A dynamic collaborative filtering system via a weighted clustering approach

A collaborative filtering system (CF) aims at filtering huge amount of information, in order to guide users of web applications towards items that might interest them. Such a system, consists in recommending a set of personalized items for an active user, according to the preferences of other similar users. Existing methods, such as memory and Matrix Factorization (MF) approaches can achieve very good recommendation accuracy, unfortunately they are computationally very expensive. Applying such approaches to real-world applications in which users, items and ratings are frequently updated remains therefore a challenge. To address this problem, we propose a novel efficient incremental CF system, based on a weighted clustering approach. Our system is designed to provide a high quality of recommendations with a very low computation cost. In contrast to existing incremental methods, the complexity of our approach does not depend on the number of users and items. Our CF system is therefore suitable for dynamic settings, involving huge databases, in which available information evolves rapidly (i.e, submission of new ratings, update of existing ratings, appearance of new users and new items). Numerical experiments, conducted on several real-world datasets, confirm the efficiency and the effectiveness of our method, by demonstrating that it is significantly better than existing incremental CF methods in terms of both scalability and recommendation quality.

[1]  Robert Tibshirani,et al.  Spectral Regularization Algorithms for Learning Large Incomplete Matrices , 2010, J. Mach. Learn. Res..

[2]  Jing Wang,et al.  Feature subspace transfer for collaborative filtering , 2014, Neurocomputing.

[3]  Trevor J. Hastie,et al.  Matrix completion and low-rank SVD via fast alternating least squares , 2014, J. Mach. Learn. Res..

[4]  John Riedl,et al.  Application of Dimensionality Reduction in Recommender System - A Case Study , 2000 .

[5]  LindenGreg,et al.  Amazon.com Recommendations , 2003 .

[6]  Chao Liu,et al.  Recommender systems with social regularization , 2011, WSDM '11.

[7]  Michael J. Pazzani,et al.  Content-Based Recommendation Systems , 2007, The Adaptive Web.

[8]  G. James Blaine,et al.  Continuous Monitoring of Physiologic Variables with a Dedicated Minicomputer , 1975, Computer.

[9]  Gérard Govaert,et al.  Co-Clustering: Models, Algorithms and Applications , 2013 .

[10]  Richard S. Zemel,et al.  Collaborative Filtering and the Missing at Random Assumption , 2007, UAI.

[11]  Fillia Makedon,et al.  Learning from Incomplete Ratings Using Non-negative Matrix Factorization , 2006, SDM.

[12]  Seungjin Choi,et al.  Bayesian binomial mixture model for collaborative prediction with non-random missing data , 2014, RecSys '14.

[13]  William Nick Street,et al.  Incremental collaborative filtering via evolutionary co-clustering , 2010, RecSys '10.

[14]  Dimitris Plexousakis,et al.  Incremental Collaborative Filtering for Highly-Scalable Recommendation Algorithms , 2005, ISMIS.

[15]  Shi Zhong,et al.  Efficient online spherical k-means clustering , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[16]  Srujana Merugu,et al.  A scalable collaborative filtering framework based on co-clustering , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[17]  BurkeRobin.  Hybrid Recommender Systems , 2002 .

[18]  Hideo Aizaki,et al.  Basic Functions for Supporting an Implementation of Choice Experiments in R , 2012 .

[19]  Huan Liu,et al.  Content-Aware Point of Interest Recommendation on Location-Based Social Networks , 2015, AAAI.

[20]  Julien Delporte,et al.  Socially Enabled Preference Learning from Implicit Feedback Data , 2013, ECML/PKDD.

[21]  Aleksandra Mojsilovic,et al.  A Family of Non-negative Matrix Factorizations for One-Class Collaborative Filtering Problems , 2009 .

[22]  Ke Wang,et al.  Scalable collaborative filtering using incremental update and local link prediction , 2012, CIKM.

[23]  Xin Liu,et al.  Document clustering based on non-negative matrix factorization , 2003, SIGIR.

[24]  Chris H. Q. Ding,et al.  Collaborative Filtering: Weighted Nonnegative Matrix Factorization Incorporating User and Item Graphs , 2010, SDM.

[25]  Juan C. Burguillo,et al.  A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition , 2010, Inf. Sci..

[26]  Yu He,et al.  The YouTube video recommendation system , 2010, RecSys '10.

[27]  Gérard Govaert,et al.  An EM algorithm for the block mixture model , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Greg Linden,et al.  Amazon . com Recommendations Item-to-Item Collaborative Filtering , 2001 .

[29]  Hyung Jun Ahn,et al.  A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem , 2008, Inf. Sci..

[30]  Gérard Govaert,et al.  Clustering with block mixture models , 2003, Pattern Recognit..

[31]  Huan Liu,et al.  Social recommendation: a review , 2013, Social Network Analysis and Mining.

[32]  J. Bobadilla,et al.  Recommender systems survey , 2013, Knowl. Based Syst..

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

[34]  Kurt Hornik,et al.  Spherical k-Means Clustering , 2012 .

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

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

[37]  Robin D. Burke,et al.  Hybrid Recommender Systems: Survey and Experiments , 2002, User Modeling and User-Adapted Interaction.

[38]  Qiang Yang,et al.  Can Movies and Books Collaborate? Cross-Domain Collaborative Filtering for Sparsity Reduction , 2009, IJCAI.

[39]  Yujiu Yang,et al.  Incremental Learning for Dynamic Collaborative Filtering , 2011, J. Softw..

[40]  Inderjit S. Dhillon,et al.  Clustering on the Unit Hypersphere using von Mises-Fisher Distributions , 2005, J. Mach. Learn. Res..

[41]  Hui Tian,et al.  A new user similarity model to improve the accuracy of collaborative filtering , 2014, Knowl. Based Syst..

[42]  Yehuda Koren,et al.  The BellKor Solution to the Netflix Grand Prize , 2009 .

[43]  D. Rubin,et al.  Statistical Analysis with Missing Data. , 1989 .

[44]  Inderjit S. Dhillon,et al.  Concept Decompositions for Large Sparse Text Data Using Clustering , 2004, Machine Learning.