Abstract
Next point-of-interest (POI) recommendation aims to predict the next destination for users. In the past, most POI recommendation models were based on the user’s historical check-in trajectory to achieve recommendations. However, when these models are trained with sparse historical trajectory data, the learned user’s sequence patterns are unstable, which is difficult to obtain good recommendations. In view of the above problem, we propose the next POI recommendation approach that combines neighbor information with location popularity to alleviate the sparsity of data. Specifically, we construct User-POI graph and POI-POI graph, and use graph neural networks (GNN) to capture neighbor information of effective users on these two graphs. In addition, considering that location popularity is influenced by different times and distances, we design a dynamic method to measure the impact of location popularity on the user’s check-in preferences. In evaluating the experimental performance of two real-world datasets, our approach outperforms several classical next POI recommendation approaches.
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Acknowledgements
This work is supported in part by the National Natural Science Foundation of China (Nos. U21A20474 and 62262003), the Guangxi Science and technology project (2021AA11006 and GuikeAD21220114), the Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing, the Guangxi Talent Highland Project of Big Data Intelligence and Application, the Guangxi Natural Science Foundation (Nos. 2020GXNSFAA297075). The Innovation Project of Guangxi Graduate Education YCSW202-2162.
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Li, X., Liu, T., Wang, Le., Sun, Z., Zeng, H. (2023). Next POI Recommendation with Neighbor and Location Popularity. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Lecture Notes in Computer Science, vol 13624. Springer, Cham. https://doi.org/10.1007/978-3-031-30108-7_31
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