Feature Decomposition for Reducing Negative Transfer: A Novel Multi-Task Learning Method for Recommender System (Student Abstract) - Zhou, Jie; Yu, Qian; Luo, Chuan; Zhang, Jing (Proceedings of the AAAI Conference on Artificial Intelligence, 2024)
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.