Automatic Multi-Task Learning Framework with Neural Architecture Search in Recommendations

Multi-task learning (MTL), which aims to make full use of knowledge contained in multiple tasks to enhance overall performance and efficiency, has been broadly applied in recommendations. The main challenge for MTL models is negative transfer. Existing MTL models, mainly built on the Mixture-of-Experts (MoE) structure, seek enhancements in performance through feature selection and specific expert sharing mode design. However, one expert sharing mode may not be universally applicable due to the complex correlations and diverse demands among various tasks. Additionally, homogeneous expert architectures in such models further limit their performance. To address these issues, in this paper, we propose an innovative automatic MTL framework, AutoMTL, leveraging neural architecture search (NAS) to design optimal expert architectures and sharing modes. The Dual-level Expert Sharing mode and Architecture Navigator (DESAN) search space of AutoMTL can not only efficiently explore expert sharing modes and feature selection schemes but also focus on the architectures of expert subnetworks. Along with this, we introduce an efficient Progressively Discretizing Differentiable Architecture Search (PD-DARTS) algorithm for search space exploration. Extensive experiments demonstrate that AutoMTL can consistently outperform state-of-the-art, human-crafted MTL models. Moreover, the insights obtained from the discovered architectures provide valuable guidance for building new multi-task recommendation models.