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BERT4Loc: BERT for Location—POI Recommender System

Author

Listed:
  • Syed Raza Bashir

    (Department of Computer Science, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada)

  • Shaina Raza

    (Vector Institute of Artificial Intelligence, Toronto, ON M5G 1M1, Canada)

  • Vojislav B. Misic

    (Department of Computer Science, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada)

Abstract

Recommending points of interest (POI) is a challenging task that requires extracting comprehensive location data from location-based social media platforms. To provide effective location-based recommendations, it is important to analyze users’ historical behavior and preferences. In this study, we present a sophisticated location-aware recommendation system that uses Bidirectional Encoder Representations from Transformers (BERT) to offer personalized location-based suggestions. Our model combines location information and user preferences to provide more relevant recommendations compared to models that predict the next POI in a sequence. Based on our experiments conducted on two benchmark datasets, we have observed that our BERT-based model surpasses baselines models in terms of HR by a significant margin of 6% compared to the second-best performing baseline. Furthermore, our model demonstrates a percentage gain of 1–2% in the NDCG compared to second best baseline. These results indicate the superior performance and effectiveness of our BERT-based approach in comparison to other models when evaluating HR and NDCG metrics. Moreover, we see the effectiveness of the proposed model for quality through additional experiments.

Suggested Citation

  • Syed Raza Bashir & Shaina Raza & Vojislav B. Misic, 2023. "BERT4Loc: BERT for Location—POI Recommender System," Future Internet, MDPI, vol. 15(6), pages 1-19, June.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:6:p:213-:d:1169566
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    References listed on IDEAS

    as
    1. Zhaoyi Li & Fei Xiong & Ximeng Wang & Hongshu Chen & Xi Xiong, 2019. "Topological Influence-Aware Recommendation on Social Networks," Complexity, Hindawi, vol. 2019, pages 1-12, February.
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