{"type":"ScholarlyArticle","title":"MRLR: Multi-level Representation Learning for Personalized Ranking in Recommendation","authors":[{"name":"Zhu Sun","slug":"zhu-sun","given":"Zhu","family":"Sun","affiliation":"Nanyang Technological University , Singapore"},{"name":"Jie Yang","slug":"jie-yang","given":"Jie","family":"Yang","affiliation":"Delft University of Technology , The Netherlands"},{"name":"Jie Zhang","slug":"jie-zhang","given":"Jie","family":"Zhang","affiliation":"Nanyang Technological University , Singapore"},{"name":"Alessandro Bozzon","slug":"alessandro-bozzon","given":"Alessandro","family":"Bozzon","affiliation":"Delft University of Technology , The Netherlands"},{"name":"Yu Chen","slug":"yu-chen","given":"Yu","family":"Chen","affiliation":"Nanyang Technological University , Singapore"},{"name":"Chi Xu","slug":"chi-xu","given":"Chi","family":"Xu","affiliation":"Singapore Institute of Manufacturing Technology , Singapore"}],"abstract":"Representation learning (RL) has recently proven to be effective in capturing local item relationships by modeling item co-occurrence in individual user's interaction record. However, the value of RL for recommendation has not reached the full potential due to two major drawbacks: 1)  recommendation is modeled as a rating prediction problem but should essentially be a personalized ranking one; 2) multi-level organizations of items are neglected for fine-grained item relationships. We design a unified Bayesian framework MRLR to learn user and item embeddings from a multi-level item organization, thus benefiting from RL as well as achieving the goal of personalized ranking. Extensive validation on real-world datasets shows that MRLR consistently outperforms state-of-the-art algorithms.","doi":"10.24963/ijcai.2017/391","url":"https://ijcai.org/proceedings/2017/391","pdf":"https://ijcai.org/proceedings/2017/0391.pdf","markdown":"https://ijcai.org/proceedings/2017/391.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/391","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning S-Z","track":"Main track","number":391,"pages":{"first":2807,"last":2813},"keywords":["Machine Learning: Learning Preferences or Rankings","Multidisciplinary Topics and Applications: Personalization and User Modeling"],"videos":[],"references":[{"position":1,"raw":"[Barkan and Koenigstein, 2016] Oren Barkan and Noam Koenigstein. Item2vec: Neural item embedding for col- laborative filtering. IEEE Workshop on MLSP, 2016.","title":"Item2vec: Neural item embedding for collaborative filtering","authors":"Koenigstein Barkan, Oren Barkan, Noam Koenigstein","year":"2016","venue":"IEEE Workshop on MLSP","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bengio et al., 2003] Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Jauvin. A neural probabilis- tic language model. JMLR, 3(Feb):1137-1155, 2003.","title":"A neural probabilistic language model","authors":"Yoshua Bengio, Réjean Ducharme, Pascal Vincent, Christian Jauvin","year":"2003","venue":"JMLR","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chen et al., 2012] Tianqi Chen, Weinan Zhang, Qiuxia Lu, Kailong Chen, Zhao Zheng, and Yong Yu. Svdfeature: a toolkit for feature-based collaborative filtering. JMLR, 13(1):3619-3622, 2012.","title":"Svdfeature: a toolkit for feature-based collaborative filtering","authors":"Tianqi Chen, Weinan Zhang, Qiuxia Lu, Kailong Chen, Zhao Zheng, Yong Yu","year":"2012","venue":"JMLR","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Covington et al., 2016] Paul Covington, Jay Adams, and Emre Sargin. Deep neural networks for youtube recom- mendations. In RecSys, pages 191-198. ACM, 2016.","title":"Deep neural networks for youtube recommendations","authors":"Paul Covington, Jay Adams, Emre Sargin","year":"2016","venue":"RecSys","doi":null,"url":null,"internal_url":null},{"position":5,"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 KDD, pages 1809-1818. ACM, 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":"KDD","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[He et al., 2016] Ruining He, Chunbin Lin, Jianguo Wang, and Julian McAuley. Sherlock: sparse hierarchical em- beddings for visually-aware one-class collaborative filter- ing. In IJCAI, pages 3740-3746, 2016.","title":"Sherlock: sparse hierarchical embeddings for visually-aware one-class collaborative filtering","authors":"He","year":"2016","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2016/526"},{"position":7,"raw":"[Karatzoglou et al., 2010] Alexandros Karatzoglou, Xavier Amatriain, Linas Baltrunas, and Nuria Oliver. Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering. In RecSys, pages 79- 86. ACM, 2010.","title":"Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering","authors":"Alexandros Karatzoglou, Xavier Amatriain, Linas Baltrunas, Nuria Oliver","year":"2010","venue":"RecSys","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Koren, 2008] Yehuda Koren. Factorization meets the neigh- borhood: a multifaceted collaborative filtering model. In KDD, pages 426-434. ACM, 2008.","title":"Factorization meets the neighborhood: a multifaceted collaborative filtering model","authors":"Koren ;, Yehuda Koren","year":"2008","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Le and Mikolov, 2014] Quoc V Le and Tomas Mikolov. Distributed representations of sentences and documents. In ICML, pages 1188-1196, 2014.","title":"Distributed representations of sentences and documents","authors":"V Quoc, Tomas Le, Mikolov","year":"2014","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Lee and Seung, 2001] Daniel D Lee and H Sebastian Seung. Algorithms for non-negative matrix factorization. In NIPS, pages 556-562, 2001.","title":"Algorithms for non-negative matrix factorization","authors":"D Daniel, H Lee, Seung Sebastian","year":"2001","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Levy and Goldberg, 2014] Omer Levy and Yoav Goldberg. Neural word embedding as implicit matrix factorization. In NIPS, pages 2177-2185, 2014.","title":"Neural word embedding as implicit matrix factorization","authors":"Goldberg Levy, Omer Levy, Yoav