Multi-Task Recommendation with Task Information Decoupling

Multi-task learning (MTL) has become increasingly prevalent in e-commerce recommender systems. However, existing MTL methods, particularly those utilizing the Multi-gate Mixture-of-Experts (MMoE) architecture, face challenges due to their implicit routing mechanisms. These mechanisms can inadvertently lead to negative knowledge transfer, failing to resolve conflicts among tasks and resulting in gradient contradictions on shared parameters. Such issues undermine the generalization capability of MTL models across various tasks. To address these limitations, we introduce the Task Information Decoupling Model (TIDM), designed to alleviate negative transfer by decoupling task knowledge. TIDM incorporates two innovative modules following the expert layer: the Maximize Information Aggregation Module (MIA) and the Automatic Information Selection Module (AIS). The MIA module employs an auxiliary loss to filter out irrelevant task information and aggregates task-specific knowledge using a dissimilar self-attention network. Subsequently, the AIS module automatically selects the most pertinent task-specific information to facilitate task tower learning. Our experiments demonstrate that TIDM outperforms five contemporary MTL models across two datasets, showcasing its effectiveness in extracting task-specific information. This advancement is crucial for enhancing the performance of recommender systems in e-commerce and other complex domains.