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.

翻译：多任务学习（MTL）因其能同时优化多个目标而在推荐系统中广受欢迎。MTL的关键挑战在于负迁移现象，但现有研究仅探讨了全体样本上的负迁移，忽视了样本内部的固有复杂性。我们根据任务间正反馈的相对数量对样本进行划分。令人惊讶的是，现有MTL方法在跨任务反馈水平相当的样本上仍会出现负迁移。现有工作普遍采用共享嵌入范式，这限制了模型对不同任务上多样化用户偏好的建模能力。本文提出一种新颖的共享与任务特定嵌入（STEM）范式，旨在融合共享嵌入与任务特定嵌入，以有效捕获用户偏好的任务特异性。在此范式下，我们设计了简洁模型STEM-Net，其配备全向前馈任务特定后向门控网络，既可促进任务特定嵌入的学习，又能实现跨任务的直接知识迁移。值得注意的是，STEM-Net在反馈水平可比较的样本上展现出卓越性能，成功实现了正迁移。在三个公开MTL推荐数据集上的全面评估表明，STEM-Net以显著优势超越现有最优模型。我们的代码已开源至https://github.com/LiangcaiSu/STEM。