A Relaxed Ranking-Based Factor Model for Recommender System from Implicit Feedback

Implicit feedback based recommendation has recently been an important task with the accumulated user-item interaction data. However, it is very challenging to produce recommendations from implicit feedback due to the sparseness of data and the lack of negative feedback/rating. Although various factor models have been proposed to tackle this problem, they either focus on rating prediction that may lead to inaccurate top-k recommendations or are dependent on the sampling of negative feedback that often results in bias. To this end, we propose a Relaxed Ranking-based Factor Model, RRFM, to relax pairwise ranking into a SVM-like task, where positive and negative feedbacks are separated by the soft boundaries, and their non-separate property is employed to capture the characteristic of unobserved data. A smooth and scalable algorithm is developed to solve group- and instance- level's optimization and parameter estimation. Extensive experiments based on real-world datasets demonstrate the effectiveness and advantage of our approach.

[1]  Yifan Hu,et al.  Collaborative Filtering for Implicit Feedback Datasets , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[2]  Martha Larson,et al.  CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering , 2012, RecSys.

[3]  Alfred Kobsa,et al.  Proceedings of the 8th ACM Conference on Recommender systems , 2014, RecSys 2014.

[4]  Alexander J. Smola,et al.  Maximum Margin Matrix Factorization for Collaborative Ranking , 2007 .

[5]  Siyuan Liu,et al.  Structured Learning from Heterogeneous Behavior for Social Identity Linkage , 2015, IEEE Transactions on Knowledge and Data Engineering.

[6]  Jacob W. Crandall,et al.  Twenty-Ninth AAAI Conference on Artificial Intelligence , 2015, AAAI 2015.

[7]  Olvi L. Mangasarian,et al.  Smoothing methods for convex inequalities and linear complementarity problems , 1995, Math. Program..

[8]  Stephen E. Robertson,et al.  SoftRank: optimizing non-smooth rank metrics , 2008, WSDM '08.

[9]  Huayu Li,et al.  Point-of-Interest Recommender Systems: A Separate-Space Perspective , 2015, 2015 IEEE International Conference on Data Mining.

[10]  Lars Schmidt-Thieme,et al.  BPR: Bayesian Personalized Ranking from Implicit Feedback , 2009, UAI.

[11]  Jason Weston,et al.  Learning to rank recommendations with the k-order statistic loss , 2013, RecSys.

[12]  Stéphan Clémençon,et al.  Collaborative Filtering with Localised Ranking , 2015, AAAI.

[13]  Michael J. Todd,et al.  Mathematical programming , 2004, Handbook of Discrete and Computational Geometry, 2nd Ed..

[14]  Ramayya Krishnan,et al.  HYDRA: large-scale social identity linkage via heterogeneous behavior modeling , 2014, SIGMOD Conference.

[15]  Gert R. G. Lanckriet,et al.  Top-N Recommendation with Missing Implicit Feedback , 2015, RecSys.

[16]  Thorsten Joachims,et al.  Accurately interpreting clickthrough data as implicit feedback , 2005, SIGIR '05.

[17]  Martha Larson,et al.  CLiMF: Collaborative Less-Is-More Filtering , 2013, IJCAI.

[18]  Li Chen,et al.  Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence GBPR: Group Preference Based Bayesian Personalized Ranking for One-Class Collaborative Filtering , 2022 .

[19]  Jin Zhang,et al.  Preference Completion: Large-scale Collaborative Ranking from Pairwise Comparisons , 2015, ICML.

[20]  Qiang Yang,et al.  EigenRank: a ranking-oriented approach to collaborative filtering , 2008, SIGIR '08.

[21]  Qiang Yang,et al.  One-Class Collaborative Filtering , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[22]  Martha Larson,et al.  TFMAP: optimizing MAP for top-n context-aware recommendation , 2012, SIGIR '12.

[23]  Shuang-Hong Yang,et al.  Collaborative competitive filtering: learning recommender using context of user choice , 2011, SIGIR.

[24]  Domonkos Tikk,et al.  Alternating least squares for personalized ranking , 2012, RecSys.

[25]  Thomas G. Dietterich,et al.  In Advances in Neural Information Processing Systems 12 , 1991, NIPS 1991.

[26]  Yuh-Jye Lee,et al.  SSVM: A Smooth Support Vector Machine for Classification , 2001, Comput. Optim. Appl..

[27]  Yehuda Koren,et al.  Matrix Factorization Techniques for Recommender Systems , 2009, Computer.

[28]  Robert Tibshirani,et al.  1-norm Support Vector Machines , 2003, NIPS.

[29]  Suhrid Balakrishnan,et al.  Collaborative ranking , 2012, WSDM '12.