{"type":"ScholarlyArticle","title":"Dynamic Bayesian Logistic Matrix Factorization for Recommendation with Implicit Feedback","authors":[{"name":"Yong Liu","slug":"yong-liu","given":"Yong","family":"Liu","affiliation":"† Institute for Infocomm Research , A; ‡ NTUC Link Pte. Ltd. , Singapore Tencent Social Ads , China"},{"name":"Lifan Zhao","slug":"lifan-zhao","given":"Lifan","family":"Zhao","affiliation":"† Institute for Infocomm Research , A"},{"name":"Guimei Liu","slug":"guimei-liu","given":"Guimei","family":"Liu","affiliation":"† Institute for Infocomm Research , A"},{"name":"Xinyan Lu","slug":"xinyan-lu","given":"Xinyan","family":"Lu","affiliation":"* STAR , Singapore"},{"name":"Peng Gao","slug":"peng-gao","given":"Peng","family":"Gao","affiliation":"* STAR , Singapore"},{"name":"Xiao-Li Li","slug":"xiao-li-li","given":"Xiao-Li","family":"Li","affiliation":"† Institute for Infocomm Research , A"},{"name":"Zhihui Jin","slug":"zhihui-jin","given":"Zhihui","family":"Jin","affiliation":"* STAR , Singapore"}],"abstract":"Matrix factorization has been widely adopted for recommendation by learning latent embeddings of users and items from observed user-item interaction data. However, previous methods usually assume the learned embeddings are static or homogeneously evolving with the same diffusion rate. This is not valid in most scenarios, where users’ preferences and item attributes heterogeneously drift over time. To remedy this issue, we have proposed a novel dynamic matrix factorization model, named Dynamic Bayesian Logistic Matrix Factorization (DBLMF), which aims to learn heterogeneous user and item embeddings that are drifting with inconsistent diffusion rates. More specifically, DBLMF extends logistic matrix factorization to model the probability a user would like to interact with an item at a given timestamp, and a diffusion process to connect latent embeddings over time. In addition, an efficient Bayesian inference algorithm has also been proposed to make DBLMF scalable on large datasets. The effectiveness of the proposed method has been demonstrated by extensive experiments on real datasets, compared with the state-of-the-art methods.","doi":"10.24963/ijcai.2018/481","url":"https://ijcai.org/proceedings/2018/481","pdf":"https://ijcai.org/proceedings/2018/0481.pdf","markdown":"https://ijcai.org/proceedings/2018/481.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/481","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":481,"pages":{"first":3463,"last":3469},"keywords":["Machine Learning: Recommender Systems","Multidisciplinary Topics and Applications: Recommender Systems"],"videos":[],"references":[{"position":1,"raw":"[Blei et al., 2017] David M Blei, Alp Kucukelbir, and Jon D McAuliffe. Variational inference: A review for statisti- cians. Journal of the American Statistical Association, 2017.","title":"Variational inference: A review for statisticians","authors":"Alp David M Blei, Jon D Kucukelbir, Mcauliffe","year":"2017","venue":"Journal of the American Statistical Association","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Charlin et al., 2015] Laurent Charlin, Rajesh Ranganath, James McInerney, and David M Blei. Dynamic poisson factorization. In RecSyS'15, pages 155-162, 2015.","title":"Dynamic poisson factorization","authors":"Laurent Charlin, Rajesh Ranganath, James Mcinerney, David M Blei","year":"2015","venue":"RecSyS'","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Du et al., 2015] Nan Du, Yichen Wang, Niao He, Jimeng Sun, and Le Song. Time-sensitive recommendation from recurrent user activities. In NIPS'15, pages 3492-3500, 2015.","title":"Time-sensitive recommendation from recurrent user activities","authors":"Nan Du, Yichen Wang, Niao He, Jimeng Sun, Le Song","year":"2015","venue":"NIPS'15","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Gultekin and Paisley, 2014] San Gultekin and John Paisley. A collaborative kalman filter for time-evolving dyadic pro- cesses. In ICDM'14, pages 140-149, 2014.","title":"A collaborative