A Privacy Preserving System for Movie Recommendations using Federated Learning. (arXiv:2303.04689v1 [cs.IR])

Recommender systems have become ubiquitous in the past years. They solve the
tyranny of choice problem faced by many users, and are employed by many online
businesses to drive engagement and sales. Besides other criticisms, like
creating filter bubbles within social networks, recommender systems are often
reproved for collecting considerable amounts of personal data. However, to
personalize recommendations, personal information is fundamentally required. A
recent distributed learning scheme called federated learning has made it
possible to learn from personal user …

businesses collecting data distributed drive engagement federated learning filter filter bubbles information movie networks online businesses personal personal data personal information privacy privacy preserving problem recommendations recommender systems sales social social networks system systems