# Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendation

arXiv: https://arxiv.org/abs/2309.02061 · HTML: https://mlsanity.com/paper/2309.02061/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2023.

## Built on by (most cited first, 4 of 4)

- [M3oE: Multi-Domain Multi-Task Mixture-of Experts Recommendation Framework](https://mlsanity.com/paper/2404.18465/) (2024)
- [LLM4MSR: An LLM-Enhanced Paradigm for Multi-Scenario Recommendation](https://mlsanity.com/paper/2406.12529/) (2024)
- [Large-Scale Multi-Domain Recommendation: an Automatic Domain Feature Extraction and Personalized Integration Framework](https://mlsanity.com/paper/2404.08361/) (2024)
- [Generative Bid Shading in Real-Time Bidding Advertising](https://mlsanity.com/paper/2508.06550/) (2025)

## Abstract (arXiv)

Click-Through Rate (CTR) prediction is a fundamental technique in recommendation and advertising systems. Recent studies have shown that implementing multi-scenario recommendations contributes to strengthening information sharing and improving overall performance. However, existing multi-scenario models only consider coarse-grained explicit scenario modeling that depends on pre-defined scenario identification from manual prior rules, which is biased and sub-optimal. To address these limitations, we propose a Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendations (HierRec), which perceives implicit patterns adaptively and conducts explicit and implicit scenario modeling jointly. In particular, HierRec designs a basic scenario-oriented module based on the dynamic weight to capture scenario-specific information. Then the hierarchical explicit and implicit scenario-aware modules are proposed to model hybrid-grained scenario information. The multi-head implicit modeling design contributes to perceiving distinctive patterns from different perspectives. Our experiments on two public datasets and real-world industrial applications on a mainstream online advertising platform demonstrate that our HierRec outperforms existing models significantly.