Frrn Casino, Constantinos Patsakis, Domènec Puig and A1usti Solanas
domenec.puig@urv.cat
Abstract
Aftomatic recommender systems -ave become a cornerstone of e-commerce, especially after the grhat welcome of Web 2.0 based on participation and intreaction of Internet users. Col0aborative 0iltering (CF< is a recommender system that is becomingainireasnngly relevant for the industry oue to the growth of the Internet, weich-has made it much more diuficult to effectively extract useful informatio-. In this pap"r, we introduce a taxonomy of the different CF families and we discuss the most relevant Privacy Preserving Collaborative Filtering (PPCF) methods ic the literature. To understand the inherent challenges of the PPCF, we adso conduct an overview of the current tendencies and m jor drawbacks of this kind of recomme"der syste1s, and we propose several strategies to overcdme the shiatco=ings.