HMGCL: Heterogeneous multigraph contrastive learning for LBSN friend recommendation
HMGCL: Heterogeneous multigraph contrastive learning for LBSN friend recommendation
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DOI：
10.1007/s11280-022-01092-5
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发表时间：
2022-10
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影响因子：
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通讯作者：
Yongkang Li;Z. Fan;Du Yin;Renhe Jiang;Jinliang Deng;Xuan Song
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作者：
Yongkang Li;Z. Fan;Du Yin;Renhe Jiang;Jinliang Deng;Xuan Song
基于用户轨迹的好友推荐是基于位置的社交网络（LBSN）服务的重要应用。此前的统计分析表明，社交网络关系可以解释10%到30%的人类活动，特别是长途旅行。因此，有必要从人的移动识别模式，以协助朋友推荐。然而，以前的作品要么建模的友谊和签到记录，任何两个节点之间只有一个连接的简单的图形或忽略了大量的重要时空信息和语义信息的原始LBSN数据。为了克服以往工作中常见的简单图的局限性，我们利用异构多重图来建模LBSN数据，并定义节点之间的各种语义连接。在此背景下，我们提出了一种异构多图对比学习（HMGCL）模型来捕获用户节点嵌入学习的人体轨迹的时空特征。大量的实验表明，我们的方法优于国家的最先进的方法在六个现实世界的城市数据集。
Friend recommendation from user trajectory is a vital real-world application of location-based social networks (LBSN) services. Previous statistical analysis indicated that social network relationships could explain 10% to 30% of human movement, especially long-distance travel. Therefore, it is necessary to recognize patterns from human mobility to assist the friend recommendation. However, previous works either modelled friendships and check-in records by simple graphs with only one connection between any two nodes or ignored a large amount of vital spatio-temporal information and semantic information in raw LBSN data. To overcome the limitation of the simple graph commonly seen in previous works, we leverage heterogeneous multigraph to model LBSN data and define various semantic connections between nodes. Against this background, we propose a Heterogeneous Multigraph Contrastive Learning (HMGCL) model to capture spatio-temporal characteristics of human trajectories for user node embedding learning. Extensive experiments show that our method outperforms the state-of-the-art approaches in six real-world city datasets.