📝 SummarXiv

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.