TY - GEN
T1 - Improved recommendations via (More) collaboration
AU - Boim, Rubi
AU - Kaplan, Haim
AU - Milo, Tova
AU - Rubinfeld, Ronitt
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=78650507361&partnerID=8YFLogxK
U2 - 10.1145/1859127.1859143
DO - 10.1145/1859127.1859143
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AN - SCOPUS:78650507361
SN - 9781450301862
T3 - Proceedings of the ACM SIGMOD International Conference on Management of Data
BT - Proceedings of the 13th International Workshop on the Web and Databases, WebDB 2010, Co-located with ACM SIGMOD 2010
PB - Association for Computing Machinery
T2 - 13th International Workshop on the Web and Databases, WebDB 2010, Co-located with ACM SIGMOD 2010
Y2 - 6 June 2010 through 6 June 2010
ER -