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.

本研究提出了一个基于 BERT 的定位感知推荐系统，该系统可以从基于位置的社交媒体平台中提取位置信息，并根据用户的历史行为和偏好，为用户提供更相关的位置推荐结果，该模型在大量的实验数据集中一致优于现有的顺序模型。