直接路由梯度（DRGrad）：一种用于多任务学习（MTL）推荐的个性化信息外科手术
Direct Routing Gradient (DRGrad): A Personalized Information Surgery for Multi-Task Learning (MTL) Recommendations
AI总结：
针对多任务学习推荐中的负迁移和跷跷板现象，提出个性化直接路由梯度框架DRGrad，通过路由器、更新器和个性化门控网络减少任务冲突，在真实和公开数据集上优于现有模型且不增加复杂度。
摘要：
多任务学习（MTL）已成为工业级推荐系统中的一种成功策略，具有显著优势，例如能够捕获多样化的用户兴趣并准确检测“点击”或“停留时间”等不同行为。然而，由于现实推荐中任务相关性复杂且常常相互矛盾，负迁移和跷跷板现象对MTL模型构成了挑战。为了在更好地利用个性化信息的同时解决该问题，我们提出了一种个性化直接路由梯度框架（DRGrad），它包含三个关键组件：路由器、更新器和个性化门控网络。DRGrad在训练过程中判断任务之间的利害关系，能够为各个任务利用所有有效的梯度以减少冲突。我们使用包含150亿样本的真实世界推荐数据集，评估了DRGrad在复杂MTL上的效率。结果表明，DRGrad在性能上优于竞争的最先进MTL模型，特别是在AUC（曲线下面积）指标方面，这表明它在不增加模型复杂度的同时，有效管理了多任务学习环境中的任务冲突，并弥补了噪声处理方面的不足。此外，在公开的Census-income数据集和Synthetic数据集上的实验，证明了DRGrad具备判断和路由具有不同程度相关性和个性化的任务间利害关系的能力。
英文摘要：
Multi-task learning (MTL) has emerged as a successful strategy in industrial-scale recommender systems, offering significant advantages such as capturing diverse users' interests and accurately detecting different behaviors like ``click" or ``dwell time". However, negative transfer and the seesaw phenomenon pose challenges to MTL models due to the complex and often contradictory task correlations in real-world recommendations. To address the problem while making better use of personalized information, we propose a personalized Direct Routing Gradient framework (DRGrad), which consists of three key components: router, updater and personalized gate network. DRGrad judges the stakes between tasks in the training process, which can leverage all valid gradients for the respective task to reduce conflicts. We evaluate the efficiency of DRGrad on complex MTL using a real-world recommendation dataset with 15 billion samples. The results show that DRGrad's superior performance over competing state-of-the-art MTL models, especially in terms of AUC (Area Under the Curve) metrics, indicating that it effectively manages task conflicts in multi-task learning environments without increasing model complexity, while also addressing the deficiencies in noise processing. Moreover, experiments on the public Census-income dataset and Synthetic dataset, have demonstrated the capability of DRGrad in judging and routing the stakes between tasks with varying degrees of correlation and personalization.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
