{"type":"ScholarlyArticle","title":"Recurrent Collaborative Filtering for Unifying General and Sequential Recommender","authors":[{"name":"Disheng Dong","slug":"disheng-dong","given":"Disheng","family":"Dong","affiliation":"Zhejiang University"},{"name":"Xiaolin Zheng","slug":"xiaolin-zheng","given":"Xiaolin","family":"Zheng","affiliation":"Zhejiang University"},{"name":"Ruixun Zhang","slug":"ruixun-zhang","given":"Ruixun","family":"Zhang","affiliation":"MIT Laboratory for Financial Engineering"},{"name":"Yan Wang","slug":"yan-wang","given":"Yan","family":"Wang","affiliation":"Macquarie University"}],"abstract":"General recommender and sequential recommender are two commonly applied modeling paradigms for recommendation tasks. General recommender focuses on modeling the general user preferences, ignoring the sequential patterns in user behaviors; whereas sequential recommender focuses on exploring the item-to-item sequential relations, failing to model the global user preferences. In addition, better recommendation performance has recently been achieved by adopting an approach to combine them. However, previous approaches are unable to solve both tasks in a unified way and cannot capture the whole historical sequential information. In this paper, we propose a recommendation model named Recurrent Collaborative Filtering (RCF), which unifies both paradigms within a single model.Specifically, we combine recurrent neural network (the sequential recommender part) and matrix factorization model (the general recommender part) in a multi-task learning framework, where we perform joint optimization with shared model parameters enforcing the two parts to regularize each other. Furthermore, we empirically demonstrate on MovieLens and Netflix datasets that our model outperforms the state-of-the-art methods across the tasks of both sequential and general recommender.","doi":"10.24963/ijcai.2018/465","url":"https://ijcai.org/proceedings/2018/465","pdf":"https://ijcai.org/proceedings/2018/0465.pdf","markdown":"https://ijcai.org/proceedings/2018/465.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/465","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Machine Learning Applications","track":"Main track","number":465,"pages":{"first":3350,"last":3356},"keywords":["Multidisciplinary Topics and Applications: Recommender Systems","Machine Learning: Transfer, Adaptation, Multi-task Learning","Machine Learning: Deep Learning"],"videos":[],"references":[{"position":1,"raw":"[Bahdanau et al., 2014] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural machine transla- tion by jointly learning to align and translate. CoRR, abs/1409.0473, 2014.","title":"Neural machine translation by jointly learning to align and translate","authors":"Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Cho et al., 2014] Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078, 2014.","title":"Learning phrase representations using rnn encoder-decoder for statistical machine translation","authors":"Cho","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Covington et al., 2016] Paul Covington, Jay Adams, and Emre Sargin. Deep neural networks for youtube recom- mendations. In Proceedings of the 10th ACM Conference on Recommender Systems, pages 191-198, 2016.","title":"Deep neural networks for youtube recommendations","authors":"Paul Covington, Jay Adams, Emre Sargin","year":"2016","venue":"Proceedings of the 10th ACM Conference on Recommender Systems","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Devooght and Bersini, 2017] Robin Devooght and Hugues Bersini. Long and short-term recommendations with re- current neural networks. In Proceedings of the 25th Con- ference on User Modeling, Adaptation and Personaliza- tion, pages 13-21, 2017.","title":"Long and short-term recommendations with recurrent neural networks","authors":"Bersini Devooght, Robin Devooght, Hugues Bersini","year":"2017","venue":"Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Duchi et al., 2011] John Duchi, Elad Hazan, and Yoram Singer. Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research, pages 2121-2159, 2011.","title":"Adaptive subgradient methods for online learning and stochastic optimization","authors":"John Duchi, Elad Hazan, Yoram Singer","year":"2011","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Grbovic et al., 2015] Mihajlo Grbovic, Vladan Radosavlje- vic, Nemanja Djuric, Narayan Bhamidipati, Jaikit Savla, Varun Bhagwan, and Doug Sharp. E-commerce in your inbox: Product recommendations at scale. In Proceed- ings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 1809- 1818, 2015.","title":"E-commerce in your inbox: Product recommendations at scale","authors":"Mihajlo Grbovic, Vladan Radosavljevic, Nemanja Djuric, Narayan Bhamidipati, Jaikit Savla, Varun Bhagwan, Doug Sharp","year":"2015","venue":"Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Hidasi et al., 2016] Balázs Hidasi, Alexandros Karat- zoglou, Linas Baltrunas, and Domonkos Tikk. Session- based recommendations with recurrent neural networks. In International Conference on Learning Representations, 2016.","title":"Sessionbased recommendations with recurrent neural networks","authors":"Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, Domonkos Tikk","year":"2016","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Hochreiter and Schmidhuber, 1997] Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural Computation, 9:1735-1780, 1997.","title":"Long short-term memory","authors":"Schmidhuber Hochreiter, Sepp Hochreiter, Jürgen Schmidhuber","year":"1997","venue":"Neural Computation","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Karatzoglou et al., 2013] Alexandros Karatzoglou, Linas Baltrunas, and Yue Shi. Learning to rank for recommender systems. In