POI Recommender System Using Review Data 


Vol. 13,  No. 11, pp. 654-660, Nov.  2024
https://doi.org/10.3745/TKIPS.2024.13.11.654


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  Abstract

With recent advances in natural language processing (NLP) and the development of large language models (LLMs), leveraging textual data such as user reviews has significantly improved the performance of recommender systems. In this paper, we propose a Point of Interest (POI) recommender system that enhances recommendation accuracy by integrating user review data. We employ a fine-tuned BERT (Bidirectional Encoder Representations from Transformers) model to extract embeddings from user reviews and integrate them with collaborative filtering models (BPR-MF, LightGCN) to improve performance. Experimental results on the Yelp dataset show that models incorporating textual data outperform baseline models in key metrics such as HitRatio@K, Recall@K, and Precision@K. Additionally, by providing users with relevant reviews alongside recommendations, the system enhances the transparency and trustworthiness of the results. This research demonstrates the potential of utilizing textual data in improving POI recommendation systems and offers new opportunities to enhance recommendation accuracy and user experience.

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[IEEE Style]

D. Park, B. Choi, M. Jo, H. Kim, J. Han, "POI Recommender System Using Review Data," The Transactions of the Korea Information Processing Society, vol. 13, no. 11, pp. 654-660, 2024. DOI: https://doi.org/10.3745/TKIPS.2024.13.11.654.

[ACM Style]

Dong-Uk Park, Bumku Choi, Muyeon Jo, Hyunseok Kim, and Jungkyu Han. 2024. POI Recommender System Using Review Data. The Transactions of the Korea Information Processing Society, 13, 11, (2024), 654-660. DOI: https://doi.org/10.3745/TKIPS.2024.13.11.654.

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