{"type":"ScholarlyArticle","title":"Improving Implicit Recommender Systems with View Data","authors":[{"name":"Jingtao Ding","slug":"jingtao-ding","given":"Jingtao","family":"Ding","affiliation":"Beijing National Research Center for Information Science and Technology (BNRist) ,"},{"name":"Guanghui Yu","slug":"guanghui-yu","given":"Guanghui","family":"Yu","affiliation":"Beijing National Research Center for Information Science and Technology (BNRist) ,"},{"name":"Xiangnan He","slug":"xiangnan-he","given":"Xiangnan","family":"He","affiliation":"School of Computing , National University of Singapore"},{"name":"Yuhan Quan","slug":"yuhan-quan","given":"Yuhan","family":"Quan","affiliation":"Beijing National Research Center for Information Science and Technology (BNRist) ,"},{"name":"Yong Li","slug":"yong-li","given":"Yong","family":"Li","affiliation":"Beijing National Research Center for Information Science and Technology (BNRist) ,"},{"name":"Tat-Seng Chua","slug":"tat-seng-chua","given":"Tat-Seng","family":"Chua","affiliation":"School of Computing , National University of Singapore"},{"name":"Depeng Jin","slug":"depeng-jin","given":"Depeng","family":"Jin","affiliation":"Beijing National Research Center for Information Science and Technology (BNRist) ,; Department of Electronic Engineering , Tsinghua University"},{"name":"Jiajie Yu","slug":"jiajie-yu","given":"Jiajie","family":"Yu","affiliation":"Beibei Inc"}],"abstract":"Most existing recommender systems leverage the primary feedback data only, such as the purchase records in E-commerce. In this work, we additionally integrate view data into implicit feedback based recommender systems (dubbed as Implicit Recommender Systems). We propose to model the pairwise ranking relations among purchased, viewed, and non-viewed interactions, being more effective and flexible than typical pointwise matrix factorization (MF) methods. However, such a pairwise formulation poses efficiency challenges in learning the model. To address this problem, we design a new learning algorithm based on the element-wise Alternating Least Squares (eALS) learner. Notably, our algorithm can efficiently learn model parameters from the whole user-item matrix (including all missing data), with a rather low time complexity that is dependent on the observed data only. Extensive experiments on two real-world datasets demonstrate that our method outperforms several state-of-the-art MF methods by 10% ∼ 28.4%. Our implementation is available at: https://github.com/ dingjingtao/View_enhanced_ALS.","doi":"10.24963/ijcai.2018/464","url":"https://ijcai.org/proceedings/2018/464","pdf":"https://ijcai.org/proceedings/2018/0464.pdf","markdown":"https://ijcai.org/proceedings/2018/464.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/464","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":464,"pages":{"first":3343,"last":3349},"keywords":["Machine Learning: Recommender Systems","Machine Learning: Learning Preferences or Rankings"],"videos":[],"references":[{"position":1,"raw":"[Agarwal et al., 2012] Deepak Agarwal, Bee-Chung Chen, and Xu- anhui Wang. Multi-faceted ranking of news articles using post- read actions. In CIKM, pages 694-703, 2012.","title":"Multi-faceted ranking of news articles using postread actions","authors":"Deepak Agarwal, Bee-Chung Chen, Xuanhui Wang","year":"2012","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bayer et al., 2017] Immanuel Bayer, Xiangnan He, Bhargav Kana- gal, and Steffen Rendle. A generic coordinate descent framework for learning from implicit feedback. In WWW, pages 1341-1350, 2017.","title":"A generic coordinate descent framework for learning from implicit feedback","authors":"Bayer","year":"2017","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Cao et al., 2017] Cheng Cao, Hancheng Ge, Haokai Lu, Xia Hu, and James Caverlee. What are you known for?: Learning user topical profiles with implicit and explicit footprints. In SIGIR, pages 743-752. ACM, 2017.","title":"What are you known for?: Learning user topical profiles with implicit and explicit footprints","authors":"Cheng Cao, Hancheng Ge, Haokai Lu, Xia Hu, James Caverlee","year":"2017","venue":"SIGIR","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Cheng et al., 2014] Jian Cheng, Ting Yuan, Jinqiao Wang, and Hanqing Lu. Group latent factor model for recommendation with multiple user behaviors. In SIGIR, pages 995-998, 2014.","title":"Group latent factor model for recommendation with multiple user behaviors","authors":"Jian Cheng, Ting Yuan, Jinqiao Wang, Hanqing Lu","year":"2014","venue":"SIGIR","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Covington et al., 2016] Paul Covington, Jay Adams, and Emre Sargin. Deep neural networks for youtube recommendations. In RecSys, pages 191-198, 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":6,"raw":"[Ding et al., 2018] 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 WWW, pages 13-14, 2018.","title":"An improved sampler for bayesian personalized ranking by leveraging view data","authors":"Jingtao Ding, Fuli Feng, Xiangnan He, Guanghui Yu, Yong Li, Depeng Jin","year":"2018","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[He and Chua, 2017] Xiangnan He and Tat-Seng Chua. Neural fac- torization machines for sparse predictive analytics. In SIGIR, pages 355-364, 2017.","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":8,"raw":"[He et al., 2016] Xiangnan He, Hanwang Zhang, Min-Yen Kan, and Tat-Seng Chua. Fast matrix factorization for online rec- ommendation with implicit feedback. In SIGIR, pages 549-558, 2016.","title":"Fast matrix factorization for online recommendation with implicit feedback","authors":"He","year":"2016","venue":"SIGIR","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Hong et al., 2013] Liangjie