Research Area: Machine Learning
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.
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Author(s) Name: Tongcun Liu, Jianxin Liao, Zhigen Wu, Yulong Wang, Jingyu Wang
Journal name: Neurocomputing
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Publisher name: ELSEVIER
DOI: https://doi.org/10.1016/j.neucom.2019.12.122
Volume Information: Volume 400, 4 August 2020, Pages 227-237
