# Residual Multi-Task Learner for Applied Ranking

arXiv: https://arxiv.org/abs/2411.09705 · HTML: https://mlsanity.com/paper/2411.09705/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 39 references that are in the map.
- First posted on arXiv 30 Oct 2024.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [Learning multiple visual domains with residual adapters](https://mlsanity.com/paper/1705.08045/) (2017)
- [Recommendations as Treatments: Debiasing Learning and Evaluation](https://mlsanity.com/paper/1602.05352/) (2016)
- [Bias and Debias in Recommender System: A Survey and Future Directions](https://mlsanity.com/paper/2010.03240/) (2020)
- [Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://mlsanity.com/paper/1804.07931/) (2018)
- [KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos](https://mlsanity.com/paper/2208.08696/) (2022)
- [Trace Norm Regularised Deep Multi-Task Learning](https://mlsanity.com/paper/1606.04038/) (2016)
- [ESCM2: Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation](https://mlsanity.com/paper/2204.05125/) (2022)
- [Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising](https://mlsanity.com/paper/2105.08489/) (2021)
- [COLD: Towards the Next Generation of Pre-Ranking System](https://mlsanity.com/paper/2007.16122/) (2020)
- [Multi-view Multi-behavior Contrastive Learning in Recommendation](https://mlsanity.com/paper/2203.10576/) (2022)
- [Low-rank Adaptation of Large Language Model Rescoring for Parameter-Efficient Speech Recognition](https://mlsanity.com/paper/2309.15223/) (2023)
- [DCMT: A Direct Entire-Space Causal Multi-Task Framework for Post-Click Conversion Estimation](https://mlsanity.com/paper/2302.06141/) (2023)
- [Residual Continual Learning](https://mlsanity.com/paper/2002.06774/) (2020)
- [Clustered Embedding Learning for Recommender Systems](https://mlsanity.com/paper/2302.01478/) (2023)
- [Multi-Scale User Behavior Network for Entire Space Multi-Task Learning](https://mlsanity.com/paper/2208.01889/) (2022)

## Built on by (most cited first, 1 of 1)

- [A Probabilistic Framework for Temporal Distribution Generalization in Industry-Scale Recommender Systems](https://mlsanity.com/paper/2511.21032/) (2025)

## Abstract (arXiv)

Modern e-commerce platforms rely heavily on modeling diverse user feedback to provide personalized services. Consequently, multi-task learning has become an integral part of their ranking systems. However, existing multi-task learning methods encounter two main challenges: some lack explicit modeling of task relationships, resulting in inferior performance, while others have limited applicability due to being computationally intensive, having scalability issues, or relying on strong assumptions. To address these limitations and better fit our real-world scenario, pre-rank in Shopee Search, we introduce in this paper ResFlow, a lightweight multi-task learning framework that enables efficient cross-task information sharing via residual connections between corresponding layers of task networks. Extensive experiments on datasets from various scenarios and modalities demonstrate its superior performance and adaptability over state-of-the-art methods. The online A/B tests in Shopee Search showcase its practical value in large-scale industrial applications, evidenced by a 1.29% increase in OPU (order-per-user) without additional system latency. ResFlow is now fully deployed in the pre-rank module of Shopee Search. To facilitate efficient online deployment, we propose a novel offline metric Weighted Recall@K, which aligns well with our online metric OPU, addressing the longstanding online-offline metric misalignment issue. Besides, we propose to fuse scores from the multiple tasks additively when ranking items, which outperforms traditional multiplicative fusion. The code is released at https://github.com/BrunoTruthAlliance/ResFlow