GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Fab: content-based, collaborative recommendation
Communications of the ACM
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
PTV: Intelligent Personalised TV Guides
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Collaborative Filtering with Privacy
SP '02 Proceedings of the 2002 IEEE Symposium on Security and Privacy
Privacy-Preserving Collaborative Filtering Using Randomized Perturbation Techniques
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
PocketLens: Toward a personal recommender system
ACM Transactions on Information Systems (TOIS)
Algorithmic Game Theory
Private distributed collaborative filtering using estimated concordance measures
Proceedings of the 2007 ACM conference on Recommender systems
Enhancing privacy and preserving accuracy of a distributed collaborative filtering
Proceedings of the 2007 ACM conference on Recommender systems
Preserving privacy in collaborative filtering through distributed aggregation of offline profiles
Proceedings of the third ACM conference on Recommender systems
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
Measuring the privacy of user profiles in personalized information systems
Future Generation Computer Systems
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We address the fundamental tradeoff between privacy preservation and high-quality recommendation stemming from a third party. Multiple users submit their ratings to a third party about items they have viewed. The third party aggregates the ratings and generates personalized recommendations for each user. The quality of recommendations for each user depends on submitted rating profiles from all users, including the user to which the recommendation is destined. Each user would like to declare a rating profile so as to preserve data privacy as much as possible, while not causing deterioration in the quality of the recommendation he would get, compared to the one he would get if he revealed his true private profile. We employ game theory to model and study the interaction of users and we derive conditions and expressions for the Nash Equilibrium Point (NEP). This consists of the rating strategy of each user, such that no user can benefit in terms of improving its privacy by unilaterally deviating from that point. User strategies converge to the NEP after an iterative best-response strategy update. For a hybrid recommendation system, we find that the NEP strategy for each user in terms of privacy preservation is to declare false rating only for one item, the one that is highly ranked in his private profile and less correlated with items for which he anticipates recommendation. We also present various modes of cooperation by which users can mutually benefit.