{"type":"ScholarlyArticle","title":"Collaborative Metric Learning with Memory Network for Multi-Relational Recommender Systems","authors":[{"name":"Xiao Zhou","slug":"xiao-zhou","given":"Xiao","family":"Zhou","affiliation":"Department of Computer Science and Technology , University of Cambridge , UK"},{"name":"Danyang Liu","slug":"danyang-liu","given":"Danyang","family":"Liu","affiliation":"University of Science and Technology of China , Hefei , China"},{"name":"Jianxun Lian","slug":"jianxun-lian","given":"Jianxun","family":"Lian","affiliation":"Microsoft Research , Beijing , China"},{"name":"Xing Xie","slug":"xing-xie","given":"Xing","family":"Xie","affiliation":"Microsoft Research , Beijing , China"}],"abstract":"The success of recommender systems in modern online platforms is inseparable from the accurate capture of users' personal tastes. In everyday life, large amounts of user feedback data are created along with user-item online interactions in a variety of ways, such as browsing, purchasing, and sharing. These multiple types of user feedback provide us with tremendous opportunities to detect individuals' fine-grained preferences. Different from most existing recommender systems that rely on a single type of feedback, we advocate incorporating multiple types of user-item interactions for better recommendations. Based on the observation that the underlying spectrum of user preferences is reflected in various types of interactions with items and can be uncovered by latent relational learning in metric space, we propose a unified neural learning framework, named Multi-Relational Memory Network (MRMN). It can not only model fine-grained user-item relations but also enable us to discriminate between feedback types in terms of the strength and diversity of user preferences. Extensive experiments show that the proposed MRMN model outperforms competitive state-of-the-art algorithms in a wide range of scenarios, including e-commerce, local services, and job recommendations.","doi":"10.24963/ijcai.2019/619","url":"https://ijcai.org/proceedings/2019/619","pdf":"https://ijcai.org/proceedings/2019/0619.pdf","markdown":"https://ijcai.org/proceedings/2019/619.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/619","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning M-Z","track":"Main track","number":619,"pages":{"first":4454,"last":4460},"keywords":["Humans and AI: Personalization and User Modeling","Machine Learning: Recommender Systems","Machine Learning: Deep Learning","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Bordes et al., 2013] Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. Translating embeddings for modeling multi-relational data. In Advances in neural information processing systems, pages 2787-2795, 2013.","title":"Translating embeddings for modeling multi-relational data","authors":"Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, Oksana Yakhnenko","year":"2013","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Chen et al., 2018] Xu Chen, Hongteng Xu, Yongfeng Zhang, Jiaxi Tang, Yixin Cao, Zheng Qin, and Hongyuan Zha. Sequential recommendation with user memory net- works. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pages 108- 116. ACM, 2018.","title":"Sequential recommendation with user memory networks","authors":"Hongteng Xu Chen, Yongfeng Xu, Jiaxi Zhang, Yixin Tang, Zheng Cao, Hongyuan Qin, Zha","year":"2018","venue":"Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Ding et al., 2018a] Jingtao Ding, Fuli Feng, Xiangnan He, Guanghui Yu, Yong Li, and Depeng Jin. An improved sampler for bayesian personalized ranking by leveraging view data. In Companion of the The Web Conference 2018 on The Web Conference 2018, pages 13-14. International World Wide Web Conferences Steering Committee, 2018.","title":"An improved sampler for bayesian personalized ranking by leveraging view data","authors":"Ding","year":"2018","venue":"Companion of the The Web Conference 2018 on The Web Conference 2018","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Ding et al., 2018b] Jingtao Ding, Guanghui Yu, Xiangnan He, Yuhan Quan, Yong Li, Tat-Seng Chua, Depeng Jin, and Jiajie Yu. Improving implicit recommender systems with view data. In IJCAI, pages 