@article{MC46315C5,
title = "Geographical Adaptive Attention Model for Points of Interest Recommendation",
journal = "Journal of KIISE, JOK",
year = "2025",
issn = "2383-630X",
doi = "10.5626/JOK.2025.52.3.217",
author = "Muyeon Jo, Sejin Chun, Jungkyu Han",
keywords = "location based social network, POI recommender system, geographical influence, attention network",
abstract = "Geographical influence, stemming from the location of Points of Interest (POIs), plays a vital role in POI recommendation. Most current studies utilize geographical information such as distance and location to define and extract POI-specific geographical influences for personalized recommendations. These approaches primarily emphasize distance-based influence, which gauges user preferences based on proximity, while often overlooking area-based influence, which reflects preferences for regions with specific POI characteristics. This paper introduces a POI recommendation model based on an attention network that integrates both distance- and area-based influences. The model adaptively assesses how previously visited POIs impact the likelihood of visiting a target POI, taking into account regional characteristics and user preferences. Experiments conducted on real-world datasets indicate that the proposed method significantly outperforms baseline models, achieving improvements of approximately 6–12% in Prec@10, 8–10% in Recall@10, and 6–7% in HR@10."
}