结合超图与高低频注意力的长尾感知兴趣点推荐

doi:10.3969/j.issn.0255-8297.2026.05.011

摘要/Abstract

摘要： 本文提出了一种结合全局频率超图网络与多跨度高低频注意力机制的长尾感知兴趣点（point of interest,POI）推荐方法。首先，构建全局频率超图网络，对头部和尾部POI的后续轨迹分别进行建模，挖掘跨会话的高阶频率信号，捕捉复杂的交互特征。然后，利用多时间跨度高低频注意力机制，捕捉隐藏序列内部的高低频特征。最后，引入长尾增强模块，通过自适应边删除和增强图构建，提高尾部POI的传播能力。在多个真实数据集上的实验结果表明，本文方法优于现有的基线模型，特别是在长尾POI推荐任务上表现出显著的优势。

关键词: 长尾效应, 超图神经网络, 高低频注意力, 增强图, 自适应边删除

Abstract: This paper proposes a long-tail-aware POI recommendation method using a global frequency hypergraph network and a multi-span HiLo (high/low) attention mechanism. First, a global frequency hypergraph network is constructed to separately model the subsequent trajectories of head and tail POIs, thus mining higher-order frequency signals across sessions and capturing complex interaction features. Then, a HiLo attention mechanism with multiple temporal spans is used to capture high- and low-frequency features within the hidden sequences. Finally, a long-tail enhancement module is introduced to improve the propagation ability of tail POIs through adaptive edge deletion and enhanced graph construction. Experimental results on multiple real-world datasets show that the proposed method outperforms existing baseline models and demonstrates significant advantages, particularly in long-tail POI recommendation tasks.

Key words: long-tail effect, hypergraph neural network, HiLo (high/low) attention, enhanced graph, adaptive edge deletion

中图分类号:

P751.1

胡君寅, 卢菁. 结合超图与高低频注意力的长尾感知兴趣点推荐[J]. 应用科学学报, 2026, 44(5): 846-860.

HU Junyin, LU Jing. Long-Tail-Aware Point of Interest Recommendation Using Hypergraph and HiLo Attention[J]. Journal of Applied Sciences, 2026, 44(5): 846-860.

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结合超图与高低频注意力的长尾感知兴趣点推荐

Long-Tail-Aware Point of Interest Recommendation Using Hypergraph and HiLo Attention

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