Beyond Personalization and Anonymity: Towards a Group-Based Recommender System

Recommender systems have received considerable attention in recent years. 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 pri- vacy in recommender systems naturally arise. In this pa- per, we present a privacy preserving recommendation frame- work based on groups. The main idea is to use groups as a natural middleware to preserve users’ privacy. A dis- tributed preference exchange algorithm is proposed to en- sure 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 recom- mendations and predictions to group members. Experimen- tal results on the MovieLens dataset show that our proposed methods outperform the baseline methods, L+ and Item- Rank, two state-of-the-art personalized recommendation al- gorithms, for both recommendation precision and hit rate despite the absence of personal preference information.