Measure prediction capability of data for collaborative filtering

Collaborative filtering (CF) approaches have been widely been employed in e-commerce to help users find items they like. Whereas most of existing work focuses on improving algorithmic performance, it is important to know whether the recommendation for users and items can be trustworthy. In this paper, we propose a metric, “relatedness,” to measure the potential that a user’s preference on an item can be accurately predicted. The relatedness of a user–item pair is determined by a community which consists of users and items most related to the pair. The relatedness is computed by solving a constrained $$\ell _{1}^{}$$ℓ1-regularized least square problem with a generalized homotopy algorithm, and we design the homotopy-based community search algorithm to identify the community by alternately selecting the most related users and items. As an application of the relatedness metric, we develop the data-oriented combination (DOC) method for recommender systems by integrating a group of benchmark CF methods based on the relatedness of user–item pairs. In experimental studies, we examine the effectiveness of the relatedness metric and validate the performance of the DOC method by comparing it with benchmark methods.

[1]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[2]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

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

[4]  Gediminas Adomavicius,et al.  Towards more confident recommendations: Improving recommender systems using filtering approach based on rating variance , 2007 .

[5]  Peter Brusilovsky,et al.  Collaborative Filtering , 2014, Encyclopedia of Social Network Analysis and Mining.

[6]  Robert M. Bell,et al.  The BellKor 2008 Solution to the Netflix Prize , 2008 .

[7]  Michael A. Saunders,et al.  Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..

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

[9]  Bradley N. Miller,et al.  GroupLens: applying collaborative filtering to Usenet news , 1997, CACM.

[10]  George Karypis,et al.  Sparse linear methods with side information for top-n recommendations , 2012, RecSys.

[11]  David A. McAllester,et al.  Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence , 2009, UAI 2009.

[12]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[13]  Ke Wang,et al.  Bias and controversy: beyond the statistical deviation , 2006, KDD '06.

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

[15]  Mehrbakhsh Nilashi,et al.  Collaborative filtering recommender systems , 2013 .

[16]  Weiwei Xia,et al.  A collaborative filtering algorithm based on Users' Partial Similarity , 2008, 2008 10th International Conference on Control, Automation, Robotics and Vision.

[17]  Bradley N. Miller,et al.  Using filtering agents to improve prediction quality in the GroupLens research collaborative filtering system , 1998, CSCW '98.

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

[19]  Jean-Luc Starck,et al.  Sparse Solution of Underdetermined Systems of Linear Equations by Stagewise Orthogonal Matching Pursuit , 2012, IEEE Transactions on Information Theory.

[20]  Jonathan L. Herlocker,et al.  A collaborative filtering algorithm and evaluation metric that accurately model the user experience , 2004, SIGIR '04.

[21]  Guy Shani,et al.  A Survey of Accuracy Evaluation Metrics of Recommendation Tasks , 2009, J. Mach. Learn. Res..

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

[23]  Yehuda Koren,et al.  Modeling relationships at multiple scales to improve accuracy of large recommender systems , 2007, KDD '07.

[24]  Yaakov Tsaig,et al.  Fast Solution of $\ell _{1}$ -Norm Minimization Problems When the Solution May Be Sparse , 2008, IEEE Transactions on Information Theory.

[25]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[26]  Jun Wang,et al.  Unifying user-based and item-based collaborative filtering approaches by similarity fusion , 2006, SIGIR.

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

[28]  John Riedl,et al.  An algorithmic framework for performing collaborative filtering , 1999, SIGIR '99.

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

[30]  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.

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

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

[33]  John Riedl,et al.  Analysis of recommendation algorithms for e-commerce , 2000, EC '00.

[34]  Hendrik Blockeel,et al.  Alleviating the Sparsity Problem in Collaborative Filtering by Using an Adapted Distance and a Graph-Based Method , 2010, SDM.

[35]  Panagiotis Symeonidis,et al.  Nearest-biclusters collaborative filtering based on constant and coherent values , 2008, Information Retrieval.

[36]  Sean M. McNee,et al.  Confidence Displays and Training in Recommender Systems , 2003, INTERACT.

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

[38]  M. R. Osborne,et al.  A new approach to variable selection in least squares problems , 2000 .

[39]  Dimitris Plexousakis,et al.  Alleviating the Sparsity Problem of Collaborative Filtering Using Trust Inferences , 2005, iTrust.

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

[41]  Luo Si,et al.  A study of mixture models for collaborative filtering , 2006, Information Retrieval.

[42]  丸山 徹.  Convex Analysisの二,三の進展について , 1977 .

[43]  Philip S. Yu,et al.  Enhanced biclustering on expression data , 2003, Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings..