# DIIT: A Domain-Invariant Information Transfer Method for Industrial Cross-Domain Recommendation

arXiv: https://arxiv.org/abs/2410.10835 · HTML: https://mlsanity.com/paper/2410.10835/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 47 references that are in the map.
- First posted on arXiv 29 Sep 2024.

## Builds on (most cited first, 29 of 29)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [FitNets: Hints for Thin Deep Nets](https://mlsanity.com/paper/1412.6550/) (2014)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [Deep & Cross Network for Ad Click Predictions](https://mlsanity.com/paper/1708.05123/) (2017)
- [Curriculum Temperature for Knowledge Distillation](https://mlsanity.com/paper/2211.16231/) (2022)
- [CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms](https://mlsanity.com/paper/1706.07068/) (2017)
- [Cross-Domain Recommendation: Challenges, Progress, and Prospects](https://mlsanity.com/paper/2103.01696/) (2021)
- [KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos](https://mlsanity.com/paper/2208.08696/) (2022)
- [Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction](https://mlsanity.com/paper/1601.02376/) (2016)
- [Adversarial Personalized Ranking for Recommendation](https://mlsanity.com/paper/1808.03908/) (2018)
- [MiNet: Mixed Interest Network for Cross-Domain Click-Through Rate Prediction](https://mlsanity.com/paper/2008.02974/) (2020)
- [A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions](https://mlsanity.com/paper/2108.03357/) (2021)
- [Differential Private Knowledge Transfer for Privacy-Preserving Cross-Domain Recommendation](https://mlsanity.com/paper/2202.04893/) (2022)
- [MADG: Margin-based Adversarial Learning for Domain Generalization](https://mlsanity.com/paper/2311.08503/) (2023)
- [Cross-Domain Recommendation to Cold-Start Users via Variational Information Bottleneck](https://mlsanity.com/paper/2203.16863/) (2022)
- [HAMUR: Hyper Adapter for Multi-Domain Recommendation](https://mlsanity.com/paper/2309.06217/) (2023)
- [A Unified Framework for Cross-Domain and Cross-System Recommendations](https://mlsanity.com/paper/2108.07976/) (2021)
- [Ensemble Knowledge Distillation for CTR Prediction](https://mlsanity.com/paper/2011.04106/) (2020)
- [Scenario-aware and Mutual-based approach for Multi-scenario Recommendation in E-Commerce](https://mlsanity.com/paper/2012.08952/) (2020)
- [KEEP: An Industrial Pre-Training Framework for Online Recommendation via Knowledge Extraction and Plugging](https://mlsanity.com/paper/2208.10174/) (2022)
- [Unbiased Knowledge Distillation for Recommendation](https://mlsanity.com/paper/2211.14729/) (2022)
- [Enhancing Cross-domain Click-Through Rate Prediction via Explicit Feature Augmentation](https://mlsanity.com/paper/2312.00078/) (2023)
- [Exploiting Variational Domain-Invariant User Embedding for Partially Overlapped Cross Domain Recommendation](https://mlsanity.com/paper/2205.06440/) (2022)
- [A Collaborative Transfer Learning Framework for Cross-domain Recommendation](https://mlsanity.com/paper/2306.16425/) (2023)
- [Continual Transfer Learning for Cross-Domain Click-Through Rate Prediction at Taobao](https://mlsanity.com/paper/2208.05728/) (2022)
- [An Incremental Learning framework for Large-scale CTR Prediction](https://mlsanity.com/paper/2209.00458/) (2022)
- [Towards More Robust and Accurate Sequential Recommendation with Cascade-guided Adversarial Training](https://mlsanity.com/paper/2304.05492/) (2023)
- [PEACE: Prototype lEarning Augmented transferable framework for Cross-domain rEcommendation](https://mlsanity.com/paper/2312.01916/) (2023)

## Abstract (arXiv)

Cross-Domain Recommendation (CDR) have received widespread attention due to their ability to utilize rich information across domains. However, most existing CDR methods assume an ideal static condition that is not practical in industrial recommendation systems (RS). Therefore, simply applying existing CDR methods in the industrial RS environment may lead to low effectiveness and efficiency. To fill this gap, we propose DIIT, an end-to-end Domain-Invariant Information Transfer method for industrial cross-domain recommendation. Specifically, We first simulate the industrial RS environment that maintains respective models in multiple domains, each of them is trained in the incremental mode. Then, for improving the effectiveness, we design two extractors to fully extract domain-invariant information from the latest source domain models at the domain level and the representation level respectively. Finally, for improving the efficiency, we design a migrator to transfer the extracted information to the latest target domain model, which only need the target domain model for inference. Experiments conducted on one production dataset and two public datasets verify the effectiveness and efficiency of DIIT.