Feature Decomposition for Reducing Negative Transfer: A Novel Multi-task Learning Method for Recommender System

TL;DR

This paper introduces a Feature Decomposition Network (FDN) for multi-task learning in recommender systems, effectively reducing negative transfer caused by feature redundancy through explicit feature decomposition.

Contribution

The paper proposes a novel FDN method that explicitly decomposes features into task-specific and shared components to mitigate negative transfer in multi-task recommender systems.

Findings

FDN outperforms state-of-the-art methods on synthetic and real datasets.

Explicit feature decomposition reduces negative transfer in multi-task learning.

Experimental results demonstrate improved recommendation accuracy.

Abstract

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.