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A user preference knowledge graph incorporating spatio-temporal transfer features for next POI recommendation

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Abstract

Knowledge graphs can improve the performance of recommendation systems and provide explanations for recommendation results, which have been widely applied in the next Point-of-Interest (POI) recommendation. However, the current knowledge graph method for the next POI recommendation focuses on the static attributes of POIs, and only describes the spatio-temporal characteristics when the user transfers between POIs. To fully tap into user preferences for different POIs, we have done the following innovative work. (1) We construct a user preference knowledge graph with spatio-temporal characteristics, named UPSTKG, which expresses preference information from both individual user and global user perspectives. (2) We use local preference triplets in preference knowledge graphs to construct user preference graphs. And use GCN to obtain user preference vectors to replace common user vectors in the sequence, thereby strengthening the potential connection between users and different POIs. (3) We combine UPSTKG and user preference graph to propose the UPSTKGRec method for the next POI recommendation. To evaluate the effectiveness of UPSTKGRec, it is compared to six highly regarded techniques on three distinct benchmark datasets. Compared with the baseline, the average performance of indicators recell@5 and NDCG@5 has increased by 13.8% and 13.1%.

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Data Availability

Data will be made available on request.

Notes

  1. https://snap.stanford.edu/data/loc-gowalla.html.

  2. https://sites.google.com/site/yangdingqi/home/foursquare-dataset.

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Acknowledgements

The authors gratefully acknowledge the supports provided for this research by the National Natural Science Foundation of China (Grant No. 62002037) and the research project of Chongqing CSTC (cstc2019jcyj-msxmX0588).

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    Chun-Yan Sang, Yang Yang, Yi-Bo Zhang & Shi-Gen Liao

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Sang, CY., Yang, Y., Zhang, YB. et al. A user preference knowledge graph incorporating spatio-temporal transfer features for next POI recommendation. Appl Intell 55, 380 (2025). https://doi.org/10.1007/s10489-025-06290-y

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