HiFIRec: Towards High-Frequency yet Low-Intention Behaviors for Multi-Behavior Recommendation

• URL: http://arxiv.org/abs/2509.25755v1
• Date: Tue, 30 Sep 2025 04:20:45 GMT
• Title: HiFIRec: Towards High-Frequency yet Low-Intention Behaviors for Multi-Behavior Recommendation
• Authors: Ruiqi Luo, Ran Jin, Zhenglong Li, Kaixi Hu, Xiaohui Tao, Lin Li,
• Abstract summary: HiFIRec is a novel multi-behavior recommendation method.<n>It corrects the effect of high-frequency yet low-intention behaviors by differential behavior modeling.<n>Experiments on two benchmarks show that HiFIRec relatively improves HR@10 by 4.21%-6.81% over several state-of-the-art methods.
• Score: 10.558247582357783
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 e-commerce. 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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