Heterogeneous graph-based joint representation learning for users and POIs in location-based social network
作者：
Highlights：
• We propose a novel framework for representation learning in LBSN by building a heterogeneous LBSN graph. This heterogeneous graph enables a joint modeling of various contextual factors including geographical influence, social relationship and temporal information.
• We devise a simple yet effective method to estimate three kinds of node-to-node relatedness, which jointly consider various contextual factors to better reflect the user spatial behavior and their social relationships.
• Through extensive experiments on two publicly available datasets, we show that the proposed UP2VEC can achieve significantly improvement in POI recommendation and social link prediction.
摘要
•We propose a novel framework for representation learning in LBSN by building a heterogeneous LBSN graph. This heterogeneous graph enables a joint modeling of various contextual factors including geographical influence, social relationship and temporal information.•We devise a simple yet effective method to estimate three kinds of node-to-node relatedness, which jointly consider various contextual factors to better reflect the user spatial behavior and their social relationships.•Through extensive experiments on two publicly available datasets, we show that the proposed UP2VEC can achieve significantly improvement in POI recommendation and social link prediction.
论文关键词：Representation learning,POI recommendation,Link prediction,Heterogeneous LBSN graph
论文评审过程：Received 17 June 2019, Revised 6 September 2019, Accepted 23 October 2019, Available online 6 November 2019, Version of Record 6 November 2019.
论文官网地址：https://doi.org/10.1016/j.ipm.2019.102151
