{"type":"ScholarlyArticle","title":"NeuRec: On Nonlinear Transformation for Personalized Ranking","authors":[{"name":"Shuai Zhang","slug":"shuai-zhang","given":"Shuai","family":"Zhang","affiliation":"University of New South Wales ,"},{"name":"Lina Yao","slug":"lina-yao","given":"Lina","family":"Yao","affiliation":"University of New South Wales ,"},{"name":"Aixin Sun","slug":"aixin-sun","given":"Aixin","family":"Sun","affiliation":"Nanyang Technological University ,"},{"name":"Sen Wang","slug":"sen-wang","given":"Sen","family":"Wang","affiliation":"Griffith University"},{"name":"Guodong Long","slug":"guodong-long","given":"Guodong","family":"Long","affiliation":"University of Technology Sydney"},{"name":"Manqing Dong","slug":"manqing-dong","given":"Manqing","family":"Dong","affiliation":"University of New South Wales ,"}],"abstract":"Modeling user-item interaction patterns is an important task for personalized recommendations. Many recommender systems are based on the assumption that there exists a linear relationship between users and items while neglecting the intricacy and non-linearity of real-life historical interactions. In this paper, we propose a neural network based recommendation model (NeuRec) that untangles the complexity of user-item interactions and establish an integrated network to combine non-linear transformation with latent factors. We further design two variants of NeuRec: user-based NeuRec and item-based NeuRec, by focusing on different aspects of the interaction matrix. Extensive experiments on four real-world datasets demonstrated their superior performances on personalized ranking task.","doi":"10.24963/ijcai.2018/510","url":"https://ijcai.org/proceedings/2018/510","pdf":"https://ijcai.org/proceedings/2018/0510.pdf","markdown":"https://ijcai.org/proceedings/2018/510.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/510","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":510,"pages":{"first":3669,"last":3675},"keywords":["Machine Learning: Recommender Systems","Machine Learning: Neural Networks","Machine Learning: Deep Learning"],"videos":[],"references":[{"position":1,"raw":"[Baltrunas et al., 2015] Linas Baltrunas, Karen Church, et al. Frappe: Understanding the usage and perception of mo- bile app recommendations in-the-wild. arXiv preprint arXiv:1505.03014, 2015.","title":"Frappe: Understanding the usage and perception of mobile app recommendations in-the-wild","authors":"Baltrunas","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Cheng et al., 2016] Heng-Tze Cheng, Levent Koc, et al. Wide & deep learning for recommender systems. In DLRS, pages 7-10. ACM, 2016.","title":"Wide & deep learning for recommender systems","authors":"Heng-Tze Cheng, Levent Koc","year":"2016","venue":"DLRS","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Guo et al., 2013] G. Guo, J. Zhang, and N. Yorke-Smith. A novel bayesian similarity measure for recommender sys- tems. In IJCAI, pages 2619-2625, 2013.","title":"A novel bayesian similarity measure for recommender systems","authors":"G Guo, J Zhang, N Yorke-Smith","year":"2013","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2013/386"},{"position":4,"raw":"[Harper and Konstan, 2015] F. Maxwell Harper and Joseph A. Konstan. The movielens datasets: His- tory and context. ACM Trans. Interact. Intell. Syst., 5(4):19:1-19:19, December 2015.","title":"The movielens datasets: History and context","authors":"F, Maxwell Harper, Joseph A Konstan","year":"2015","venue":"ACM Trans. Interact. Intell. Syst","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[He and Chua, 2017] Xiangnan He and Tat-Seng Chua. Neu- ral factorization machines for sparse predictive analytics. In SIGIR, pages 355-364, NY, USA, 2017. ACM.","title":"Xiangnan He and Tat-Seng Chua. Neural factorization machines for sparse predictive analytics","authors":"Chua He","year":"2017","venue":"SIGIR","doi":null,"url":null,"internal_url":null},{"position":6,"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":7,"raw":"[He et al., 2017] Xiangnan He, Lizi Liao, et al. Neural col- laborative filtering. In WWW, pages 173-182, 2017.","title":"Neural collaborative filtering","authors":"He","year":"2017","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Hidasi et al., 2015] Balázs Hidasi, Alexandros Karat- zoglou, Linas Baltrunas, and Domonkos Tikk. Session- based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939, 