Trilateral Spatiotemporal Attention Network for User Behavior Modeling in Location-based Search

In location-based search, user's click behavior is naturally bonded with trilateral spatiotemporal information, i.e., the locations of historical user requests, the locations of corresponding clicked items and the occurring time of historical clicks. Appropriate modeling of the trilateral spatiotemporal user click behavior sequence is key to the success of any location-based search service. Though abundant and helpful, existing user behavior modeling methods are insufficient for modeling the rich patterns in trilateral spatiotemporal sequence in that they ignore the interplay among request's geographic information, item's geographic information and the click time. In this work, we study the user behavior modeling problem in location-based search systematically. We propose TRISAN, short for Trilateral Spatiotemporal Attention Network, a novel attention-based neural model that incorporates temporal relatedness into both the modeling of item's geographic closeness and the modeling of request's geographic closeness through a fusion mechanism. In addition, we propose to model the geographic closeness both by distance and by semantic similarity. Extensive experiments demonstrate that the proposed method outperforms existing methods by a large margin and every part of our modeling strategy contributes to its final success.