论文标题
bert4loc：位置的伯特 - POI推荐系统
BERT4Loc: BERT for Location -- POI Recommender System
论文作者
论文摘要
推荐点（POI）是一项艰巨的任务，需要从基于位置的社交媒体平台中提取全面的位置数据。为了提供有效的基于位置的建议，重要的是要分析用户的历史行为和偏好。在这项研究中，我们提出了一个复杂的位置感知推荐系统，该系统使用来自变形金刚（BERT）的双向编码器表示，以提供个性化的基于位置的建议。我们的模型结合了位置信息和用户偏好，以提供更相关的建议，而与序列预测下一个POI的模型相比。我们在两个基准数据集上的实验表明，我们基于BERT的模型的表现优于各种最新的顺序模型。此外，我们通过其他实验看到了提出的质量模型的有效性。
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.