kalman filter for time-evolving dyadic processes","authors":"Paisley Gultekin, San Gultekin, John Paisley","year":"2014","venue":"ICDM'14","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[He et al., 2016] Xiangnan He, Hanwang Zhang, Min-Yen Kan, and Tat-Seng Chua. Fast matrix factorization for on- line recommendation with implicit feedback. In SIGIR'16, pages 549-558, 2016.","title":"Fast matrix factorization for online recommendation with implicit feedback","authors":"He","year":"2016","venue":"SIGIR'16","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Hosseini et al., 2017] Seyed Abbas Hosseini, Keivan Al- izadeh, Ali Khodadadi, Ali Arabzadeh, Mehrdad Fara- jtabar, Hongyuan Zha, and Hamid R Rabiee. Recurrent poisson factorization for temporal recommendation. In KDD'17, pages 847-855, 2017.","title":"Recurrent poisson factorization for temporal recommendation","authors":"Abbas Seyed, Keivan Hosseini, Ali Alizadeh, Ali Khodadadi, Mehrdad Arabzadeh, Hongyuan Farajtabar, Zha, Hamid R Rabiee","year":"2017","venue":"KDD'17","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Hu et al., 2014] Longke Hu, Aixin Sun, and Yong Liu. Your neighbors affect your ratings: on geographical neighbor- hood influence to rating prediction. In SIGIR'14, pages 345-354, 2014.","title":"Your neighbors affect your ratings: on geographical neighborhood influence to rating prediction","authors":"Hu","year":"2014","venue":"SIGIR'14","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Jaakkola and Jordan, 1997] T Jaakkola and M Jordan. A variational approach to bayesian logistic regression mod- els and their extensions. In Sixth International Workshop on Artificial Intelligence and Statistics, 1997.","title":"A variational approach to bayesian logistic regression models and their extensions","authors":"T Jaakkola, M Jordan","year":"1997","venue":"Sixth International Workshop on Artificial Intelligence and Statistics","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Johnson, 2014] Christopher C Johnson. Logistic matrix fac- torization for implicit feedback data. In NIPS 2014 Work- shop on Distributed Machine Learning and Matrix Com- putations, 2014.","title":"Logistic matrix factorization for implicit feedback data","authors":"Johnson Christopher","year":"2014","venue":"NIPS 2014 Workshop on Distributed Machine Learning and Matrix Computations","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Koren, 2009] Yehuda Koren. Collaborative filtering with temporal dynamics. In KDD'09, pages 89-97, 2009.","title":"Collaborative filtering with temporal dynamics","authors":"Koren ;, Yehuda Koren","year":"2009","venue":"KDD'09","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Lian et al., 2016] Defu Lian, Zhenyu Zhang, Yong Ge, Fuzheng Zhang, Nicholas Jing Yuan, and Xing Xie. Regu- larized content-aware tensor factorization meets temporal- aware location recommendation. In ICDM'16, pages 1029-1034, 2016.","title":"Regularized content-aware tensor factorization meets temporalaware location recommendation","authors":"Defu Lian, Zhenyu Zhang, Yong Ge, Fuzheng Zhang, Nicholas Jing Yuan, Xing Xie","year":"2016","venue":"ICDM'16","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Liu et al., 2010] Nathan N Liu, Min Zhao, Evan Xiang, and Qiang Yang. Online evolutionary collaborative filtering. In RecSyS'10, pages 95-102, 2010.","title":"Online evolutionary collaborative filtering","authors":"Min Nathan N Liu, Evan Zhao, Qiang Xiang, Yang","year":"2010","venue":"RecSyS'10","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Liu et al., 2014] Yong Liu, Wei Wei, Aixin Sun, and Chun- yan Miao. Exploiting geographical neighborhood charac- teristics for location recommendation. In CIKM'14, pages 739-748, 2014.","title":"Exploiting geographical neighborhood characteristics for location recommendation","authors":"Yong Liu, Wei Wei, Aixin Sun, Chunyan Miao","year":"2014","venue":"CIKM'14","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Liu et al., 2015] Yong Liu, Peilin Zhao, Aixin