A personal data store approach for recommender systems: enhancing privacy without sacrificing accuracy

Recommender systems have become extremely common in recent years, and are applied in a variety of domains. Existing recommender systems exhibit two major limitations: (1) Privacy - each service provider holds a database that contains information about all of its users; and (2) Partial view - when recommending to users, each such service can rely only on data that were collected by the service itself. The Open Personal Data Store (openPDS) architecture was recently suggested for storing personal data in a privacy preserving way. Inspired by openPDS, we suggest a novel architecture for recommender systems that overcomes the two limitations mentioned above. The suggested architecture allows the recommender system to utilize rich data collected about the user (possibly through other services) to produce more accurate recommendations, while allowing its users to manage and gain control over their own data. We evaluate the suggested architecture on two different use cases: movies and web browsing, and compare its performance with that of a popular non-privacy-aware collaborative-filtering algorithm. We find that in comparison to the alternative approach, our approach is able to enhance privacy significantly without sacrificing the accuracy level of the recommendations (and in some cases providing even higher level of accuracy).