NRDL: Decentralized user preference learning for privacy-preserving next POI recommendation

Predicting where a user goes next in terms of his or her previously visited points of interest (POIs) is significant for facilitating users' daily lives. Simultaneously, it must be acknowledged that the check-in and trajectory in-formation of the user is absolutely disclosed to others in location-based social networks when recommending the next POIs. Therefore, how to achieve an accurate next POI recommendation on the premise of privacy preser-vation is a critical challenge. To address this challenge, we propose decentralized user preference learning for privacy-preserving next POI recommendation, called NRDL. First, to capture the user's next POI preference, we model the user's real-time demand representation by POI profile, POI category, absolute time, transition time and distance between previously visited POIs, which is input into an attention-based recurrent neural network (RNN) model for embedding. Second, to perform privacy preservation, we develop a decentralized learning framework that can achieve user preference learning by raw data on each user's side. Learning on each user's side can make the privacy data of the user not be revealed to platforms or others, and learning from raw data can guarantee the value of check-ins and further accuracy. Finally, we evaluate the proposed model on two widely used Gowalla and Foursquare datasets, and the improvements over the state-of-the-art model are 25.00% and 9.95% at recall@1 and NDCG@1 on Gowalla as well as 22.51% and 10.59% on Foursquare.

Automated summary

Predicting a user's next actions based on previously visited points of interest is important, but preserving privacy is a critical challenge. To address this challenge, we propose a decentralized user preference learning method that models user demand and implements privacy protection to achieve accurate next point of interest recommendations.