针对高频低意图行为干扰推荐效果的问题，提出分层抑制噪声的新方法。
HiFIRec Towards High-Frequency yet Low-Intention Behaviors for Multi-Behavior Recommendation
- 分层聚合邻域信息，逐步抑制高频低意图行为的干扰信号
- 通过自适应跨层特征融合，更准确捕捉用户真实意图
- 动态调整负样本权重，纠正看似合理却误导的频繁模式
多行为推荐利用多种用户-物品交互缓解数据稀疏和冷启动问题，在医疗和电商等领域提供个性化服务。现有方法多采用图神经网络统一建模用户意图，未能充分考虑不同行为间的异质性。尤其高频但低意图的行为可能隐含噪声信号，其频繁模式看似合理却具有误导性，阻碍用户意图学习。为此，本文提出新方法HiFIRec，通过差异化行为建模修正高频低意图行为的影响。为消除噪声信号，采用分层邻域聚合机制逐层抑制，并通过自适应跨层特征融合捕捉用户意图。为纠正合理但误导的频繁模式，提出强度感知非采样策略，动态调整负样本权重。在两个基准数据集上的大量实验表明，HiFIRec相比多个先进方法在HR@10上相对提升4.21%至6.81%。
原文摘要 · Abstract (English)
Multi behavior recommendation leverages multiple types of user-item interactions to address data sparsity and cold-start issues,providing personalized services in domains such as healthcare and ecommerce.Most existing methods utilize graph neural networks to model user intention in a unified manner,which inadequately considers the heterogeneity across different behaviors.Especially,high frequency yet low intention behaviors may implicitly contain noisy signals,and frequent patterns that are plausible while misleading,thereby hindering the learning of user intentions.To this end,this paper proposes a novel multi-behavior recommendation method,HiFIRec,that corrects the effect of high-frequency yet low-intention behaviors by differential behavior modeling.To revise the noisy signals,we hierarchically suppress it across layers by extracting neighborhood information through layer-wise neighborhood aggregation and further capturing user intentions through adaptive cross layer feature fusion.To correct plausible frequent patterns,we propose an intensity-aware non-sampling strategy that dynamically adjusts the weights of negative samples.Extensive experiments on two benchmarks show that HiFIRec relatively improves HR@10 by 4.21%-6.81% over several state-of-the-art methods.
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