Abstract: The increasingly large scale of location-based social networks (LBSN) promotes the rapid development of point-of-interest (POI) recommendation business. POI geospatial distance directly adopted by traditional methods is difficult to simulate the highly random behavior path of users. And the point-of-interest recommendation process brings sensitivity to the location distance measurement. Meanwhile, the sparse POI check-in data of users in social networks are also easy to have a huge impact on the recommendation accuracy. To solve the above issues, geographically insensitive spatio-temporal POI recommendation model based on heterogeneous graph embedding (GIPR) is proposed. Firstly, the user behavior sequence is introduced to construct the spatial and temporal topological diagram of the behavior POI. The weighted spatial path is used to represent the relative location distance. It can not only conform to the characteristics of user behavior, but also reduce the sensitivity of the recommendation process to the distance between POI, thus enhancing the ability to explain the recommendation results. As for heterogeneous and highly sparse interaction data, the proposed recommendation method can learn the complete LBSN heterogeneous graph from local and global perspectives, and integrate richer user and POI features. Finally, the long-term and short-term preferences of users are extracted through the attention layer to achieve more personalized POI recommendation. Experiments on two large-scale real datasets Foursquare and Gowalla show that GIPR has higher recommendation accuracy and stronger interpretability.

Key words: point-of-interest (POI), heterogeneous graph embedding, geographically insensitive, POI spatial-temporal topology graph

摘要： 基于地理位置的社交网络（LBSN）规模日渐庞大，促进了兴趣点（POI）推荐业务快速发展。现有研究常直接引入POI地理空间距离难以模拟用户高度随机的行为路径，导致兴趣点推荐过程对地点位置距离度量较为敏感。同时，社交网络中用户稀疏的POI签到数据也容易对推荐精度产生巨大影响。针对以上问题，提出一种基于异质图嵌入的地理不敏感时空兴趣点推荐模型（GIPR）。首先，引入用户行为序列，构造行为POI时空拓扑图，使用权重空间路径表示相对位置距离，既符合用户行为特征，也降低了推荐过程对兴趣点间距离的敏感程度，进而增强推荐结果的可解释性。其次，面对异质且高度稀疏的交互数据，提出的GIPR推荐方法可从局部和全局对完整的LBSN异质图进行学习，融合更丰富的用户和POI特征。最后，经过注意力层提取用户的长短期偏好，实现更加个性化的兴趣点推荐。在两个大规模真实数据集Foursquare和Gowalla上的实验表明，GIPR方法具有更高的推荐精度与更强的可解释性。

关键词: 兴趣点（POI）, 异质图嵌入, 地理不敏感, POI时空拓扑图