Matrix Factorization Model in Collaborative Filtering Algorithms: A Survey

Abstract Recommendation Systems (RSs) are becoming tools of choice to select the online information relevant to a given user. Collaborative Filtering (CF) is the most popular approach to build Recommendation System and has been successfully employed in many applications. Collaborative Filtering algorithms are much explored technique in the field of Data Mining and Information Retrieval. In CF, past user behavior are analyzed in order to establish connections between users and items to recommend an item to a user based on opinions of other users. Those customers, who had similar likings in the past, will have similar likings in the future. In the past decades due to the rapid growth of Internet usage, vast amount of data is generated and it has becomea challenge for CF algorithms. So, CF faces issues with sparsity of rating matrix and growing nature of data. These challenges are well taken care of by Matrix Factorization (MF). In this paper we are going to discuss different Matrix Factorization models such as Singular Value Decomposition (SVD), Principal Component Analysis (PCA) and Probabilistic Matrix Factorization (PMF). This paper attempts to present a comprehensive survey of MF model like SVD to address the challenges of CF algorithms, which can be served as a roadmap for research and practice in this area.

[1]  Martin Ester,et al.  A matrix factorization technique with trust propagation for recommendation in social networks , 2010, RecSys '10.

[2]  Ruslan Salakhutdinov,et al.  Bayesian probabilistic matrix factorization using Markov chain Monte Carlo , 2008, ICML '08.

[3]  Diego Fernández,et al.  Comparison of collaborative filtering algorithms , 2011, ACM Trans. Web.

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

[5]  Wenliang Du,et al.  SVD-based collaborative filtering with privacy , 2005, SAC '05.

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

[7]  Yehuda Koren,et al.  Factorization meets the neighborhood: a multifaceted collaborative filtering model , 2008, KDD.

[8]  SongJie Gong,et al.  Combining Singular Value Decomposition and Item-based Recommender in Collaborative Filtering , 2009, 2009 Second International Workshop on Knowledge Discovery and Data Mining.

[9]  Bracha Shapira,et al.  Recommender Systems Handbook , 2015, Springer US.

[10]  Zhigang Luo,et al.  NeNMF: An Optimal Gradient Method for Nonnegative Matrix Factorization , 2012, IEEE Transactions on Signal Processing.

[11]  Nicolas Gillis,et al.  Robust near-separable nonnegative matrix factorization using linear optimization , 2013, J. Mach. Learn. Res..

[12]  Kenneth Y. Goldberg,et al.  Eigentaste: A Constant Time Collaborative Filtering Algorithm , 2001, Information Retrieval.

[13]  Zhijun Zhang,et al.  Application and Research of Improved Probability Matrix Factorization Techniques in Collaborative Filtering , 2014 .

[14]  Lars Schmidt-Thieme,et al.  Matrix and Tensor Factorization for Predicting Student Performance , 2011, CSEDU.

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

[16]  James Bennett,et al.  The Netflix Prize , 2007 .

[17]  Royi Ronen,et al.  Sage: recommender engine as a cloud service , 2013, RecSys.

[18]  Konstantinos G. Margaritis,et al.  Using SVD and demographic data for the enhancement of generalized Collaborative Filtering , 2007, Inf. Sci..

[19]  Douglas B. Terry,et al.  Using collaborative filtering to weave an information tapestry , 1992, CACM.

[20]  Konstantinos G. Margaritis,et al.  Collaborative Filtering through SVD-Based and Hierarchical Nonlinear PCA , 2010, ICANN.