Hong, Aziz S Doumith, and Brian D Davison. Co-factorization machines: modeling user interests and predicting individual decisions in twitter. In WSDM, pages 557- 566, 2013.","title":"Co-factorization machines: modeling user interests and predicting individual decisions in twitter","authors":"Hong","year":"2013","venue":"WSDM","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Koren, 2010] Yehuda Koren. Collaborative filtering with temporal dynamics. Communications of the ACM, 53(4):89-97, 2010.","title":"Collaborative filtering with temporal dynamics","authors":"Koren ;, Yehuda Koren","year":"2010","venue":"Communications of the ACM","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Krohn-Grimberghe et al., 2012] Artus Krohn-Grimberghe, Lucas Drumond, Christoph Freudenthaler, and Lars Schmidt-Thieme. Multi-relational matrix factorization using bayesian personalized ranking for social network data. In WSDM, pages 173-182, 2012.","title":"Multi-relational matrix factorization using bayesian personalized ranking for social network data","authors":"Artus Krohn-Grimberghe, Lucas Drumond, Christoph Freudenthaler, Lars Schmidt-Thieme","year":"2012","venue":"WSDM","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Lerche and Jannach, 2014] Lukas Lerche and Dietmar Jannach. Using graded implicit feedback for bayesian personalized rank- ing. In RecSys, pages 353-356, 2014.","title":"Using graded implicit feedback for bayesian personalized ranking","authors":"Lukas Lerche, Dietmar Jannach","year":"2014","venue":"RecSys","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Liu et al., 2016] An-An Liu, Wei-Zhi Nie, Yue Gao, and Yu-Ting Su. Multi-modal clique-graph matching for view-based 3d model retrieval. IEEE Transactions on Image Processing, 25(5):2103- 2116, 2016.","title":"Multi-modal clique-graph matching for view-based 3d model retrieval","authors":"Liu","year":"2016","venue":"IEEE Transactions on Image Processing","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 recommendation via inte- grating multiple feedbacks. In PAKDD, pages 131-143, 2017.","title":"Personalized ranking recommendation via integrating multiple feedbacks","authors":"Jian Liu, Chuan Shi, Binbin Hu, Shenghua Liu, S Yu, Philip","year":"2017","venue":"PAKDD","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 RecSys, pages 361-364, 2016.","title":"Bayesian personalized ranking with multichannel user feedback","authors":"Babak Loni, Roberto Pagano, Martha Larson, Alan Hanjalic","year":"2016","venue":"RecSys","doi":null,"url":null,"internal_url":null},{"position":16,"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 ICDM, 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":"ICDM","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Pan et al., 2015] Weike Pan, Hao Zhong, Congfu Xu, and Zhong Ming. Adaptive bayesian personalized ranking for heteroge- neous implicit feedbacks. Knowledge-Based Systems, 73:173- 180, 2015.","title":"Adaptive bayesian personalized ranking for heterogeneous implicit feedbacks","authors":"Weike Pan, Hao Zhong, Congfu Xu, Zhong Ming","year":"2015","venue":"Knowledge-Based Systems","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Rendle et al., 2009] Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. Bpr: Bayesian person- alized ranking from implicit feedback. In UAI, 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","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Singh and Gordon, 2008] Ajit P Singh and Geoffrey J Gordon. Re- lational learning via collective matrix factorization. In KDD, pages 650-658, 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":20,"raw":"[Takács and Tikk, 2012] Gábor Takács and Domonkos Tikk. Alter- nating least squares for personalized ranking. In RecSys, pages 83-90, 2012.","title":"Alternating least squares for personalized ranking","authors":"Tikk Takács, Gábor Takács, Domonkos Tikk","year":"2012","venue":"RecSys","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Tang et al., 2016] Liang Tang, Bo Long, Bee-Chung Chen, and Deepak Agarwal. An empirical study on recommendation with multiple types of feedback. In KDD, pages 283-292, 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":"KDD","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Yuan et al., 2014] Ting Yuan, Jian Cheng, Xi Zhang, Shuang Qiu, Hanqing Lu, et al. Recommendation by mining multiple user behaviors with group sparsity. In AAAI, pages 222-228, 2014.","title":"Recommendation by mining multiple user behaviors with group sparsity","authors":"Ting Yuan, Jian Cheng, Xi Zhang, Shuang Qiu, Hanqing Lu","year":"2014","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Zhang et al., 2017] Yan Zhang, Defu Lian, and Guowu Yang. Dis- crete personalized ranking for fast collaborative filtering from im- plicit feedback. In AAAI, pages 1669-1675, 2017.","title":"Discrete personalized ranking for fast collaborative filtering from implicit feedback","authors":"Yan Zhang, Defu Lian, Guowu Yang","year":"2017","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Zhao et al., 2015a] Tong Zhao, Julian McAuley, and Irwin King. Improving latent factor models via personalized feature projec- tion for one class recommendation. In CIKM, pages 821-830, 2015.","title":"Improving latent factor models via personalized feature projection for one class recommendation","authors":"Zhao","year":"2015","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Zhao et al., 2015b] Zhe Zhao, Zhiyuan Cheng, Lichan Hong, and Ed H Chi. Improving user topic interest profiles by behavior fac- torization. In WWW, pages 1406-1416, 2015.","title":"Improving user topic interest profiles by behavior factorization","authors":"Zhao","year":"2015","venue":"WWW","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}