Several geographical latent representation models that capture geographical influences among points-of-interest (POIs) have been proposed. Although the models improve POI recommendation performance, they depend on shallow methods that cannot effectively capture highly non-linear geographical influences from complex user-POI networks. In this paper, we propose a new graph-based geographical latent representation model (GGLR) which can capture highly non-linear geographical influences from complex user-POI networks. Our proposed GGLR considers two types of geographical influences: ingoing influences and outgoing influences. Based on a graph auto-encoder, geographical latent representations of ingoing and outgoing influences are trained to increase geographical influences between two consecutive POIs that frequently appear in check-in histories. Furthermore, we propose a graph neural network-based POI recommendation model (GPR) that uses the trained geographical latent representations of ingoing and outgoing influences for the estimation of user preferences. In the experimental evaluation on real-world datasets, we show that GGLR effectively captures highly non-linear geographical influences and GPR achieves state-of-the-art performance.
|Title of host publication
|CIKM 2020 - Proceedings of the 29th ACM International Conference on Information and Knowledge Management
|Association for Computing Machinery
|Number of pages
|Published - 2020 Oct 19
|29th ACM International Conference on Information and Knowledge Management, CIKM 2020 - Virtual, Online, Ireland
Duration: 2020 Oct 19 → 2020 Oct 23
|International Conference on Information and Knowledge Management, Proceedings
|29th ACM International Conference on Information and Knowledge Management, CIKM 2020
|20/10/19 → 20/10/23
Bibliographical noteFunding Information:
This research was supported by the National Research Foundation of Korea (No. NRF-2017R1A2A1A17069645, NRF-2017M3C4A7065887).
© 2020 ACM.
- POI recommendation
- collaborative filtering
- location-based social network
- recommender system
ASJC Scopus subject areas
- General Business,Management and Accounting
- General Decision Sciences