to-Image Synthesis via Visual-Memory Creative Adversarial Network.. In Advances in Multimedia Information Processing - PCM 2018 - 19th Pacific-Rim Conference on Multimedia , Hefei , China, September 21-22, 2018, Proceedings, Part III. Shengyu Zhang, Hao Dong, Wei Hu, Yike Guo, Chao Wu, Di Xie, and Fei Wu. 2018. Text-to-Image Synthesis via Visual-Memory Creative Adversarial Network.. In Advances in Multimedia Information Processing - PCM 2018 - 19th Pacific-Rim Conference on Multimedia, Hefei, China, September 21-22, 2018, Proceedings, Part III."},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394171.3413518"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394171.3413880"},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403325"},{"key":"e_1_3_2_1_59_1","unstructured":"Yuyu Zhang Hanjun Dai Chang Xu Jun Feng Taifeng Wang Jiang Bian Bin Wang and Tie-Yan Liu. 2014. Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks. CoRR abs\/1404.5772(2014). arxiv:1404.5772http:\/\/arxiv.org\/abs\/1404.5772  Yuyu Zhang Hanjun Dai Chang Xu Jun Feng Taifeng Wang Jiang Bian Bin Wang and Tie-Yan Liu. 2014. Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks. CoRR abs\/1404.5772(2014). arxiv:1404.5772http:\/\/arxiv.org\/abs\/1404.5772"},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741656"},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"crossref","unstructured":"Guorui Zhou Chengru Song Xiaoqiang Zhu Ying Fan Han Zhu Xiao Ma Yanghui Yan Junqi Jin Han Li and Kun Gai. 2017. Deep Interest Network for Click-Through Rate Prediction. arxiv:1706.06978\u00a0[stat.ML]  Guorui Zhou Chengru Song Xiaoqiang Zhu Ying Fan Han Zhu Xiao Ma Yanghui Yan Junqi Jin Han Li and Kun Gai. 2017. Deep Interest Network for Click-Through Rate Prediction. arxiv:1706.06978\u00a0[stat.ML]","DOI":"10.1145\/3219819.3219823"},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"crossref","unstructured":"Yu Zhu Hao Li Yikang Liao Beidou Wang Ziyu Guan Haifeng Liu and Deng Cai. 2017. What to Do Next: Modeling User Behaviors by Time-LSTM. 3602\u20133608. https:\/\/doi.org\/10.24963\/ijcai.2017\/504  Yu Zhu Hao Li Yikang Liao Beidou Wang Ziyu Guan Haifeng Liu and Deng Cai. 2017. What to Do Next: Modeling User Behaviors by Time-LSTM. 3602\u20133608. https:\/\/doi.org\/10.24963\/ijcai.2017\/504","DOI":"10.24963\/ijcai.2017\/504"}],"event":{"name":"WWW '21: The Web Conference 2021","location":"Ljubljana Slovenia","acronym":"WWW '21","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the Web Conference 2021"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3442381.3449791","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3442381.3449791","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:24:22Z","timestamp":1750195462000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3442381.3449791"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,19]]},"references-count":62,"alternative-id":["10.1145\/3442381.3449791","10.1145\/3442381"],"URL":"https:\/\/doi.org\/10.1145\/3442381.3449791","relation":{},"subject":[],"published":{"date-parts":[[2021,4,19]]},"assertion":[{"value":"2021-06-03","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}