An Efficient Non-Negative Matrix-Factorization-Based Approach to Collaborative Filtering for Recommender Systems

Matrix-factorization (MF)-based approaches prove to be highly accurate and scalable in addressing collaborative filtering (CF) problems. During the MF process, the non-negativity, which ensures good representativeness of the learnt model, is critically important. However, current non-negative MF (NMF) models are mostly designed for problems in computer vision, while CF problems differ from them due to their extreme sparsity of the target rating-matrix. Currently available NMF-based CF models are based on matrix manipulation and lack practicability for industrial use. In this work, we focus on developing an NMF-based CF model with a single-element-based approach. The idea is to investigate the non-negative update process depending on each involved feature rather than on the whole feature matrices. With the non-negative single-element-based update rules, we subsequently integrate the Tikhonov regularizing terms, and propose the regularized single-element-based NMF (RSNMF) model. RSNMF is especially suitable for solving CF problems subject to the constraint of non-negativity. The experiments on large industrial datasets show high accuracy and low-computational complexity achieved by RSNMF.

[1]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[2]  Guy Shani,et al.  Evaluating Recommendation Systems , 2011, Recommender Systems Handbook.

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

[4]  Qiang Yang,et al.  Tracking Mobile Users in Wireless Networks via Semi-Supervised Colocalization , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  H. Sebastian Seung,et al.  Algorithms for Non-negative Matrix Factorization , 2000, NIPS.

[6]  Shengcai Liao,et al.  Which photo groups should I choose? A comparative study of recommendation algorithms in Flickr , 2010, J. Inf. Sci..

[7]  Yung-Yu Chuang,et al.  Collaborative video reindexing via matrix factorization , 2012, TOMCCAP.

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

[9]  Genevieve Gorrell,et al.  Generalized Hebbian Algorithm for Incremental Singular Value Decomposition in Natural Language Processing , 2006, EACL.

[10]  Michael R. Lyu,et al.  Learning to recommend with explicit and implicit social relations , 2011, TIST.

[11]  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.

[12]  Gang Chen,et al.  Collaborative Filtering Using Orthogonal Nonnegative Matrix Tri-factorization , 2007, Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007).

[13]  Yongmoo Suh,et al.  A new similarity function for selecting neighbors for each target item in collaborative filtering , 2013, Knowl. Based Syst..

[14]  Thomas Hofmann,et al.  Latent semantic models for collaborative filtering , 2004, TOIS.

[15]  Chris H. Q. Ding,et al.  Convex and Semi-Nonnegative Matrix Factorizations , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

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

[18]  Ning Zhou,et al.  A Hybrid Probabilistic Model for Unified Collaborative and Content-Based Image Tagging , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Michael R. Lyu,et al.  SoRec: social recommendation using probabilistic matrix factorization , 2008, CIKM '08.

[20]  András A. Benczúr,et al.  Methods for large scale SVD with missing values , 2007 .

[21]  M. Wu,et al.  Collaborative Filtering via Ensembles of Matrix Factorizations , 2007, KDD 2007.

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

[23]  M. Analoui,et al.  Application of nonnegative matrix factorization in recommender systems , 2012, 6th International Symposium on Telecommunications (IST).

[24]  Michael W. Berry,et al.  Algorithms and applications for approximate nonnegative matrix factorization , 2007, Comput. Stat. Data Anal..

[25]  Seungjin Choi,et al.  Weighted Nonnegative Matrix Co-Tri-Factorization for Collaborative Prediction , 2009, ACML.

[26]  Domonkos Tikk,et al.  Scalable Collaborative Filtering Approaches for Large Recommender Systems , 2009, J. Mach. Learn. Res..

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

[28]  Zibin Zheng,et al.  Predicting Quality of Service for Selection by Neighborhood-Based Collaborative Filtering , 2013, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[29]  Zibin Zheng,et al.  Collaborative Web Service QoS Prediction via Neighborhood Integrated Matrix Factorization , 2013, IEEE Transactions on Services Computing.

[30]  Yehuda Koren,et al.  Advances in Collaborative Filtering , 2011, Recommender Systems Handbook.

[31]  Yihong Gong,et al.  Fast nonparametric matrix factorization for large-scale collaborative filtering , 2009, SIGIR.

[32]  Hong-Hee Lee,et al.  A New SVM Method for an Indirect Matrix Converter With Common-Mode Voltage Reduction , 2014, IEEE Transactions on Industrial Informatics.

[33]  Zhaohui Wu,et al.  An Efficient Recommendation Method for Improving Business Process Modeling , 2014, IEEE Transactions on Industrial Informatics.

[34]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[35]  Juan-Zi Li,et al.  Typicality-Based Collaborative Filtering Recommendation , 2014, IEEE Transactions on Knowledge and Data Engineering.

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

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

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

[39]  Josep Lluís de la Rosa i Esteva,et al.  A Negotiation-Style Recommender Based on Computational Ecology in Open Negotiation Environments , 2011, IEEE Transactions on Industrial Electronics.

[40]  Tommi S. Jaakkola,et al.  Maximum-Margin Matrix Factorization , 2004, NIPS.

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

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

[43]  Philippe Delarue,et al.  A Simple Carrier-Based Modulation for the SVM of the Matrix Converter , 2013, IEEE Transactions on Industrial Informatics.