Behavior Pathway Based Multi-scale Self-Attention for Sequence Recommendation
The sequence recommendation algorithm based on self-attention mechanism shows strong ability in captu-ring the global features of user interaction sequence.However,not all the behaviors in the interaction sequence will play a decisive role in the evolution of the user's future behavior,and the single-scale self-attention mechanism is dif-ficult to capture user behavior from different granularity.This paper proposes a multi-scale self-attention mechanism based on behavior path for sequence recommendation.It dynamically captures the behavior evolution mode that plays a decisive role in the final recommendation at different granularity,and removes redundant non-critical behaviors.The experimental results on three public datasets show that the proposed algorithm has a certain improvement over the baseline method in different evaluation metrics.
万方数据
维普
