Cost-sensitive three-way recommendations by learning pair-wise preferences

Recommender systems aim to identify items that a user may like. In this paper, we discuss a three-way decision approach which provides a more meaningful way to recommend items to a user. Besides recommended items and not recommended items, the proposed model adds a set of items that are possibly recommended to users. In the model, we focus on two issues. One is the computation of required thresholds to define the three sets based on the decision-theoretic rough set model. The other is the notion of user preference on the three sets which forms the basis of a ranking strategy, and then a pair-wise preference learning algorithm using gradient descent is adopted for inferring latent vectors for users and items. Working with a sigmoid function of a product of a user and item latent vector, we estimate the probability that the user prefers the item to make recommendations. Experimental results show that the proposed method improves recommendation quality from the cost-sensitive view. The computation of required thresholds to define the three sets based on the decision-theoretic rough set model.The notion of user preference on the three sets which forms the basis of a ranking strategy.A pair-wise learning algorithm to estimate the probability of the user liking the item to make recommendations.

[1]  Fan Min,et al.  Three-way recommender systems based on random forests , 2016, Knowl. Based Syst..

[2]  Gediminas Adomavicius,et al.  Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques , 2012, IEEE Transactions on Knowledge and Data Engineering.

[3]  Patrick Seemann,et al.  Matrix Factorization Techniques for Recommender Systems , 2014 .

[4]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[5]  Jiajin Huang,et al.  Using Market Value Functions for Targeted Marketing Data Mining , 2002, Int. J. Pattern Recognit. Artif. Intell..

[6]  R. M. Adelson,et al.  Utility Theory for Decision Making , 1971 .

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

[8]  Richang Hong,et al.  Point-of-Interest Recommendations: Learning Potential Check-ins from Friends , 2016, KDD.

[9]  Michael R. Lyu,et al.  Probabilistic factor models for web site recommendation , 2011, SIGIR.

[10]  Nouman Azam,et al.  Game-theoretic rough sets for recommender systems , 2014, Knowl. Based Syst..

[11]  F. Maxwell Harper,et al.  The MovieLens Datasets: History and Context , 2016, TIIS.

[12]  Yiyu Yao,et al.  Decision-Theoretic Rough Set Models , 2007, RSKT.

[13]  Decui Liang,et al.  Incorporating logistic regression to decision-theoretic rough sets for classifications , 2014, Int. J. Approx. Reason..

[14]  Hai Thanh Nguyen,et al.  TBPR: Trinity Preference based Bayesian Personalized Ranking for Multivariate Implicit Feedback , 2016, UMAP.

[15]  M. Kane.  Measurement theory. , 1980, NLN publications.

[16]  Dun Liu,et al.  A Multiple-category Classification Approach with Decision-theoretic Rough Sets , 2012, Fundam. Informaticae.

[17]  Arkadiusz Paterek,et al.  Improving regularized singular value decomposition for collaborative filtering , 2007 .

[18]  Stephen E. Robertson,et al.  Probabilistic relevance ranking for collaborative filtering , 2008, Information Retrieval.

[19]  Ling-Hwei Chen,et al.  A New Method for Extracting Primitives of Regular Textures Based on Wavelet Transform , 2002, Int. J. Pattern Recognit. Artif. Intell..

[20]  Yiyu Yao,et al.  Three-way decisions with probabilistic rough sets , 2010, Inf. Sci..

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

[22]  Yiyu Yao,et al.  A UNIFIED FRAMEWORK OF TARGETED MARKETING USING CUSTOMER PREFERENCES , 2014, Comput. Intell..

[23]  Congfu Xu,et al.  Adaptive Bayesian personalized ranking for heterogeneous implicit feedbacks , 2015, Knowl. Based Syst..

[24]  Chengqi Zhang,et al.  An information filtering model on the Web and its application in JobAgent , 2000, Knowl. Based Syst..

[25]  H. Robbins.  A Stochastic Approximation Method , 1951 .

[26]  Tong Zhao,et al.  Leveraging Social Connections to Improve Personalized Ranking for Collaborative Filtering , 2014, CIKM.

[27]  Peter C. Fishburn,et al.  Utility theory for decision making , 1970 .

[28]  Xiuyi Jia,et al.  How to Evaluate Three-Way Decisions Based Binary Classification? , 2015, RSFDGrC.

[29]  Decui Liang,et al.  A Novel Risk Decision Making Based on Decision-Theoretic Rough Sets Under Hesitant Fuzzy Information , 2015, IEEE Transactions on Fuzzy Systems.

[30]  Yiyu Yao,et al.  Cost-sensitive three-way email spam filtering , 2013, Journal of Intelligent Information Systems.

[31]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.

[32]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[33]  Yiyu Yao,et al.  The superiority of three-way decisions in probabilistic rough set models , 2011, Inf. Sci..