Behavior sequence aggregation and attention mechanism-based interest recommendation
Xinlin Li, Lin Sun, Siyuan Chen, Handong Wang, Qiang Wen, Xin Zhang · 2023
Deep Interest Networks (DINs) capture potential user interests by mining long-term and short-term behavioral sequences of users, thereby predicting the information that users are interested in. However, vanilla DINs are unable to handle user behavior sequences exceeding a predetermined length, resulting in the inability to effectively capture potential long-term needs of users. To address this issue, an Attention-based Interest Network(AIN) is proposed which utilizes aggregated user behavior sequences. AIN aggregates users' historical behavior sequences and adopts attention mechanism to enhance expression capabilities. Experimental results demonstrate that AIN can significantly improve recommendation performance. Compared to vanilla DIN models, AIN achieves the best AUC of 0.6429 and ReIaImpr of 7.44.