A Spatial-Temporal Self-Attention Network (STSAN) for Location Prediction. (5th April 2021)

• Record Type: Journal Article
• Authors: Wang, Shuang; Li, AnLiang; Xie, Shuai; Li, WenZhu; Wang, BoWei; Yao, Shuai; Asif, Muhammad
• Other Names: Xia Min Academic Editor.
• Is Part Of: Complexity. Volume 2021(2021)
• Publication Date: 2021-04-05
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2021/6692313
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : With the popularity of location-based social networks, location prediction has become an important task and has gained significant attention in recent years. However, how to use massive trajectory data and spatial-temporal context information effectively to mine the user's mobility pattern and predict the users' next location is still unresolved. In this paper, we propose a novel network named STSAN (spatial-temporal self-attention network), which can integrate spatial-temporal information with the self-attention for location prediction. In STSAN, we design a trajectory attention module to learn users' dynamic trajectory representation, which includes three modules: location attention, which captures the location sequential transitions with self-attention; spatial attention, which captures user's preference for geographic location; and temporal attention, which captures the user temporal activity preference. Finally, extensive experiments on four real-world check-ins datasets are designed to verify the effectiveness of our proposed method. Experimental results show that spatial-temporal information can effectively improve the performance of the model. Our method STSAN gains about 39.8% Acc@1 and 4.4% APR improvements against the strongest baseline on New York City dataset.