3343-3349, 2018.","title":"Improving implicit recommender systems with view data","authors":"Ding","year":"2018","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2018/464"},{"position":5,"raw":"[Ebesu et al., 2018] Travis Ebesu, Bin Shen, and Yi Fang. Collaborative memory network for recommendation sys- tems. arXiv preprint arXiv:1804.10862, 2018.","title":"Collaborative memory network for recommendation systems","authors":"Travis Ebesu, Bin Shen, Yi Fang","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Ekstrand et al., 2011] Michael D Ekstrand, John T Riedl, Joseph A Konstan, et al. Collaborative filtering recom- mender systems. Foundations and Trends R in Human- Computer Interaction, 4(2):81-173, 2011.","title":"Collaborative filtering recommender systems","authors":"John T Michael D Ekstrand, Joseph A Riedl, Konstan","year":"2011","venue":"Foundations and Trends R in Human-Computer Interaction","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[He and McAuley, 2016] Ruining He and Julian McAuley. Vbpr: Visual bayesian personalized ranking from implicit feedback. In AAAI, pages 144-150, 2016.","title":"Ruining He and Julian McAuley. Vbpr: Visual bayesian personalized ranking from implicit feedback","authors":"Mcauley He","year":"2016","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Hsieh et al., 2017] Cheng-Kang Hsieh, Longqi Yang, Yin Cui, Tsung-Yi Lin, Serge Belongie, and Deborah Estrin. Collaborative metric learning. In Proceedings of the 26th International Conference on World Wide Web, pages 193- 201. International World Wide Web Conferences Steering Committee, 2017.","title":"Collaborative metric learning","authors":"Cheng-Kang Hsieh, Longqi Yang, Yin Cui, Tsung-Yi Lin, Serge Belongie, Deborah Estrin","year":"2017","venue":"Proceedings of the 26th International Conference on World Wide Web","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Hu et al., 2008] Yifan Hu, Yehuda Koren, and Chris Volin- sky. Collaborative filtering for implicit feedback datasets. In Data Mining, 2008. ICDM'08. Eighth IEEE Interna- tional Conference on, pages 263-272. Ieee, 2008.","title":"Collaborative filtering for implicit feedback datasets","authors":"Yifan Hu, Yehuda Koren, Chris Volinsky","year":"2008","venue":"ICDM'08. Eighth IEEE International Conference on","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Huang et al., 2017] Haoran Huang, Qi Zhang, Xuanjing Huang, et al. Mention recommendation for twitter with end-to-end memory network. In Proc. IJCAI, volume 17, pages 1872-1878, 2017.","title":"Mention recommendation for twitter with end-to-end memory network","authors":"Haoran Huang, Qi Zhang, Xuanjing Huang","year":"2017","venue":"Proc. IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2017/260"},{"position":11,"raw":"[Koren, 2008] Yehuda Koren. Factorization meets the neigh- borhood: a multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD international con- ference on Knowledge discovery and data mining, pages 426-434. ACM, 2008.","title":"Factorization meets the neighborhood: a multifaceted collaborative filtering model","authors":"Koren ;, Yehuda Koren","year":"2008","venue":"Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Li et al., 2018] Zhi Li, Hongke Zhao, Qi Liu, Zhenya Huang, Tao Mei, and Enhong Chen. Learning from his- tory and present: next-item recommendation via discrim- inatively exploiting user behaviors. In Proceedings of the 24th ACM SIGKDD International Conference on Knowl- edge Discovery & Data Mining, pages 1734-1743. ACM, 2018.","title":"Learning from history and present: next-item recommendation via discriminatively exploiting user behaviors","authors":"Zhi Li, Hongke Zhao, Qi Liu, Zhenya Huang, Tao Mei, Enhong Chen","year":"2018","venue":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Lian et al., 2018] Jianxun Lian, Xiaohuan Zhou, Fuzheng Zhang, Zhongxia Chen, Xing Xie, and Guangzhong Sun. xdeepfm: Combining explicit and implicit feature inter- actions for recommender systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowl- edge Discovery & Data Mining, pages 1754-1763. ACM, 2018.","title":"xdeepfm: Combining explicit and implicit feature interactions for recommender systems","authors":"Lian","year":"2018","venue":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Liu et al., 2017] Jian Liu, Chuan Shi, Binbin Hu, Shenghua Liu, and S Yu Philip. Personalized ranking recommen- dation via integrating multiple feedbacks. