On Privacy Preserving Collaborative Filtering: Current Trends, Open Problems, and New Issues

摘要

Automatic recommender systems have become a cornerstone of e-commerce, especially after the great welcome of Web 2.0 based on participation and interaction of Internet users. Collaborative Filtering (CF) is a recommender system that is becoming increasingly relevant for the industry due to the growth of the Internet, which has made it much more difficult to effectively extract useful information. In this paper, we introduce a taxonomy of the different CF families and we discuss the most relevant Privacy Preserving Collaborative Filtering (PPCF) methods in the literature. To understand the inherent challenges of the PPCF, we also conduct an overview of the current tendencies and major drawbacks of this kind of recommender systems, and we propose several strategies to overcome the shortcomings.