Abstract
Point-of-interest (POI) recommendation is of paramount importance to user travel efficiency since users always expect their intended POIs can be recommended within a specified geospatial range. However, existing methods may not well establish user–user, user–POI, and POI–POI relatedness in a feature-interaction manner and social influence may not be infused into the recommendation process through feature fusion, causing unstable recommendation accuracy in different real-world datasets with multiple variables. A multi-aspect fusion of social and individual features POI recommendation method is proposed in this study, establishing feature interaction gate with user check-in ability, POI popularity, and POI physical characteristics involved. Furthermore, these multi-aspect feature interactions are exploited to incorporate multimodal data and establish information sharing and delivery between users in internal fusion through embedded factorization machine variants imposing individual influence and social influence in location-based social network (LBSN) on recommendation results. Moreover, relatedness enhancement module is established to balance contextual influence and social influence on user next movement decision in external fusion such that direct external information can be transmitted and shared, which diversifies recommendation results. Extensive experiments are conducted on two real-world datasets, and the results show that the proposed model achieves significantly superiority compared with its state-of-the-art baseline models and effectiveness of each proposed modules.







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The data that support the findings of this study are available from Yelp and Dazhongdianping but restrictions apply to the availability of these data, which were used under license for the current study, and so some are not publicly available. Yelp dataset can be accessed through website https://www.yelp.com/dataset. Dazhongdianping dataset is, however, available from the authors upon reasonable request and with permission of Dazhongdianping company.
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Acknowledgements
This research is supported by the National Natural Science Foundation of China (No. 42301529 and No. U21A20107).
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Zhang, Y., Liu, Y. Point-of-interest recommendation based on LBSN with multi-aspect fusion of social and individual features. Neural Comput & Applic 36, 12163–12184 (2024). https://doi.org/10.1007/s00521-024-09711-0
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DOI: https://doi.org/10.1007/s00521-024-09711-0