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, pages 131-143. Springer, 2017.","title":"Personalized ranking recommendation via integrating multiple feedbacks","authors":"Jian Liu, Chuan Shi, Binbin Hu, Shenghua Liu, S Yu, Philip","year":"2017","venue":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Loni et al., 2016] Babak Loni, Roberto Pagano, Martha Larson, and Alan Hanjalic. Bayesian personalized ranking with multi-channel user feedback. In Proceedings of the 10th ACM Conference on Recommender Systems, pages 361-364. ACM, 2016.","title":"Bayesian personalized ranking with multi-channel user feedback","authors":"Babak Loni, Roberto Pagano, Martha Larson, Alan Hanjalic","year":"2016","venue":"Proceedings of the 10th ACM Conference on Recommender Systems","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Pan et al., 2010] Weike Pan, Evan Wei Xiang, Nathan Nan Liu, and Qiang Yang. Transfer learning in collaborative filtering for sparsity reduction. In AAAI, volume 10, pages 230-235, 2010.","title":"Transfer learning in collaborative filtering for sparsity reduction","authors":"Weike Pan, Evan Wei Xiang, Nathan, Nan Liu, Qiang Yang","year":"2010","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Pazzani and Billsus, 2007] Michael J Pazzani and Daniel Billsus. Content-based recommendation systems. In The adaptive web, pages 325-341. Springer, 2007.","title":"Content-based recommendation systems","authors":"J Michael, Daniel Pazzani, Billsus","year":"2007","venue":"The adaptive web","doi":null,"url":null,"internal_url":null},{"position":18,"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 twenty-fifth conference on uncertainty in artificial intelligence. AUAI Press, 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 twenty-fifth conference on uncertainty in artificial intelligence","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Singh and Gordon, 2008] Ajit P. Singh and Geoffrey J. Gor- don. Relational learning via collective matrix factoriza- tion. In Proceedings of the 14th ACM SIGKDD Interna- tional Conference on Knowledge Discovery and Data Min- ing, pages 650-658. ACM, 2008.","title":"Relational learning via collective matrix factorization","authors":"P Ajit, Geoffrey J Singh, Gordon","year":"2008","venue":"Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Tang et al., 2016] Liang Tang, Bo Long, Bee-Chung Chen, and Deepak Agarwal. An empirical study on recommen- dation with multiple types of feedback. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 283-292. ACM, 2016.","title":"An empirical study on recommendation with multiple types of feedback","authors":"Liang Tang, Bo Long, Bee-Chung Chen, Deepak Agarwal","year":"2016","venue":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Tay et al., 2018] Yi Tay, Luu Anh Tuan, and Siu Cheung Hui. Latent relational metric learning via memory-based attention for collaborative ranking. In Proceedings of the 2018 World Wide Web Conference on World Wide Web, pages 729-739. International World Wide Web Confer- ences Steering Committee, 2018.","title":"Latent relational metric learning via memory-based attention for collaborative ranking","authors":"Yi Tay, Anh Luu, Siu Tuan, Hui Cheung","year":"2018","venue":"Proceedings of the 2018 World Wide Web Conference on World Wide Web","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Weston et al., 2015] Jason Weston, Sumit Chopra, and An- toine Bordesm. Memory networks. In ICLR, 2015.","title":"Antoine Bordesm. Memory networks","authors":"Jason Weston, Sumit Chopra","year":"2015","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Zhou et al., 2019] Xiao Zhou, Cecilia Mascolo, and Zhongxiang Zhao. Topic-enhanced memory networks for personalised point-of-interest recommendation. arXiv preprint arXiv:1905.13127, 2019.","title":"Topic-enhanced memory networks for personalised point-of-interest recommendation","authors":"Xiao Zhou, Cecilia Mascolo, Zhongxiang Zhao","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}