Privacy Preserving Recommendation System Based on Groups
This addresses privacy concerns for users in recommendation systems by offering a method that maintains anonymity while improving performance, though it is incremental as it builds on existing collaborative filtering techniques.
The paper tackles the trade-off between recommendation quality and user privacy by proposing a group-based framework that uses a distributed preference exchange algorithm and a hybrid collaborative filtering model, achieving higher precision and hit rates than state-of-the-art baselines like L+ and ItemRank on MovieLens and Epinions datasets despite not using personal preference information.
Recommendation systems have received considerable attention in the recent decades. Yet with the development of information technology and social media, the risk in revealing private data to service providers has been a growing concern to more and more users. Trade-offs between quality and privacy in recommendation systems naturally arise. In this paper, we present a privacy preserving recommendation framework based on groups. The main idea is to use groups as a natural middleware to preserve users' privacy. A distributed preference exchange algorithm is proposed to ensure the anonymity of data, wherein the effective size of the anonymity set asymptotically approaches the group size with time. We construct a hybrid collaborative filtering model based on Markov random walks to provide recommendations and predictions to group members. Experimental results on the MovieLens and Epinions datasets show that our proposed methods outperform the baseline methods, L+ and ItemRank, two state-of-the-art personalized recommendation algorithms, for both recommendation precision and hit rate despite the absence of personal preference information.