Improved recommendations via (more) collaboration

We consider in this paper a popular class of recommender systems that are based on Collaborative Filtering (CF for short). CF is the process of predicting customer ratings to items based on previous ratings of (similar) users to (similar) items, and is typically used by a single organization, using its own customer ratings. We argue here that a multi-organization collaboration, even for organizations operating in different subject domains, can greatly improve the quality of the recommendations that the individual organizations provide to their users. To substantiate this claim, we present C2F (Collaborative CF), a recommender system that retains the simplicity and efficiency of classical CF, while allowing distinct organizations to collaborate and boost their recommendations. C2F employs CF in a distributed fashion that improves the quality of the generated recommendations, while minimizing the amount of data exchanged between the collaborating parties. Key ingredient of the solution are succinct signatures that can be computed locally for items (users) in a given organization and suffice for identifying similar items (users) in the collaborating organizations. We show that the use of such compact signatures not only reduces data exchange but also allows to speed up, by over 50%, the recommendations computation time.

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

[2]  AdomaviciusGediminas,et al.  Toward the Next Generation of Recommender Systems , 2005 .

[3]  Kamal Ali,et al.  TiVo: making show recommendations using a distributed collaborative filtering architecture , 2004, KDD.

[4]  Bernard Chazelle,et al.  Faster dimension reduction , 2010, Commun. ACM.

[5]  Richard A. Harshman,et al.  Indexing by Latent Semantic Analysis , 1990, J. Am. Soc. Inf. Sci..

[6]  Byeong Man Kim,et al.  A decentralized CF approach based on cooperative agents , 2006, WWW '06.

[7]  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.

[8]  Igor V. Tetko,et al.  Comparison of Overfitting and Overtraining , 2001 .

[9]  I K Fodor,et al.  A Survey of Dimension Reduction Techniques , 2002 .

[10]  Alexander Tuzhilin,et al.  Towards the Next Generation of Recommender Systems , 2010, ICE-B 2010.

[11]  Reza Shokri,et al.  Preserving privacy in collaborative filtering through distributed aggregation of offline profiles , 2009, RecSys '09.

[12]  Tsvi Kuflik,et al.  Distributed collaborative filtering with domain specialization , 2007, RecSys '07.

[13]  J. Rodgers,et al.  Thirteen ways to look at the correlation coefficient , 1988 .

[14]  Jun Wang,et al.  Self-organizing distributed collaborative filtering , 2005, SIGIR '05.

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

[16]  Jun Wang,et al.  Distributed collaborative filtering for peer-to-peer file sharing systems , 2006, SAC.

[17]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..