A Group-Specific Recommender System

ABSTRACT In recent years, there has been a growing demand to develop efficient recommender systems which track users’ preferences and recommend potential items of interest to users. In this article, we propose a group-specific method to use dependency information from users and items which share similar characteristics under the singular value decomposition framework. The new approach is effective for the “cold-start” problem, where, in the testing set, majority responses are obtained from new users or for new items, and their preference information is not available from the training set. One advantage of the proposed model is that we are able to incorporate information from the missing mechanism and group-specific features through clustering based on the numbers of ratings from each user and other variables associated with missing patterns. In addition, since this type of data involves large-scale customer records, traditional algorithms are not computationally scalable. To implement the proposed method, we propose a new algorithm that embeds a back-fitting algorithm into alternating least squares, which avoids large matrices operation and big memory storage, and therefore makes it feasible to achieve scalable computing. Our simulation studies and MovieLens data analysis both indicate that the proposed group-specific method improves prediction accuracy significantly compared to existing competitive recommender system approaches. Supplementary materials for this article are available online.

[1]  Pasquale Lops,et al.  Content-based Recommender Systems: State of the Art and Trends , 2011, Recommender Systems Handbook.

[2]  A. Land,et al.  An Automatic Method for Solving Discrete Programming Problems , 1960, 50 Years of Integer Programming.

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

[4]  S. Geer.  Hellinger-Consistency of Certain Nonparametric Maximum Likelihood Estimators , 1993 .

[5]  Hongtu Zhu,et al.  Tensor Regression with Applications in Neuroimaging Data Analysis , 2012, Journal of the American Statistical Association.

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

[7]  D. Rubin.  INFERENCE AND MISSING DATA , 1975 .

[8]  Catherine Berrut,et al.  Improving new user recommendations with rule-based induction on cold user data , 2007, RecSys '07.

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

[10]  Xiaotong Shen.  ON THE METHOD OF PENALIZATION , 1998 .

[11]  Nathaniel Good,et al.  Naïve filterbots for robust cold-start recommendations , 2006, KDD '06.

[12]  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.

[13]  W. Wong,et al.  Probability inequalities for likelihood ratios and convergence rates of sieve MLEs , 1995 .

[14]  Xiaotong Shen,et al.  Personalized Prediction and Sparsity Pursuit in Latent Factor Models , 2016 .

[15]  Ailsa H. Land,et al.  An Automatic Method of Solving Discrete Programming Problems , 1960 .

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

[17]  Yu He,et al.  Statistical Significance of the Netflix Challenge , 2012, 1207.5649.

[18]  W. Wong,et al.  Convergence Rate of Sieve Estimates , 1994 .

[19]  R. Tibshirani,et al.  Least angle regression , 2004, math/0406456.

[20]  J. Friedman,et al.  Estimating Optimal Transformations for Multiple Regression and Correlation. , 1985 .

[21]  Raymond J. Mooney,et al.  Content-boosted collaborative filtering for improved recommendations , 2002, AAAI/IAAI.

[22]  José Juan Pazos-Arias,et al.  Providing entertainment by content-based filtering and semantic reasoning in intelligent recommender systems , 2008, IEEE Transactions on Consumer Electronics.

[23]  D. Pollard.  Convergence of stochastic processes , 1984 .

[24]  Furong Huang,et al.  Escaping From Saddle Points - Online Stochastic Gradient for Tensor Decomposition , 2015, COLT.

[25]  Yehuda Koren,et al.  Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[26]  Richard A. Harshman,et al.  Foundations of the PARAFAC procedure: Models and conditions for an "explanatory" multi-model factor analysis , 1970 .

[27]  Robert Kohn,et al.  Convergence of the backfitting algorithm for additive models , 1994 .

[28]  Ken Lang,et al.  NewsWeeder: Learning to Filter Netnews , 1995, ICML.

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

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

[31]  Noga Alon,et al.  Generalization Error Bounds for Collaborative Prediction with Low-Rank Matrices , 2004, NIPS.

[32]  J. Chang,et al.  Analysis of individual differences in multidimensional scaling via an n-way generalization of “Eckart-Young” decomposition , 1970 .

[33]  Leo Breiman,et al.  A deterministic algorithm for global optimization , 1993, Math. Program..

[34]  Shuzhong Zhang,et al.  Maximum Block Improvement and Polynomial Optimization , 2012, SIAM J. Optim..

[35]  Xiaotong Shen,et al.  Computational Developments of ψ-learning , 2005, SDM.

[36]  Mu Zhu,et al.  Content‐boosted matrix factorization techniques for recommender systems , 2012, Stat. Anal. Data Min..

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

[38]  Deepak Agarwal,et al.  Regression-based latent factor models , 2009, KDD.

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

[40]  Loriene Roy,et al.  Content-based book recommending using learning for text categorization , 1999, DL '00.

[41]  Junhui Wang.  Consistent selection of the number of clusters via crossvalidation , 2010 .