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Point-of-interest recommendation based on bidirectional self-attention mechanism by fusing spatio-temporal preference

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Abstract

Point-of-Interest (POI) recommendation has emerged as a significant research area within Location-Based Social Networks (LBSNs). Many extant POI recommendation approaches utilize a unidirectional structure to encode users’ historical check-in sequences, often neglecting to adequately consider the time interval between users’ check-ins and the spatial distance between POIs. This omission potentially leads to lower recommendation accuracy. To counter these issues, we propose a model, BSA-ST-Rec (Point-of-Interest Recommendation based on Bidirectional Self-Attention Mechanism by Fusing Spatio-Temporal Preference). Initially, feature information such as check-in sequences, time intervals, and spatial distances are extracted from users’ check-in time sequences. Following this, the feature information is transformed into sequential and spatio-temporal fusion embeddings through an embedding layer. These can be used as enhanced data sources and in conjunction with the bidirectional self-attention mechanism, to capture the dynamic interest preferences of users, thereby predicting users’ next POIs and improving POI recommendation performance. We conducted experiments using two public datasets from Foursquare and Gowalla and compared our results with benchmark methods. The experimental findings demonstrate that our proposed method effectively improves the accuracy of POI recommendations.

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Availability of data and materials

The datasets generated during and analysed during the current study are available in the [Foursquare] and [Gowalla] repository, https://personal.ntu.edu.sg/gaocong/data/poidata.zip.

Code Availability

The relevant code for the paper is available.

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Acknowledgements

The work was supported by grants from the Nature Science Foundation of Anhui Province in China, No.2008085MF193, the Outstanding Young Talents Program of Anhui Province in China, No.gxyqZD2018060, the Major Science and Technology Project of Anhui Province, No.201903a06020006, the Provincial Quality Project of the Anhui Province Education Department in China, No.2019jyxm0285. And we thank all the anonymous reviewers for their hard work and valuable comments.

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Authors and Affiliations

  1. School of Computer and Information, Anqing Normal University, Anqing, 246000, Anhui, China

    Shulin Cheng, Zhongquan Wu, Meng Qian & Wentao Huang

  2. The University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing, 246000, Anhui, China

    Shulin Cheng & Meng Qian

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  1. Shulin Cheng
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Correspondence to Shulin Cheng or Meng Qian.

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Cheng, S., Wu, Z., Qian, M. et al. Point-of-interest recommendation based on bidirectional self-attention mechanism by fusing spatio-temporal preference. Multimed Tools Appl 83, 26333–26347 (2024). https://doi.org/10.1007/s11042-023-16542-z

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