A user behavior-aware multi-task learning model for enhanced short video recommendation

In the rapidly evolving landscape of digital media consumption, accurately predicting user preferences and behaviors is critical for the effectiveness of recommendation systems, especially for short video content. Traditional recommendation methods often ignore the association between multiple user behavior types and struggle with dynamically adapting to user behavior changes, leading to suboptimal personalization and user engagement. To address these issues, this paper introduces a user behavior-aware multi-task learning model for enhanced short video recommendation (UBA-SVR) by leveraging insights into dynamic user interactions. In our approach, we construct a user behavior-aware transformer to comprehensively capture users' dynamic interests and generate the fusion feature representation. Subsequently, we introduce a hierarchical knowledge extraction framework to process features in multi-stage and adopt a task-aware attention mechanism within the tower network structure to facilitate effective information sharing and differentiation among tasks. Furthermore, we employ a dynamic joint loss optimization strategy to adaptively adjust the loss weights for different tasks to promote collaborative enhancement. Extensive experiments on two real-world datasets demonstrate that the proposed method achieves significant improvements in multiple prediction tasks simultaneously.