2015.","title":"Sessionbased recommendations with recurrent neural networks","authors":"Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, Domonkos Tikk","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Hong-Jian Xue and others, 2017] Xinyu Dai Hong- Jian Xue et al. Deep matrix factorization models for recommender systems. In IJCAI, pages 3203-3209, 2017.","title":"Deep matrix factorization models for recommender systems","authors":"Xinyu Dai, Hong-Jian Xue","year":"2017","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2017/447"},{"position":10,"raw":"[Karatzoglou and Hidasi, 2017] Alexandros Karatzoglou and Balázs Hidasi. Deep learning for recommender systems. In RecSys, RecSys '17, pages 396-397, New York, NY, USA, 2017. ACM.","title":"Deep learning for recommender systems","authors":"Alexandros Karatzoglou, Balázs Hidasi","year":"2017","venue":"RecSys, RecSys '17","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Kim et al., 2016] Donghyun Kim, Chanyoung Park, et al. Convolutional matrix factorization for document context- aware recommendation. In RecSys, pages 233-240. ACM, 2016.","title":"Convolutional matrix factorization for document contextaware recommendation","authors":"Donghyun Kim, Chanyoung Park","year":"2016","venue":"RecSys","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Kingma and Ba, 2014] Diederik Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.","title":"Adam: A method for stochastic optimization","authors":"Diederik Kingma, Jimmy Ba","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Koren and Bell, 2015] Yehuda Koren and Robert Bell. Ad- vances in collaborative filtering. In Recommender systems handbook, pages 77-118. Springer, 2015.","title":"Advances in collaborative filtering","authors":"Koren, Bell ;, Yehuda Koren, Robert Bell","year":"2015","venue":"Recommender systems handbook","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Koren et al., 2009] Yehuda Koren, Robert Bell, and Chris Volinsky. Matrix factorization techniques for recom- mender systems. Computer, 42(8):30-37, August 2009.","title":"Matrix factorization techniques for recommender systems","authors":"Koren","year":"2009","venue":"Computer","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Koren, 2008] Yehuda Koren. Factorization meets the neigh- borhood: a multifaceted collaborative filtering model. In SIGKDD, pages 426-434. ACM, 2008.","title":"Factorization meets the neighborhood: a multifaceted collaborative filtering model","authors":"Koren ;, Yehuda Koren","year":"2008","venue":"SIGKDD","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Linden et al., 2003] G. Linden, B. Smith, and J. York. Ama- zon.com recommendations: item-to-item collaborative fil- tering. IEEE Internet Computing, 7(1):76-80, Jan 2003.","title":"com recommendations: item-to-item collaborative filtering","authors":"G Linden, B Smith, J York, Amazon","year":"2003","venue":"IEEE Internet Computing","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Liu and others, 2009] Tie-Yan Liu et al. Learning to rank for information retrieval. Foundations and Trends R in In- formation Retrieval, 3(3):225-331, 2009.","title":"Learning to rank for information retrieval","authors":"Others ; Tie-Yan Liu, Liu","year":"2009","venue":"Foundations and Trends R in Information Retrieval","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Ning and Karypis, 2011] X. Ning and G. Karypis. Slim: Sparse linear methods for top-n recommender systems. In ICDM, pages 497-506, Dec 2011.","title":"Slim: Sparse linear methods for top-n recommender systems","authors":"Karypis Ning, X Ning, G Karypis","year":"2011","venue":"ICDM","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Rendle et al., 2009] Steffen Rendle, Christoph Freuden- thaler, et al. Bpr: Bayesian personalized ranking from implicit feedback. In UAI, pages 452-461, 2009.","title":"Bpr: Bayesian personalized ranking from implicit feedback","authors":"Steffen Rendle, Christoph Freudenthaler","year":"2009","venue":"UAI","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Ricci et al., 2015] Francesco Ricci, Lior Rokach, Bracha Shapira, and Paul B Kantor. Recommender systems hand- book. Springer, 2015.","title":"Recommender systems handbook","authors":"Francesco Ricci, Lior Rokach, Bracha Shapira, Paul B Kantor","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Salakhutdinov and Mnih, 2007] Ruslan Salakhutdinov and Andriy