In recent years, thanks to the rapid development of deep learning (DL), DL-based multi-task learning (MTL) has made significant progress, and it has been successfully applied to recommendation systems (RS). However, in a recommender system, the correlations among the involved tasks are complex. Therefore, the existing MTL models designed for RS suffer from negative transfer to different degrees, which will injure optimization in MTL. We find that the root cause of negative transfer is feature redundancy that features learned for different tasks interfere with each other. To alleviate the issue of negative transfer, we propose a novel multi-task learning method termed Feature Decomposition Network (FDN). The key idea of the proposed FDN is reducing the phenomenon of feature redundancy by explicitly decomposing features into task-specific features and task-shared features with carefully designed constraints. We demonstrate the effectiveness of the proposed method on two datasets, a synthetic dataset and a public datasets (i.e., Ali-CCP). Experimental results show that our proposed FDN can outperform the state-of-the-art (SOTA) methods by a noticeable margin.

翻译：近年来，得益于深度学习（DL）的快速发展，基于深度学习的多任务学习（MTL）取得了显著进步，并被成功应用于推荐系统（RS）。然而，在推荐系统中，各任务之间的相关性较为复杂。因此，现有面向RS的MTL模型在不同程度上受到负迁移的影响，这将损害MTL的优化效果。我们发现，负迁移的根本原因在于特征冗余——即为不同任务学习的特征相互干扰。为缓解负迁移问题，我们提出了一种名为特征分解网络（FDN）的新型多任务学习方法。FDN的核心思想是通过精心设计的约束，将特征显式分解为任务特有特征与任务共享特征，从而降低特征冗余现象。我们在两个数据集（一个合成数据集和一个公开数据集Ali-CCP）上验证了所提方法的有效性。实验结果表明，我们的FDN以显著优势超越了现有最先进（SOTA）方法。