Feature Decomposition for Reducing Negative Transfer: A Novel Multi-Task Learning Method for Recommender System (Student Abstract)

We propose a novel multi-task learning method termed Feature Decomposition Network (FDN). The key idea of the proposed FDN is to reduce the phenomenon of feature redundancy by explicitly decomposing features into task-specific features and task-shared features with carefully designed constraints. Experimental results show that our proposed FDN can outperform the state-of-the-art (SOTA) methods by a noticeable margin on Ali-CCP.

Authors

• Jie Zhou East China Normal University
• Qian Yu Beihang University
• Chuan Luo Beihang University
• Jing Zhang

Keywords

• Multi-task Learning
• Negative Transfer
• Recommender System