Proceedings of the 7th ACM Conference on Recommender Systems, pages 493-494, 2013.","title":"Learning to rank for recommender systems","authors":"Alexandros Karatzoglou, Linas Baltrunas, Yue Shi","year":"2013","venue":"Proceedings of the 7th ACM Conference on Recommender Systems","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Koren et al., 2009] Yehuda Koren, Robert Bell, and Chris Volinsky. Matrix factorization techniques for recom- mender systems. Computer, 42:30-37, 2009.","title":"Matrix factorization techniques for recommender systems","authors":"Koren","year":"2009","venue":"Computer","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Liang et al., 2016] Dawen Liang, Jaan Altosaar, Laurent Charlin, and David M. Blei. Factorization meets the item embedding: Regularizing matrix factorization with item co-occurrence. In Proceedings of the 10th ACM Confer- ence on Recommender Systems, pages 59-66, 2016.","title":"Factorization meets the item embedding: Regularizing matrix factorization with item co-occurrence","authors":"Dawen Liang, Jaan Altosaar, Laurent Charlin, David M Blei","year":"2016","venue":"Proceedings of the 10th ACM Conference on Recommender Systems","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Mikolov et al., 2013] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed rep- resentations of words and phrases and their composition- ality. In Advances in Neural Information Processing Sys- tems, pages 3111-3119. 2013.","title":"Distributed representations of words and phrases and their compositionality","authors":"Mikolov","year":"2013","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Pan et al., 2008] Rong Pan, Yunhong Zhou, Bin Cao, Nathan N Liu, Rajan Lukose, Martin Scholz, and Qiang Yang. One-class collaborative filtering. In 8th IEEE In- ternational Conference on Data Mining, pages 502-511, 2008.","title":"One-class collaborative filtering","authors":"Rong Pan, Yunhong Zhou, Bin Cao, Nathan N Liu, Rajan Lukose, Martin Scholz, Qiang Yang","year":"2008","venue":"8th IEEE International Conference on Data Mining","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Press and Wolf, 2016] Ofir Press and Lior Wolf. Using the output embedding to improve language models. arXiv preprint arXiv:1608.05859, 2016.","title":"Using the output embedding to improve language models","authors":"Lior Wolf","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Rendle et al., 2009] Steffen Rendle, Christoph Freuden- thaler, Zeno Gantner, and Lars Schmidt-Thieme. Bpr: Bayesian personalized ranking from implicit feedback. In Proceedings of the 25th Conference on Uncertainty in Ar- tificial Intelligence, pages 452-461, 2009.","title":"Bpr: Bayesian personalized ranking from implicit feedback","authors":"Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, Lars Schmidt-Thieme","year":"2009","venue":"Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Rendle et al., 2010] Steffen Rendle, Christoph Freuden- thaler, and Lars Schmidt-Thieme. Factorizing person- alized markov chains for next-basket recommendation. In Proceedings of the 19th International Conference on World Wide Web, pages 811-820, 2010.","title":"Factorizing personalized markov chains for next-basket recommendation","authors":"Steffen Rendle, Christoph Freudenthaler, Lars Schmidt-Thieme","year":"2010","venue":"Proceedings of the 19th International Conference on World Wide Web","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Shani et al., 2005] Guy Shani, David Heckerman, and Ro- nen I Brafman. An mdp-based recommender system. Journal of Machine Learning Research, pages 1265-1295, 2005.","title":"An mdp-based recommender system","authors":"Guy Shani, David Heckerman, Ronen I Brafman","year":"2005","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Tan et al., 2016] Yong Kiam Tan, Xinxing Xu, and Yong Liu. Improved recurrent neural networks for session-based recommendations. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, pages 17-22, 2016.","title":"Improved recurrent neural networks for session-based recommendations","authors":"Yong Kiam Tan, Xinxing Xu, Yong Liu","year":"2016","venue":"Proceedings of the 1st Workshop on Deep Learning for Recommender Systems","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Wang et al., 2015] Pengfei Wang, Jiafeng Guo, Yanyan Lan, Jun Xu, Shengxian Wan, and Xueqi Cheng. Learning hierarchical representation model for next-basket recom- mendation. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Infor- mation Retrieval, pages 403-412, 2015.","title":"Learning hierarchical representation model for next-basket recommendation","authors":"Pengfei Wang, Jiafeng Guo, Yanyan Lan, Jun Xu, Shengxian Wan, Xueqi Cheng","year":"2015","venue":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Yu et al., 2016] Feng Yu, Qiang Liu, Shu Wu, Liang Wang, and Tieniu Tan. A dynamic recurrent model for next basket recommendation. In Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 729-732, 2016.","title":"A dynamic recurrent model for next basket recommendation","authors":"Feng Yu, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan","year":"2016","venue":"Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Zhang et al., 2014] Yuyu Zhang, Hanjun Dai, Chang Xu, Jun Feng, Taifeng Wang, Jiang Bian, Bin Wang, and Tie- Yan Liu. Sequential click prediction for sponsored search with recurrent neural networks. In Proceedings of the 20th AAAI Conference on Artificial Intelligence, pages 1369- 1375, 2014.","title":"Sequential click prediction for sponsored search with recurrent neural networks","authors":"Yuyu Zhang, Hanjun Dai, Chang Xu, Jun Feng, Taifeng Wang, Jiang Bian, Bin Wang, Tie-Yan Liu","year":"2014","venue":"Proceedings of the 20th AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}