Abstract
Point-of-Interest (POI) recommender systems can effectively assist users to find their preferred POIs. Recent studies mainly focus on extracting users’ dynamic context from their check-in behaviors and using attention mechanism to capture different influence of context information for predicting their real-time requirements. However, the existing methods mainly focus on learning the weights of different POI as well as their correlations in the check-in sequences. In addition, these methods still suffer from limited performance, especially when the interaction data are sparse. In this paper, we propose a C ontext- and C ategory-aware D ouble S elf-A ttention (CCDSA) model for POI recommendation to explore and capture users’ contextual preferences in two different aspects collaboratively, including the fine-grained preference for POI in check-in behaviors and the coarse-grained preference for category. Specifically, we first design a double self-attention mechanism module to learn the users’ preferences for both POI and category in specific context. Then we combine users’ check-in behaviors with POIs’ category information to alleviate data sparsity problem in context-aware recommendation. Finally, we leverage context and category information to perform personalized POI recommendation. In particular, we devise an improved version of CCDSA, i.e., CCDSA+, which further replaces the self-attention mechanism with the sparse self-attention mechanism for improving training efficiency. The experimental results on four real-world datasets, Foursquare-NY, Foursquare-TKY, Weeplaces-NY and Weeplaces-SF show that the proposed models, CCDSA and CCDSA+, outperform the baselines.
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Funding
This research was supported by the National Natural Science Foundation of China under Grant No.62202131, Zhejiang Provincial Natural Science Foundation of China under No.LQ20F020015, and Zhejiang Provincial Key Science and Technology Program Foundation under No.2020C01165.
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Wang, D., Wan, F., Yu, D. et al. Context-and category-aware double self-attention model for next POI recommendation. Appl Intell 53, 18355–18380 (2023). https://doi.org/10.1007/s10489-022-04396-1
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DOI: https://doi.org/10.1007/s10489-022-04396-1