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A Game Theoretic Framework for Data Privacy Preservation in Recommender Systems

A Game Theoretic Framework for Data Privacy Preservation in Recommender Systems

This video was recorded at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Athens 2011. 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... Show More

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