One-class collaborative filtering based on rating prediction and ranking prediction

One-Class Collaborative Filtering (OCCF) has recently received much attention in recommendation communities due to their close relationship with real industry problem settings. However, the problem with previous research studies on OCCF is that they focused on either rating prediction or ranking prediction, but no concerted research effort has been devoted to developing a recommendation approach that simultaneously optimizes both the ratings and rank of the recommended items. In order to overcome the defects of prior research, a new better unified OCCF approach (UOCCF) based on the newest Collaborative Less-is-More Filtering (CLiMF) approach and the Probabilistic Matrix Factorization (PMF) approach was proposed, which benefits from the ranking-oriented perspective and the rating-oriented perspective by sharing common latent features of users and items in CLiMF and PMF. We also provide an efficient learning algorithm to solve the optimization problem for UOCCF. Experimental results on practical datasets showed that our proposed UOCCF approach outperformed existing OCCF approaches (both ranking-oriented and rating-oriented) over different evaluation metrics, and that the UOCCF approach enjoys the advantage of low complexity and is shown to be linear with the number of observed ratings in a given useritem rating matrix. Because of its high precision and good expansibility, UOCCF is suitable for processing big data, and has wide application prospects in the field of internet information recommendation.

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

[2]  Xinge You,et al.  A Blind Watermarking Scheme Using New Nontensor Product Wavelet Filter Banks , 2010, IEEE Transactions on Image Processing.

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

[4]  Tie-Yan Liu,et al.  Learning to Rank for Information Retrieval , 2011 .

[5]  Jianying Hu,et al.  One-Class Matrix Completion with Low-Density Factorizations , 2010, 2010 IEEE International Conference on Data Mining.

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

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

[8]  Qinmu Peng,et al.  Segmentation of retinal blood vessels using the radial projection and semi-supervised approach , 2011, Pattern Recognit..

[9]  Ruslan Salakhutdinov,et al.  Probabilistic Matrix Factorization , 2007, NIPS.

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

[11]  George Karypis,et al.  Item-based top-N recommendation algorithms , 2004, TOIS.

[12]  George Karypis,et al.  SLIM: Sparse Linear Methods for Top-N Recommender Systems , 2011, 2011 IEEE 11th International Conference on Data Mining.

[13]  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 .

[14]  Tie-Yan Liu,et al.  Learning to rank for information retrieval , 2009, SIGIR.

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

[16]  Domonkos Tikk,et al.  Applications of the conjugate gradient method for implicit feedback collaborative filtering , 2011, RecSys '11.

[17]  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.

[18]  Yehuda Koren,et al.  Factor in the neighbors: Scalable and accurate collaborative filtering , 2010, TKDD.

[19]  Yuan Yan Tang,et al.  Facial Biometrics Using Nontensor Product Wavelet and 2D Discriminant Techniques , 2009, Int. J. Pattern Recognit. Artif. Intell..

[20]  Nathan Srebro,et al.  Fast maximum margin matrix factorization for collaborative prediction , 2005, ICML.

[21]  Michael Jahrer,et al.  Collaborative Filtering Ensemble for Ranking , 2012, KDD Cup.

[22]  Xinge You,et al.  A method using long digital straight segments for fingerprint recognition , 2012, Neurocomputing.

[23]  Rong Pan,et al.  Mind the gaps: weighting the unknown in large-scale one-class collaborative filtering , 2009, KDD.

[24]  Martha Larson,et al.  Unifying rating-oriented and ranking-oriented collaborative filtering for improved recommendation , 2013, Inf. Sci..

[25]  Qiang Chen,et al.  Exploiting Explicit and Implicit Feedback for Personalized Ranking , 2016 .

[26]  Geoffrey E. Hinton,et al.  Restricted Boltzmann machines for collaborative filtering , 2007, ICML '07.

[27]  Lior Rokach,et al.  Introduction to Recommender Systems Handbook , 2011, Recommender Systems Handbook.

[28]  Domonkos Tikk,et al.  Fast als-based matrix factorization for explicit and implicit feedback datasets , 2010, RecSys '10.

[29]  Lior Rokach,et al.  Recommender Systems Handbook , 2010 .

[30]  Min Zhao,et al.  Probabilistic latent preference analysis for collaborative filtering , 2009, CIKM.

[31]  Lars Schmidt-Thieme,et al.  Multi-relational matrix factorization using bayesian personalized ranking for social network data , 2012, WSDM '12.

[32]  Alan Hanjalic,et al.  List-wise learning to rank with matrix factorization for collaborative filtering , 2010, RecSys '10.

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

[34]  Martha Larson,et al.  xCLiMF: optimizing expected reciprocal rank for data with multiple levels of relevance , 2013, RecSys.

[35]  Yan Liu,et al.  Collaborative Topic Regression with Social Matrix Factorization for Recommendation Systems , 2012, ICML.

[36]  Chong Wang,et al.  Collaborative topic modeling for recommending scientific articles , 2011, KDD.

[37]  Weihua Ou,et al.  Robust Personalized Ranking from Implicit Feedback , 2016, Int. J. Pattern Recognit. Artif. Intell..

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