Goldberg","year":"2014","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":12,"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 RecSys, pages 59-66. ACM, 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":"RecSys","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Maaten and Hinton, 2008] Laurens van der Maaten and Ge- offrey Hinton. Visualizing data using t-sne. JMLR, 9(Nov):2579-2605, 2008.","title":"Laurens van der Maaten and Geoffrey Hinton","authors":"Hinton Maaten","year":"2008","venue":"JMLR","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[McAuley et al., 2015] Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton van den Hengel. Image-based rec- ommendations on styles and substitutes. In SIGIR, pages 43-52. ACM, 2015.","title":"Image-based recommendations on styles and substitutes","authors":"Julian Mcauley, Christopher Targett, Qinfeng Shi, Anton Van Den, Hengel","year":"2015","venue":"SIGIR","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Mikolov et al., 2013a] Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781, 2013.","title":"Efficient estimation of word representations in vector space","authors":"Mikolov","year":"2013","venue":null,"doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Mikolov et al., 2013b] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed repre- sentations of words and phrases and their compositionality. In NIPS, pages 3111-3119, 2013.","title":"Distributed representations of words and phrases and their compositionality","authors":"Mikolov","year":"2013","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Mnih and Salakhutdinov, 2007] Andriy Mnih and Ruslan Salakhutdinov. Probabilistic matrix factorization. In NIPS, pages 1257-1264, 2007.","title":"Probabilistic matrix factorization","authors":"Andriy Mnih, Ruslan Salakhutdinov","year":"2007","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Pennington et al., 2014] Jeffrey Pennington, Richard Socher, and Christopher D Manning. Glove: Global vectors for word representation. In EMNLP, volume 14, pages 1532-43, 2014.","title":"Glove: Global vectors for word representation","authors":"Jeffrey Pennington, Richard Socher, Christopher D Manning","year":"2014","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Rendle et al., 2009] Steffen Rendle, Christoph Freuden- thaler, Zeno Gantner, and Lars Schmidt-Thieme. Bpr: Bayesian personalized ranking from implicit feedback. In UAI, pages 452-461. AUAI Press, 2009.","title":"Bpr: Bayesian personalized ranking from implicit feedback","authors":"Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, Lars Schmidt-Thieme","year":"2009","venue":"UAI","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Rendle, 2010] Steffen Rendle. Factorization machines. In ICDM, pages 995-1000. IEEE, 2010.","title":"Factorization machines","authors":"Steffen Rendle","year":"2010","venue":"ICDM","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Shi et al., 2012] Yue Shi, Alexandros Karatzoglou, Linas Baltrunas, Martha Larson, Nuria Oliver, and Alan Han- jalic. Climf: learning to maximize reciprocal rank with collaborative less-is-more filtering. In RecSys, pages 139- 146. ACM, 2012.","title":"Climf: learning to maximize reciprocal rank with collaborative less-is-more filtering","authors":"Yue Shi, Alexandros Karatzoglou, Linas Baltrunas, Martha Larson, Nuria Oliver, Alan Hanjalic","year":"2012","venue":"RecSys","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Shi et al., 2014] Yue Shi, Martha Larson, and Alan Han- jalic. Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges. ACM Computing Surveys (CSUR), 47(1):3, 2014.","title":"Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges","authors":"Yue Shi, Martha Larson, Alan Hanjalic","year":"2014","venue":"ACM Computing Surveys (CSUR)","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Singh and Gordon, 2008] Ajit P Singh and Geoffrey J Gor- don. Relational learning via collective matrix factoriza- tion. In KDD, pages 650-658. ACM, 2008.","title":"Relational learning via collective matrix factorization","authors":"P Ajit, Geoffrey J Singh, Gordon","year":"2008","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Steck, 2013] Harald Steck. Evaluation of recommenda- tions: rating-prediction and ranking. In RecSys, pages 213-220. ACM, 2013.","title":"Evaluation of recommendations: rating-prediction and ranking","authors":"Harald Steck, Steck","year":"2013","venue":"RecSys","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Su and Khoshgoftaar, 2009] Xiaoyuan Su and Taghi M Khoshgoftaar. A survey of collaborative filtering tech- niques. Advances in artificial intelligence, 2009:4, 2009.","title":"A survey of collaborative filtering techniques","authors":"Xiaoyuan Su, M Taghi, Khoshgoftaar","year":"2009","venue":"Advances in artificial intelligence","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Vasile et al., 2016] Flavian Vasile, Elena Smirnova, and Alexis Conneau. Meta-prod2vec: Product embeddings using side-information for recommendation. In RecSys, pages 225-232. ACM, 2016.","title":"Meta-prod2vec: Product embeddings using side-information for recommendation","authors":"Flavian Vasile, Elena Smirnova, Alexis Conneau","year":"2016","venue":"RecSys","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Weimer et al., 2007] Markus Weimer, Alexandros Karat- zoglou, Quoc Viet Le, and Alex Smola. Maximum margin matrix factorization for collaborative ranking. In NIPS, pages 1-8, 2007.","title":"Maximum margin matrix factorization for collaborative ranking","authors":"Markus Weimer, Alexandros Karatzoglou, Quoc Viet Le, Alex Smola","year":"2007","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Yang et al., 2016] Jie Yang, Zhu Sun, Alessandro Bozzon, and Jie Zhang. Learning hierarchical feature influence for recommendation by recursive regularization. In RecSys, pages 51-58. ACM, 2016.","title":"Learning hierarchical feature influence for recommendation by recursive regularization","authors":"Jie Yang, Zhu Sun, Alessandro Bozzon, Jie Zhang","year":"2016","venue":"RecSys","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}