Modeling user micro-behaviors and original interest via Adaptive Multi-Attention Network for session-based recommendation

摘要

Session-based recommendation aims to recommend the next item that matches the user’s preferences based on short-term limited session information. It does not need user’s account or other privacy information and can be applied widely on most e-commerce platforms. Many efforts have made great achievements. However, the following two insights are often neglected. First, a user’s micro-behaviors, such as item browsing, item clicking, item collecting, item carting and purchasing, offer detailed information and can help infer the user’s intention. Second, the first item of a session usually implies the user’s original interest. These insights motivate us to propose a novel model named AMAN (Adaptive Multi-Attention Network) which considers both user micro-behaviors and original interest for session-based recommendation. AMAN contains three key components: Directed Graph Attention Network (DGAN) learns item representation from item-level and adaptively captures items’ associations in the same micro-behavior sequence; Transformer-based Operation-level Attention Network (TOAN) learns item representation from operation-level and adaptively captures items’ associations across different micro-behavior sequences; Micro-behavior Co-Attention Network (MBCAN) learns micro-behavior sequence representation and adaptively captures the co-dependence among different micro-behavior sequences from item-level, and explicitly models the first item that reflects the user’s original interest. Experimental results on Yoochoose, Taobao14 and Taobao15 benchmark datasets show that AMAN outperforms the state-of-the-art baselines, justifying the effectiveness of incorporating user micro-behaviors and original interest for session-based recommendation.