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

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.