Exploiting geographical-temporal awareness attention for next point-of-interest recommendation

• Published in: Neurocomputing (Amsterdam) Vol. 400; pp. 227 - 237
• Main Authors: Liu, Tongcun, Liao, Jianxin, Wu, Zhigen, Wang, Yulong, Wang, Jingyu
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 04-08-2020
• Subjects: Attention mechanism; Geographical-temporal awareness; Location-based social network; POI recommendation; Location-based social network; POI recommendation; Attention mechanism; Geographical-temporal awareness
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2019.12.122

With the prosperity of the location-based social networks, next point-of-interest (POI) recommendation has become an increasingly significant requirement since it can benefit both users and business. Obtaining insight into user mobility for the next POI recommendations is a vital yet challenging task. Existing approaches to understanding user mobility mainly gloss over the check-in sequence, making it fail to explicitly capture the subtle POI–POI interactions across the entire user check-in history and distinguish relevant check-ins from the irrelevant. In this paper, we proposed a novel recommendation approach, namely geographical-temporal awareness hierarchical attention network (GT-HAN) to resolve those issues. We first establish a geographical-temporal attention network to simultaneously uncover the overall sequence dependence and the subtle POI–POI relationships. Then, a context-specific co-attention network was designed to learn to change 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. Finally, we make a POI recommendation using a conditional probability distribution function. Experimental results on real world datasets (obtained from Foursquare and Gowalla) show that the GT-HAN model significantly outperforms current state-of-the-art approaches, and demonstrating the benefits produced by new technologies incorporated into GT-HAN.