Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Collaborative Filtering with Privacy
SP '02 Proceedings of the 2002 IEEE Symposium on Security and Privacy
PocketLens: Toward a personal recommender system
ACM Transactions on Information Systems (TOIS)
SVD-based collaborative filtering with privacy
Proceedings of the 2005 ACM symposium on Applied computing
A Client/Server User-Based Collaborative Filtering Algorithm: Model and Implementation
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
UM'05 Proceedings of the 10th international conference on User Modeling
Distributed rating prediction in user generated content streams
Proceedings of the fifth ACM conference on Recommender systems
Crowdsourcing recommendations from social sentiment
Proceedings of the First International Workshop on Issues of Sentiment Discovery and Opinion Mining
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The amount of data exponentially increases in information systems and it becomes more and more difficult to extract the most relevant information within a very short time. Among others, collaborative filtering processes help users to find interesting items by modeling their preferences and by comparing them with users having the same tastes. Nevertheless, there are a lot of aspects to consider when implementing such a recommender system. The number of potential users and the confidential nature of some data are taken into account. This paper introduces a new distributed recommender system based on a user-based filtering algorithm. Our model has been transposed for Peer-to-Peer architectures. It has been especially designed to deal with problems of scalability and privacy. Moreover, it adapts its prediction computations to the density of the user neighborhood.