A Geographical-Temporal Awareness Hierarchical Attention Network for Next Point-of-Interest Recommendation

Abstract

Obtaining insight into user mobility for next point-of-interest (POI) recommendations is a vital yet challenging task in location-based social networking. Information is needed not only to estimate user preferences but to leverage sequence relationships from user check-ins. Existing approaches to understanding user mobility gloss over the check-in sequence, making it difficult to capture the subtle POI-POI connections and distinguish relevant check-ins from the irrelevant. We created a geographically-temporally awareness hierarchical attention network (GT-HAN) to resolve those issues. GT-HAN contains an extended attention network that uses a theory of geographical influence to simultaneously uncover the overall sequence dependence and the subtle POI-POI relationships. We show that the mining of subtle POI-POI relationships significantly improves the quality of next POI recommendations. A context-specific co-attention network was designed to learn changing user preferences by adaptively selecting relevant check-in activities from check-in histories, which enabled GT-HAN to distinguish degrees of user preference for different check-ins. Tests using two large-scale datasets (obtained from Foursquare and Gowalla) demonstrated the superiority of GT-HAN over existing approaches and achieved excellent results.