关键词: 位置社会网络, 朋友关系预测, 网络嵌入, 时空信息

Abstract: Location-based Social Networks (LBSNs), which contain the friend relationships between users and the trajectories that users check in POIs, have been widely used to recommend POIs to users, plan events for users and predict the friend relationships between users. However, existing methods for predicting the friend relationships usually ignore the users’ preferences, spatial constraints and dynamic changes for POIs hidden in the check-in trajectories, which have important effects on friend relationship prediction. To address the above issues, a Friendship Prediction method Integrating Multi-View information (FPIMV) is proposed to enhance the embeddings of user pairs and improve friendship prediction by capturing the spatial-temporal information in the check-in trajectories and the friend relationships between users. Specifically, in the spatial information embedding layer of FPIMV, we propose a method for learning the embeddings of user pairs based on the characteristics of check-in POIs, which effectively captures the users’ preferences and spatial constraints for POIs by learning the similarity and the proximity of POIs. In the temporal information embedding layer of FPIMV, we propose a method for learning the embeddings of user pairs based on the characteristics of check-in times, which effectively captures the users’ dynamic changes for POIs by learning the local and global features of check-in times. In the social relationship embedding layer of FPIMV, we learn the embeddings of user pairs based on the meta-paths, which capture the semantic relationships between users. Finally, we enhance the embeddings of user pairs by fusing the above embeddings learned. Experimental results on three datasets from LBSNs show that compared with the baselines, FPIMV improves the performance of friendship prediction.

Key words: location-based social networks, friendship prediction, network embedding, spatial-temporal information