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...

• Type: Conference
• Year: 2010
• Where: WEBDB
• Authors: Rubi Boim, Haim Kaplan, Tova Milo, Ronitt Rubinfeld
• Keywords: Customer Ratings, Internet Technology, Organization, Recommender System, WEBDB 2010