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

2025-09-30

AI Summary

In multi-behavior recommendation, high-frequency but low-intent behaviors (e.g., browsing) introduce noise and spurious frequent patterns, undermining accurate user intent modeling. To address this, we propose HiFIRec—a hierarchical noise-suppression and intensity-aware multi-behavior recommendation framework. Its core contributions are: (1) a hierarchical graph neural network architecture enabling layer-wise neighbor aggregation and adaptive cross-layer feature fusion; (2) an intensity-aware, non-sampling negative-sample weighting mechanism that dynamically suppresses implicit noise in high-frequency behaviors; and (3) a behavior-semantic-intensity-driven differential modeling strategy. Evaluated on two benchmark datasets, HiFIRec consistently outperforms state-of-the-art methods, achieving absolute improvements of 4.21–6.81% in Hit Rate@10. These results demonstrate its effectiveness in enhancing the fidelity and accuracy of user intent learning.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Data Mining & Knowledge Management: Recommender Systems
• Machine Learning: Learning Preferences or Rankings

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Modeling heterogeneous user behaviors in multi-behavior recommendation systems
• Correcting noisy signals from high-frequency low-intention user behaviors
• Addressing misleading frequent patterns through adaptive negative sampling

Innovation

• Hierarchically suppresses noisy signals across layers
• Extracts neighborhood information via layer-wise aggregation
• Dynamically adjusts negative sample weights intensity-aware