BERT4Loc: BERT for Location -- POI Recommender System
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Abstract (arXiv)
Recommending points of interest (POIs) is a challenging task that requires extracting comprehensive location data from location-based social media platforms. To provide effective location-based recommendations, it's 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. Our experiments on two benchmark dataset show that our BERT-based model outperforms various state-of-the-art sequential models. Moreover, we see the effectiveness of the proposed model for quality through additional experiments.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 23 of its 59 references that are in the map. First posted on arXiv 2 Aug 2022.
Builds on
23 of 59 references.
References · 23
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