# MLoRA: Multi-Domain Low-Rank Adaptive Network for CTR Prediction

arXiv: https://arxiv.org/abs/2408.08913 · HTML: https://mlsanity.com/paper/2408.08913/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2024.

## Built on by (most cited first, 7 of 7)

- [Low-Rank Adaptation for Foundation Models: A Comprehensive Review](https://mlsanity.com/paper/2501.00365/) (2024)
- [MoE-MLoRA for Multi-Domain CTR Prediction: Efficient Adaptation with Expert Specialization](https://mlsanity.com/paper/2506.07563/) (2025)
- [MTmixAtt: Integrating Mixture-of-Experts with Multi-Mix Attention for Large-Scale Recommendation](https://mlsanity.com/paper/2510.15286/) (2025)
- [TokenMinds: Pretrained User Tokens and Embeddings for User Understanding in Large Recommender Systems](https://mlsanity.com/paper/2606.25147/) (2026)
- [Generative Multi-Target Cross-Domain Recommendation](https://mlsanity.com/paper/2507.12871/) (2025)
- [Cross-Scenario Unified Modeling of User Interests at Billion Scale](https://mlsanity.com/paper/2510.14788/) (2025)
- [Cross-Domain Off-Policy Evaluation and Learning for Contextual Bandits](https://mlsanity.com/paper/2607.22012/) (2026)

## Abstract (arXiv)

Click-through rate (CTR) prediction is one of the fundamental tasks in the industry, especially in e-commerce, social media, and streaming media. It directly impacts website revenues, user satisfaction, and user retention. However, real-world production platforms often encompass various domains to cater for diverse customer needs. Traditional CTR prediction models struggle in multi-domain recommendation scenarios, facing challenges of data sparsity and disparate data distributions across domains. Existing multi-domain recommendation approaches introduce specific-domain modules for each domain, which partially address these issues but often significantly increase model parameters and lead to insufficient training. In this paper, we propose a Multi-domain Low-Rank Adaptive network (MLoRA) for CTR prediction, where we introduce a specialized LoRA module for each domain. This approach enhances the model's performance in multi-domain CTR prediction tasks and is able to be applied to various deep-learning models. We evaluate the proposed method on several multi-domain datasets. Experimental results demonstrate our MLoRA approach achieves a significant improvement compared with state-of-the-art baselines. Furthermore, we deploy it in the production environment of the Alibaba.COM. The online A/B testing results indicate the superiority and flexibility in real-world production environments. The code of our MLoRA is publicly available.