Decentralized Next Point-of-Interest Recommendation Guided by Willingness to Share

Decentralized learning (DL) has proven to be effective for privacy-preserving next point-of-interest (POI) recommendation by sharing check-in information among users and collaboratively training on-device models. Existing works, however, simply assume that users tend to share check-ins with neighbors of short geographical distance or similar preferences yet ignore users’ actual willingness to share the information (WSI), causing potential privacy concerns. As such, we present a WSI-guided hierarchical DL framework for next POI recommendation (WHDL-Rec) to seek enhanced privacy protection with recommendation accuracy assured. In particular, WHDL-Rec first performs hierarchical data segregation to partition the private and public user data. It then accords to the server-client architecture, where the server exploits the public data to automatically learn users’ WSI w.r.t. check-ins and capture global user behavior patterns for recommendation accuracy maintenance; and the clients fuse the learned global patterns with the local private data for personalized on-device next POI recommendation, whereby WSI-guided collaborative learning is conducted with more secure check-in sharing. Extensive experiments on three real-world datasets demonstrate the efficacy of WHDL-Rec in delivering more accurate and privacy-preserved recommendations.