Sun, and Chunyan Miao. A boosting algorithm for item recommen- dation with implicit feedback. In IJCAI'15, pages 1792- 1798, 2015.","title":"A boosting algorithm for item recommendation with implicit feedback","authors":"Yong Liu, Peilin Zhao, Aixin Sun, Chunyan Miao","year":"2015","venue":"IJCAI'15","doi":null,"url":null,"internal_url":"/proceedings/2015/255"},{"position":15,"raw":"[Liu et al., 2016] Yong Liu, Min Wu, Chunyan Miao, Peilin Zhao, and Xiao-Li Li. Neighborhood regularized logistic matrix factorization for drug-target interaction prediction. PLoS Computational Biology, 12(2):e1004760, 2016.","title":"Neighborhood regularized logistic matrix factorization for drug-target interaction prediction","authors":"Yong Liu, Min Wu, Chunyan Miao, Peilin Zhao, Xiao-Li Li","year":"2016","venue":"PLoS Computational Biology","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Liu et al., 2017] Yong Liu, Peilin Zhao, Xin Liu, Min Wu, and Xiao-Li Li. Learning user dependencies for recom- mendation. In IJCAI'17, pages 2379-2385, 2017.","title":"Learning user dependencies for recommendation","authors":"Yong Liu, Peilin Zhao, Xin Liu, Min Wu, Xiao-Li Li","year":"2017","venue":"IJCAI'17","doi":null,"url":null,"internal_url":"/proceedings/2017/331"},{"position":17,"raw":"[Liu, 2015] Xin Liu. Modeling users' dynamic preference for personalized recommendation. In IJCAI'15, pages 1785-1791, 2015.","title":"Modeling users' dynamic preference for personalized recommendation","authors":"Xin Liu","year":"2015","venue":"IJCAI'15","doi":null,"url":null,"internal_url":"/proceedings/2015/254"},{"position":18,"raw":"[Pan and Chen, 2013] Weike Pan and Li Chen. Gbpr: Group preference based bayesian personalized ranking for one- class collaborative filtering. In IJCAI'13, pages 2691- 2697, 2013.","title":"Gbpr: Group preference based bayesian personalized ranking for oneclass collaborative filtering","authors":"Chen ; ] Weike Pan, Li Pan, Chen","year":"2013","venue":"IJCAI'13","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'09, 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":"UAI'09","doi":null,"url":null,"internal_url":null},{"position":20,"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, 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","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Wang et al., 2016] Yichen Wang, Nan Du, Rakshit Trivedi, and Le Song. Coevolutionary latent feature processes for continuous-time user-item interactions. In NIPS'16, pages 4547-4555, 2016.","title":"Coevolutionary latent feature processes for continuous-time user-item interactions","authors":"Yichen Wang, Nan Du, Rakshit Trivedi, Le Song","year":"2016","venue":"NIPS'16","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Wang et al., 2017] Yixin Wang, Alp Kucukelbir, and David M Blei. Robust probabilistic modeling with bayesian data reweighting. In ICML'17, pages 3646-3655, 2017.","title":"Robust probabilistic modeling with bayesian data reweighting","authors":"Yixin Wang, Alp Kucukelbir, David M Blei","year":"2017","venue":"ICML'17","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Xiong et al., 2010] Liang Xiong, Xi Chen, Tzu-Kuo Huang, Jeff Schneider, and Jaime G Carbonell. Temporal collab- orative filtering with bayesian probabilistic tensor factor- ization. In SDM'10, pages 211-222, 2010.","title":"Temporal collaborative filtering with bayesian probabilistic tensor factorization","authors":"Liang Xiong, Xi Chen, Tzu-Kuo Huang, Jeff Schneider, Jaime G Carbonell","year":"2010","venue":"SDM'10","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Yu et al., 2016] Hsiang-Fu Yu, Nikhil Rao, and Inderjit S Dhillon. Temporal regularized matrix factorization for high-dimensional time series prediction. In NIPS'16, pages 847-855, 2016.","title":"Temporal regularized matrix factorization for high-dimensional time series prediction","authors":"Hsiang-Fu Yu, Nikhil Rao, Inderjit S Dhillon","year":"2016","venue":"NIPS'16","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}