Meta Matrix Factorization for Federated Rating Predictions

Abstract

With distinct privacy protection advantages, federated recommendation is becoming increasingly feasible to store data locally in devices and federally train recommender models. However, previous work on federated recommender systems does not take full account of the limitations of storage, RAM, energy and communication bandwidth in the mobile environment. Their model scales are too big to run easily in mobile devices. Moreover, existing federated recommenders need to fine-tune recommendation models in each device, which makes them hard to effectively exploit collaborative filtering (CF) information among users/devices.