Location Predicts You: Location Prediction via Bi-direction Speculation and Dual-level Association

Location Predicts You: Location Prediction via Bi-direction Speculation and Dual-level Association

Xixi Li, Ruimin Hu, Zheng Wang, Toshihiko Yamasaki

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
Main Track. Pages 529-536. https://doi.org/10.24963/ijcai.2021/74

Location prediction is of great importance in location-based applications for the construction of the smart city. To our knowledge, existing models for location prediction focus on the users' preference on POIs from the perspective of the human side. However, modeling users' interests from the historical trajectory is still limited by the data sparsity. Additionally, most of existing methods predict the next location according to the individual data independently. But the data sparsity makes it difficult to mine explicit mobility patterns or capture the casual behavior for each user. To address the issues above, we propose a novel Bi-direction Speculation and Dual-level Association method (BSDA), which considers both users' interests in POIs and POIs' appeal to users. Furthermore, we develop the cross-user and cross-POI association to alleviate the data sparsity by similar users and POIs to enrich the candidates. Experimental results on two public datasets demonstrate that BSDA achieves significant improvements over state-of-the-art methods.
Keywords:
AI Ethics, Trust, Fairness: Surveillance, Manipulation of People
Multidisciplinary Topics and Applications: Social Sciences
Data Mining: Mining Spatial, Temporal Data
Data Mining: Recommender Systems