Heterogeneous Hypergraph Neural Network for Friend Recommendation with Human Mobility
Heterogeneous Hypergraph Neural Network for Friend Recommendation with Human Mobility
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DOI：
10.1145/3511808.3557609
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Yongkang Li;Z. Fan;Jixiao Zhang;Dengheng Shi;Tianqi Xu;Du Yin;Jinliang Deng;Xuan Song
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文献类型：
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作者：
Yongkang Li;Z. Fan;Jixiao Zhang;Dengheng Shi;Tianqi Xu;Du Yin;Jinliang Deng;Xuan Song
基于人类移动的朋友推荐是基于位置的社交网络(LBSN)在现实世界中的重要应用。为了辅助好友推荐，有必要从人类的移动性中识别模式，因为以往的研究已经显示了它们之间的复杂关系。然而，以往的大多数工作要么分别对社交网络和用户轨迹进行建模，要么仅使用经典的基于简单图的方法，这些方法只有一条边连接两个节点，不能完全建模LBSN的复杂数据结构。受超边可以连接多个不同类型节点的启发，我们将用户轨迹和签到记录建模为一种新的异质LBSN超图中的超边，以表示复杂的时空信息。然后，我们为端到端可训练的异质超图神经网络(HHGNN)设计了一种特定类型的注意机制，该机制可以为下一个朋友推荐任务学习超图节点的嵌入。最后，我们的HHGNN模型在四个真实城市数据集上的性能优于最新的方法，同时消融研究也证实了每个模型部分的有效性。
Friend recommendation from human mobility is a vital real-world application of location-based social networks (LBSN). It is necessary to recognize patterns from human mobility to assist friend recommendation because previous works have shown complex relations between them. However, most of previous works either modelled social networks and user trajectories separately, or only used classical simple graph-based methods with an edge linking two nodes that cannot fully model the complex data structure of LBSN. Inspired by the fact that hyperedges can connect multiple nodes of different types, we model user trajectories and check-in records as hyperedges in a novel heterogeneous LBSN hypergraph to represent complex spatio-temporal information. And then, we design a type-specific attention mechanism for an end-to-end trainable heterogeneous hypergraph neural network (HHGNN) with supervised contrastive learning, which can learn hypergraph node embedding for the next friend recommendation task. At last, our model HHGNN outperforms the state-of-the-art methods on four real-world city datasets, while ablation studies also confirm the effectiveness of each model part.