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

arXiv: https://arxiv.org/abs/2302.05031 · HTML: https://mlsanity.com/paper/2302.05031/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 9 references that are in the map.
- First posted on arXiv 10 Feb 2023.

## Builds on (most cited first, 3 of 3)

- [Domain Separation Networks](https://mlsanity.com/paper/1608.06019/) (2016)
- [Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://mlsanity.com/paper/1804.07931/) (2018)
- [Multi-Level Deep Cascade Trees for Conversion Rate Prediction in Recommendation System](https://mlsanity.com/paper/1805.09484/) (2018)

## Built on by (most cited first, 9 of 9)

- [Decoupling Common and Unique Representations for Multimodal Self-supervised Learning](https://mlsanity.com/paper/2309.05300/) (2023)
- [HiNet: Novel Multi-Scenario & Multi-Task Learning with Hierarchical Information Extraction](https://mlsanity.com/paper/2303.06095/) (2023)
- [Scene-wise Adaptive Network for Dynamic Cold-start Scenes Optimization in CTR Prediction](https://mlsanity.com/paper/2408.07278/) (2024)
- [Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution](https://mlsanity.com/paper/2505.21438/) (2025)
- [FashionLens: Toward Versatile Fashion Image Retrieval via Task-Adaptive Learning](https://mlsanity.com/paper/2605.22552/) (2026)
- [Advancements in Recommender Systems: A Comprehensive Analysis Based on Data, Algorithms, and Evaluation](https://mlsanity.com/paper/2407.18937/) (2024)
- [Mask-Guided Multi-Task Network for Face Attribute Recognition](https://mlsanity.com/paper/2601.01408/) (2026)
- [Multi-Modal Decouple and Recouple Network for Robust 3D Object Detection](https://mlsanity.com/paper/2603.07486/) (2026)
- [DUET -- Dual User Embedding Transformers for Offsite Conversion Prediction](https://mlsanity.com/paper/2606.10243/) (2026)

## Abstract (arXiv)

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.