STEM: Unleashing the Power of Embeddings for Multi-Task Recommendation

Multi-task learning (MTL) has gained significant popularity in recommender systems as it enables simultaneous optimization of multiple objectives. A key challenge in MTL is negative transfer, but existing studies explored negative transfer on all samples, overlooking the inherent complexities within them. We split the samples according to the relative amount of positive feedback among tasks. Surprisingly, negative transfer still occurs in existing MTL methods on samples that receive comparable feedback across tasks. Existing work commonly employs a shared-embedding paradigm, limiting the ability of modeling diverse user preferences on different tasks. In this paper, we introduce a novel Shared and Task-specific EMbeddings (STEM) paradigm that aims to incorporate both shared and task-specific embeddings to effectively capture task-specific user preferences. Under this paradigm, we propose a simple model STEM-Net, which is equipped with an All Forward Task-specific Backward gating network to facilitate the learning of task-specific embeddings and direct knowledge transfer across tasks. Remarkably, STEM-Net demonstrates exceptional performance on comparable samples, achieving positive transfer. Comprehensive evaluation on three public MTL recommendation datasets demonstrates that STEM-Net outperforms state-of-the-art models by a substantial margin. Our code is released at https://github.com/LiangcaiSu/STEM.

Authors

• Liangcai Su Shenzhen International Graduate School, Tsinghua University
• Junwei Pan Tencent Inc.
• Ximei Wang Tencent Inc.
• Xi Xiao Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China Peng Cheng Laboratory, Shenzhen, China Key Laboratory of Cyberspace Security, Ministry of Education of China, Zhengzhou, China
• Shijie Quan Tencent Inc.
• Xihua Chen Tencent Inc.
• Jie Jiang National University of Defense Technology

Keywords

• DMKM: Applications
• DMKM: Recommender Systems