Adapting Recommender Systems to the New Data Privacy Regulations
Álvaro Tejeda-Lorente, Bernabé-Moreno Juan, Julio Herce-Zelaya, Carlos Porcel, Enrique Herrera‐Viedma · Frontiers in artificial intelligence and applications · 2018
Recommender systems are key enablers to provide personalization and to make systems be adapted to users' needs. Both, users and content or commercial providers benefit from these techniques. While user profiling is required during the recommendation process, it can also introduce additional threats to the user's privacy. New regulations come into place to palliate the misuse of personal information from companies and public institutions. However, there are no clear rules defined for recommender systems. We find in the literature different proposals to privacy-preserving recommender system, but none of them tackle the compliance with the General Data Protection Regulation (GDPR). In this work we suggest a set of guidelines to assess and implement GDPR compliant recommender systems. Recommender providers shall follow our guidelines to make sure that their systems are not only privacy-preserving, but also GDPR compliant.