{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T13:10:43Z","timestamp":1753881043288,"version":"3.41.2"},"reference-count":55,"publisher":"Association for Computing Machinery (ACM)","license":[{"start":{"date-parts":[[2022,11,22]],"date-time":"2022-11-22T00:00:00Z","timestamp":1669075200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"abstract":"<jats:p>\n            Sequential recommendation (SR) aims to predict a user\u2019s next interacted item given his\/her historical interactions. Most existing sequential recommendation systems model user preferences only with item-level representations, where a user\u2019s interaction sequence are often modeled with sequential or graph-based method to infer the user\u2019s sequential interaction pattern. However, since a user\u2019s preference factors may vary over time, the user modeling on item-level could hardly represent the user\u2019s preference precisely and sufficiently, resulting in suboptimal recommendation performance. In addition, the recommendation results based on the item-level user representations lack the interpretability of preference factors. To address these problems, we propose a novel SR model with dual-view user representations in this paper, namely DUVRec, where a user\u2019s preference is learned based on the representations of two distinct views, i.e.,\n            <jats:italic>item view<\/jats:italic>\n            and\n            <jats:italic>factor view<\/jats:italic>\n            . Specifically, the item-view user representation is learned as the previous SR models to encode the user preference of item level, while the factor-view user representation is learned by an coarse-grained graph embedding method to explicitly represent the user in terms of preference factors. As a result, such dual-view user representations are more comprehensive than that in the previous SR models, leading to enhanced SR performance. Furthermore, we design a contrastive learning strategy to achieve mutual complementation between these two views. Our extensive experiments upon three benchmark datasets justify DUVRec\u2019s superior performance over the state-of-the-art SR models, including the advantage of the dual-view contrastive learning. In addition, DUVRec\u2019s capability of providing explanations on recommendation results is also demonstrated through some specific case studies.\n          <\/jats:p>","DOI":"10.1145\/3572028","type":"journal-article","created":{"date-parts":[[2022,11,22]],"date-time":"2022-11-22T11:56:03Z","timestamp":1669118163000},"update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Learning Dual-view User Representations for Enhanced Sequential Recommendation"],"prefix":"10.1145","author":[{"given":"Lyuxin","family":"Xue","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deqing","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuoyao","family":"Zhai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuxin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanghua","family":"Xiao","sequence":"additional","affiliation":[{"name":"Fudan University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,11,22]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Joan Bruna Wojciech Zaremba Arthur Szlam and Yann LeCun. 2013. Spectral networks and locally connected networks on graphs. In arXiv preprint arXiv:1312.6203.  Joan Bruna Wojciech Zaremba Arthur Szlam and Yann LeCun. 2013. Spectral networks and locally connected networks on graphs. In arXiv preprint arXiv:1312.6203."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462968"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159668"},{"key":"e_1_2_1_4_1","volume-title":"Intent Contrastive Learning for Sequential Recommendation. In The ACM Web Conference \u201922: The ACM Web Conference 2022","author":"Chen Yongjun","year":"2022","unstructured":"Yongjun Chen , Zhiwei Liu , Jia Li , Julian\u00a0 J. McAuley , and Caiming Xiong . 2022 . Intent Contrastive Learning for Sequential Recommendation. In The ACM Web Conference \u201922: The ACM Web Conference 2022 , Virtual Event, Lyon, France, April 25 - 29 , 2022. ACM, 2172\u20132182. Yongjun Chen, Zhiwei Liu, Jia Li, Julian\u00a0J. 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CoRR abs\/2011.02260(2020)."},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482388"},{"key":"e_1_2_1_47_1","volume-title":"Contrastive Learning for Sequential Recommendation. In 38th IEEE International Conference on Data Engineering, ICDE 2022","author":"Xie Xu","year":"2022","unstructured":"Xu Xie , Fei Sun , Zhaoyang Liu , Shiwen Wu , Jinyang Gao , Jiandong Zhang , Bolin Ding , and Bin Cui . 2022 . Contrastive Learning for Sequential Recommendation. In 38th IEEE International Conference on Data Engineering, ICDE 2022 , Kuala Lumpur, Malaysia , May 9-12, 2022. IEEE, 1259\u20131273. Xu Xie, Fei Sun, Zhaoyang Liu, Shiwen Wu, Jinyang Gao, Jiandong Zhang, Bolin Ding, and Bin Cui. 2022. Contrastive Learning for Sequential Recommendation. In 38th IEEE International Conference on Data Engineering, ICDE 2022, Kuala Lumpur, Malaysia, May 9-12, 2022. 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Wallach, Hugo Larochelle, Kristen Grauman, Nicol\u00f2 Cesa-Bianchi, and Roman Garnett (Eds.). 4805\u20134815. Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William\u00a0L. Hamilton, and Jure Leskovec. 2018. Hierarchical Graph Representation Learning with Differentiable Pooling. In Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montr\u00e9al, Canada, Samy Bengio, Hanna\u00a0M. Wallach, Hugo Larochelle, Kristen Grauman, Nicol\u00f2 Cesa-Bianchi, and Roman Garnett (Eds.). 4805\u20134815."},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219823"},{"key":"e_1_2_1_53_1","unstructured":"Jie Zhou Ganqu Cui Zhengyan Zhang Cheng Yang Zhiyuan Liu Lifeng Wang Changcheng Li and Maosong Sun. 2018. Graph neural networks: A review of methods and applications. In arXiv preprint arXiv:1812.08434.  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