Mnih. Probabilistic matrix factorization. In NIPS, pages 1257-1264, USA, 2007.","title":"Ruslan Salakhutdinov and Andriy Mnih. Probabilistic matrix factorization","authors":"Mnih Salakhutdinov","year":"2007","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"Curran Associates Inc. [Sedhain et al., 2015] Suvash Sedhain, Aditya Krishna Menon, et al. Autorec: Autoencoders meet collaborative filtering. In WWW, pages 111-112. ACM, 2015.","title":"Autorec: Autoencoders meet collaborative filtering","authors":"Curran Associates, Inc [ Sedhain","year":"2015","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Shani and Gunawardana, 2011] Guy Shani and Asela Gu- nawardana. Evaluating recommendation systems. Rec- ommender systems handbook, pages 257-297, 2011.","title":"Guy Shani and Asela Gunawardana","authors":"Gunawardana Shani","year":"2011","venue":"Evaluating recommendation systems. Recommender systems handbook","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Srivastava et al., 2014] Nitish Srivastava, Geoffrey Hinton, et al. Dropout: A simple way to prevent neural networks from overfitting. JMLR, 15(1):1929-1958, 2014.","title":"Dropout: A simple way to prevent neural networks from overfitting","authors":"Nitish Srivastava, Geoffrey Hinton","year":"2014","venue":"JMLR","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Tay et al., 2018a] Yi Tay, Luu Anh Tuan, et al. Latent rela- tional metric learning via memory-based attention for col- laborative ranking. In WWW, pages 729-739, 2018.","title":"Latent relational metric learning via memory-based attention for collaborative ranking","authors":"Tay","year":"2018","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Tay et al., 2018b] Yi Tay, Luu Anh Tuan, et al. Multi- pointer co-attention networks for recommendation. CoRR, abs/1801.09251, 2018.","title":"Multipointer co-attention networks for recommendation","authors":"Tay","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Van den Oord et al., 2013] Aaron Van den Oord, Sander Dieleman, and Benjamin Schrauwen. Deep content-based music recommendation. In NIPS, pages 2643-2651, 2013.","title":"Deep content-based music recommendation","authors":"Oord Van Den","year":"2013","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Wang et al., 2015] Hao Wang, Naiyan Wang, and Dit-Yan Yeung. Collaborative deep learning for recommender sys- tems. In SIGKDD, pages 1235-1244. ACM, 2015.","title":"Collaborative deep learning for recommender systems","authors":"Hao Wang, Naiyan Wang, Dit-Yan Yeung","year":"2015","venue":"SIGKDD","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Wu et al., 2017] Chao-Yuan Wu, Amr Ahmed, et al. Recur- rent recommender networks. In WSDM, pages 495-503, NY, USA, 2017. ACM.","title":"Recurrent recommender networks","authors":"Chao-Yuan, Amr Wu, Ahmed","year":"2017","venue":"WSDM","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Zhang et al., 2013] Weinan Zhang, Tianqi Chen, Jun Wang, and Yong Yu. Optimizing top-n collaborative filtering via dynamic negative item sampling. In SIGIR, pages 785- 788, New York, NY, USA, 2013. ACM.","title":"Optimizing top-n collaborative filtering via dynamic negative item sampling","authors":"Weinan Zhang, Tianqi Chen, Jun Wang, Yong Yu","year":"2013","venue":"SIGIR","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Zhang et al., 2017a] Shuai Zhang, Lina Yao, and Aixin Sun. Deep learning based recommender system: A survey and new perspectives. arXiv preprint arXiv:1707.07435, 2017.","title":"Deep learning based recommender system: A survey and new perspectives","authors":"Zhang","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Zhang et al., 2017b] Shuai Zhang, Lina Yao, and Xiwei Xu. Autosvd++: An efficient hybrid collaborative filter- ing model via contractive auto-encoders. In SIGIR, pages 957-960, New York, NY, USA, 2017. ACM.","title":"Autosvd++: An efficient hybrid collaborative filtering model via contractive auto-encoders","authors":"Zhang","year":"2017","venue":"SIGIR","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Zheng et al., 2017] Lei Zheng, Vahid Noroozi, et al. Joint deep modeling of users and items using reviews for rec- ommendation. In WSDM, pages 425-434. ACM, 2017.","title":"Joint deep modeling of users and items using reviews for recommendation","authors":"Lei Zheng, Vahid Noroozi","year":"2017","venue":"WSDM","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}