283 research outputs found
BERT4Loc: BERT for Location -- POI Recommender System
Recommending points of interest is a difficult problem that requires precise
location information to be extracted from a location-based social media
platform. Another challenging and critical problem for such a location-aware
recommendation system is modelling users' preferences based on their historical
behaviors. We propose a location-aware recommender system based on
Bidirectional Encoder Representations from Transformers for the purpose of
providing users with location-based recommendations. The proposed model
incorporates location data and user preferences. When compared to predicting
the next item of interest (location) at each position in a sequence, our model
can provide the user with more relevant results. Extensive experiments on a
benchmark dataset demonstrate that our model consistently outperforms a variety
of state-of-the-art sequential models
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