# HiNet: Novel Multi-Scenario & Multi-Task Learning with Hierarchical Information Extraction

arXiv: https://arxiv.org/abs/2303.06095 · HTML: https://mlsanity.com/paper/2303.06095/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Placed in MoE for recommender systems, one of 12 directions in Mixture-of-experts.
- Builds on 10 of its 22 references that are in the map.
- First posted on arXiv 10 Mar 2023.

## Placed in

- Mixture-of-experts / [MoE for recommender systems](https://mlsanity.com/moe/moe-for-recommender-systems/)

## Builds on (most cited first, 10 of 10)

- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [Multi-Task Learning as Multi-Objective Optimization](https://mlsanity.com/paper/1810.04650/) (2018)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [Learning Factored Representations in a Deep Mixture of Experts](https://mlsanity.com/paper/1312.4314/) (2013)
- [Routing Networks: Adaptive Selection of Non-linear Functions for Multi-Task Learning](https://mlsanity.com/paper/1711.01239/) (2017)
- [One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction](https://mlsanity.com/paper/2101.11427/) (2021)
- [Cross-Domain Recommendation: Challenges, Progress, and Prospects](https://mlsanity.com/paper/2103.01696/) (2021)
- [Feature Decomposition for Reducing Negative Transfer: A Novel Multi-task Learning Method for Recommender System](https://mlsanity.com/paper/2302.05031/) (2023)
- [Diverse Preference Augmentation with Multiple Domains for Cold-start Recommendations](https://mlsanity.com/paper/2204.00327/) (2022)

## Built on by (most cited first, 11 of 11)

- [Retrievable Domain-Sensitive Feature Memory for Multi-Domain Recommendation](https://mlsanity.com/paper/2405.12892/) (2024)
- [Scene-wise Adaptive Network for Dynamic Cold-start Scenes Optimization in CTR Prediction](https://mlsanity.com/paper/2408.07278/) (2024)
- [PERSCEN: Learning Personalized Interaction Pattern and Scenario Preference for Multi-Scenario Matching](https://mlsanity.com/paper/2506.18382/) (2025)
- [Large-Scale Multi-Domain Recommendation: an Automatic Domain Feature Extraction and Personalized Integration Framework](https://mlsanity.com/paper/2404.08361/) (2024)
- [Scenario-Adaptive Fine-Grained Personalization Network: Tailoring User Behavior Representation to the Scenario Context](https://mlsanity.com/paper/2404.09709/) (2024)
- [Robust Representation Learning for Unified Online Top-K Recommendation](https://mlsanity.com/paper/2310.15492/) (2023)
- [Distillation-based Scenario-Adaptive Mixture-of-Experts for the Matching Stage of Multi-scenario Recommendation](https://mlsanity.com/paper/2601.02368/) (2025)
- [MDL: A Unified Multi-Distribution Learner in Large-scale Industrial Recommendation through Tokenization](https://mlsanity.com/paper/2602.07520/) (2026)
- [SeaAlert: Robust Severity Classification and LLM-Based Information Extraction for Noisy Maritime Distress Communications](https://mlsanity.com/paper/2604.14163/) (2026)
- [UME: A Unified Meta-Generalization Framework for Cross-Domain ETA](https://mlsanity.com/paper/2606.00979/) (2026)
- [Scene-Conditioned Relation Routing for urban cellular activity forecasting](https://mlsanity.com/paper/2609.20209/) (2026)

## Abstract (arXiv)

Multi-scenario & multi-task learning has been widely applied to many recommendation systems in industrial applications, wherein an effective and practical approach is to carry out multi-scenario transfer learning on the basis of the Mixture-of-Expert (MoE) architecture. However, the MoE-based method, which aims to project all information in the same feature space, cannot effectively deal with the complex relationships inherent among various scenarios and tasks, resulting in unsatisfactory performance. To tackle the problem, we propose a Hierarchical information extraction Network (HiNet) for multi-scenario and multi-task recommendation, which achieves hierarchical extraction based on coarse-to-fine knowledge transfer scheme. The multiple extraction layers of the hierarchical network enable the model to enhance the capability of transferring valuable information across scenarios while preserving specific features of scenarios and tasks. Furthermore, a novel scenario-aware attentive network module is proposed to model correlations between scenarios explicitly. Comprehensive experiments conducted on real-world industrial datasets from Meituan Meishi platform demonstrate that HiNet achieves a new state-of-the-art performance and significantly outperforms existing solutions. HiNet is currently fully deployed in two scenarios and has achieved 2.87% and 1.75% order quantity gain respectively.