Ranking-based Federated POI Recommendation with Geographic Effect

Point of Interest (POI) recommendation system rec-ommends places in which users have never been to but may be in-terested. Traditionally, it centrally collects contextual information and interaction data to model users' preferences, which raises many privacy concerns. The current studies habitually sacrifice the recommendation performance to cope with privacy con-cerns. To protect users' privacy while ensuring the performance of the POI recommendation system, we propose a Ranking-based Federated POI Recommendation with Geographic Effect (RFPG). The RFPG allows users to reserve their private data on local devices to secure privacy. It adaptively constructs an active region to model the geographic effect, enhancing users' personalized preference modeling. In addition, we design a probability-based negative sampling method to protect privacy further and improve recommendation performance. This method calculates the probability of a POI being a negative sample through POI geographic distribution, then combines the positive samples to construct a local triplet training dataset. Theoretical analysis and experiments on two real datasets demonstrate that our proposed RFPG improves the performance of the POI recommendation while protecting users' private data.