# Adaptive Domain Interest Network for Multi-domain Recommendation

arXiv: https://arxiv.org/abs/2206.09672 · HTML: https://mlsanity.com/paper/2206.09672/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2022.

## Built on by (most cited first, 20 of 20)

- [Binary Classification with Confidence Difference](https://mlsanity.com/paper/2310.05632/) (2023)
- [Rec4Ad: A Free Lunch to Mitigate Sample Selection Bias for Ads CTR Prediction in Taobao](https://mlsanity.com/paper/2306.03527/) (2023)
- [Multi-domain Recommendation with Embedding Disentangling and Domain Alignment](https://mlsanity.com/paper/2308.05508/) (2023)
- [BASM: A Bottom-up Adaptive Spatiotemporal Model for Online Food Ordering Service](https://mlsanity.com/paper/2211.12033/) (2022)
- [Hybrid Contrastive Constraints for Multi-Scenario Ad Ranking](https://mlsanity.com/paper/2302.02636/) (2023)
- [Large Language Model as Universal Retriever in Industrial-Scale Recommender System](https://mlsanity.com/paper/2502.03041/) (2025)
- [Masked Multi-Domain Network: Multi-Type and Multi-Scenario Conversion Rate Prediction with a Single Model](https://mlsanity.com/paper/2403.17425/) (2024)
- [Pre-train and Fine-tune: Recommenders as Large Models](https://mlsanity.com/paper/2501.14268/) (2025)
- [PERSCEN: Learning Personalized Interaction Pattern and Scenario Preference for Multi-Scenario Matching](https://mlsanity.com/paper/2506.18382/) (2025)
- [Personalized Recommendation Models in Federated Settings: A Survey](https://mlsanity.com/paper/2504.07101/) (2025)
- [MTMD: A Multi-Task Multi-Domain Framework for Unified Ad Lightweight Ranking at Pinterest](https://mlsanity.com/paper/2510.09857/) (2025)
- [Tokenize Once, Recommend Anywhere: Unified Item Tokenization for Multi-domain LLM-based Recommendation](https://mlsanity.com/paper/2511.12922/) (2025)
- [Robust Representation Learning for Unified Online Top-K Recommendation](https://mlsanity.com/paper/2310.15492/) (2023)
- [Adaptive Conditional Expert Selection Network for Multi-domain Recommendation](https://mlsanity.com/paper/2411.06826/) (2024)
- [Adaptive2: Adaptive Domain Mining for Fine-grained Domain Adaptation Modeling](https://mlsanity.com/paper/2412.08198/) (2024)
- [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)
- [Recent advances in weakly supervised learning: New supervision paradigms, assumption relaxations, and practical solutions](https://mlsanity.com/paper/2608.06896/) (2026)
- [CoRCi: Cross-Reconstruction of Coherent Interests Modeling in Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2608.09580/) (2026)
- [User Representation via Cross Multi-source Behavior Pre-training for Mobile Games](https://mlsanity.com/paper/2609.01057/) (2026)

## Abstract (arXiv)

Industrial recommender systems usually hold data from multiple business scenarios and are expected to provide recommendation services for these scenarios simultaneously. In the retrieval step, the topK high-quality items selected from a large number of corpus usually need to be various for multiple scenarios. Take Alibaba display advertising system for example, not only because the behavior patterns of Taobao users are diverse, but also differentiated scenarios' bid prices assigned by advertisers vary significantly. Traditional methods either train models for each scenario separately, ignoring the cross-domain overlapping of user groups and items, or simply mix all samples and maintain a shared model which makes it difficult to capture significant diversities between scenarios. In this paper, we present Adaptive Domain Interest network that adaptively handles the commonalities and diversities across scenarios, making full use of multi-scenarios data during training. Then the proposed method is able to improve the performance of each business domain by giving various topK candidates for different scenarios during online inference. Specifically, our proposed ADI models the commonalities and diversities for different domains by shared networks and domain-specific networks, respectively. In addition, we apply the domain-specific batch normalization and design the domain interest adaptation layer for feature-level domain adaptation. A self training strategy is also incorporated to capture label-level connections across domains.ADI has been deployed in the display advertising system of Alibaba, and obtains 1.8% improvement on advertising revenue.