# Retrievable Domain-Sensitive Feature Memory for Multi-Domain Recommendation

arXiv: https://arxiv.org/abs/2405.12892 · HTML: https://mlsanity.com/paper/2405.12892/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 45 references that are in the map.
- First posted on arXiv 21 May 2024.

## Builds on (most cited first, 28 of 28)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention](https://mlsanity.com/paper/2006.16236/) (2020)
- [Rethinking Attention with Performers](https://mlsanity.com/paper/2009.14794/) (2020)
- [Linformer: Self-Attention with Linear Complexity](https://mlsanity.com/paper/2006.04768/) (2020)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020)
- [Pruning neural networks without any data by iteratively conserving synaptic flow](https://mlsanity.com/paper/2006.05467/) (2020)
- [Efficient Attention: Attention with Linear Complexities](https://mlsanity.com/paper/1812.01243/) (2018)
- [Deep Interest Evolution Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1809.03672/) (2018)
- [A Survey on Neural Network Interpretability](https://mlsanity.com/paper/2012.14261/) (2020)
- [Visualizing Deep Neural Network Decisions: Prediction Difference Analysis](https://mlsanity.com/paper/1702.04595/) (2017)
- [One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction](https://mlsanity.com/paper/2101.11427/) (2021)
- [PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information](https://mlsanity.com/paper/2302.01115/) (2023)
- [Optimized Cost per Click in Taobao Display Advertising](https://mlsanity.com/paper/1703.02091/) (2017)
- [Learning Hidden Unit Contributions for Unsupervised Acoustic Model Adaptation](https://mlsanity.com/paper/1601.02828/) (2016)
- [Deep CTR Prediction in Display Advertising](https://mlsanity.com/paper/1609.06018/) (2016)
- [AutoField: Automating Feature Selection in Deep Recommender Systems](https://mlsanity.com/paper/2204.09078/) (2022)
- [HAMUR: Hyper Adapter for Multi-Domain Recommendation](https://mlsanity.com/paper/2309.06217/) (2023)
- [Leaving No One Behind: A Multi-Scenario Multi-Task Meta Learning Approach for Advertiser Modeling](https://mlsanity.com/paper/2201.06814/) (2022)
- [HiNet: Novel Multi-Scenario & Multi-Task Learning with Hierarchical Information Extraction](https://mlsanity.com/paper/2303.06095/) (2023)
- [Multi-Scenario Ranking with Adaptive Feature Learning](https://mlsanity.com/paper/2306.16732/) (2023)

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

- [Few-shot LLM Synthetic Data with Distribution Matching](https://mlsanity.com/paper/2502.08661/) (2025)
- [SampleLLM: Optimizing Tabular Data Synthesis in Recommendations](https://mlsanity.com/paper/2501.16125/) (2025)
- [No One Left Behind: How to Exploit the Incomplete and Skewed Multi-Label Data for Conversion Rate Prediction](https://mlsanity.com/paper/2512.13300/) (2025)
- [Heterogeneous Ranking in Industrial-Scale Recommender Systems: A Case Study](https://mlsanity.com/paper/2607.27577/) (2026)

## Abstract (arXiv)

With the increase in the business scale and number of domains in online advertising, multi-domain ad recommendation has become a mainstream solution in the industry. The core of multi-domain recommendation is effectively modeling the commonalities and distinctions among domains. Existing works are dedicated to designing model architectures for implicit multi-domain modeling while overlooking an in-depth investigation from a more fundamental perspective of feature distributions. This paper focuses on features with significant differences across various domains in both distributions and effects on model predictions. We refer to these features as domain-sensitive features, which serve as carriers of domain distinctions and are crucial for multi-domain modeling. Experiments demonstrate that existing multi-domain modeling methods may neglect domain-sensitive features, indicating insufficient learning of domain distinctions. To avoid this neglect, we propose a domain-sensitive feature attribution method to identify features that best reflect domain distinctions from the feature set. Further, we design a memory architecture that extracts domain-specific information from domain-sensitive features for the model to retrieve and integrate, thereby enhancing the awareness of domain distinctions. Extensive offline and online experiments demonstrate the superiority of our method in capturing domain distinctions and improving multi-domain recommendation performance.