POI Recommendation with Federated Learning and Privacy Preserving in Cross Domain Recommendation

Point-of-Interest (POI) recommendation is one of the most popular recommendation methodologies. However, POI data is very sensitive and sparse. Users' reluctance to share their context information due to privacy concerns, along with the cold-start problem caused by data sparsity reduces recommendation efficiency. To address these issues, we propose a POI framework for cross-domain recommendation with federated learning and privacy protection features. It utilizes data in an auxiliary domain in users' interest analysis to alleviate the cold-start problem. Moreover, it applies federated learning by analyzing the users' historical data locally and encrypts latent feature distribution for knowledge migration to protect users' privacy. Experiments on real datasets have shown that our framework improves recommendation accuracy while preserving users' privacy as compared to convolutional neural network-based methods when analyzing users' comments.