An introduction to variable and feature selection
The Journal of Machine Learning Research
TiVo: making show recommendations using a distributed collaborative filtering architecture
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
Self-organizing distributed collaborative filtering
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
A decentralized CF approach based on cooperative agents
Proceedings of the 15th international conference on World Wide Web
Distributed collaborative filtering with domain specialization
Proceedings of the 2007 ACM conference on Recommender systems
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Preserving privacy in collaborative filtering through distributed aggregation of offline profiles
Proceedings of the third ACM conference on Recommender systems
Communications of the ACM
Diversification and refinement in collaborative filtering recommender
Proceedings of the 20th ACM international conference on Information and knowledge management
In the Mood4: recommendation by examples
Proceedings of the 16th International Conference on Extending Database Technology
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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.