2021/02/08 by Yingtao Luo, Qiang Liu, Zhaocheng Liu · 2 citations
Computer Science · Social Sciences · #Aggregate (composite) #Artificial intelligence #Computer science #Data Management and Algorithms #Data mining #Exploit #Human Mobility and Location-Based Analysis #Intuition #Machine learning #Recommender Systems and Techniques #Relation (database) #Trajectory #Visualization #cs.AI #cs.IR
paper · pdf · doi:10.1145/3442381.3449998
Accepted to The Web Conference (WWW2021)
arxiv created 2021/02/08 · arxiv updated 2021/02/09 · openalex publication_date 2021/04/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
The next location recommendation is at the core of various location-based applications. Current state-of-the-art models have attempted to solve spatial sparsity with hierarchical gridding and model temporal relation with explicit time intervals, while some vital questions remain unsolved. Non-adjacent locations and non-consecutive visits provide non-trivial correlations for understanding a user’s behavior but were rarely considered. To aggregate all relevant visits from user trajectory and recall the most plausible candidates from weighted representations, here we propose a Spatio-Temporal Attention Network (STAN) for location recommendation. STAN explicitly exploits relative spatiotemporal information of all the check-ins with self-attention layers along the trajectory. This improvement allows a point-to-point interaction between non-adjacent locations and non-consecutive check-ins with explicit spatio-temporal effect. STAN uses a bi-layer attention architecture that firstly aggregates spatiotemporal correlation within user trajectory and then recalls the target with consideration of personalized item frequency (PIF). By visualization, we show that STAN is in line with the above intuition. Experimental results unequivocally show that our model outperforms the existing state-of-the-art methods by 9-17%.