# Universal Multi-modal Multi-domain Pre-trained Recommendation

arXiv: https://arxiv.org/abs/2311.01831 · HTML: https://mlsanity.com/paper/2311.01831/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2023.

## Built on by (most cited first, 2 of 2)

- [Multimodal Quantitative Language for Generative Recommendation](https://mlsanity.com/paper/2504.05314/) (2025)
- [Federated Vision-Language-Recommendation with Personalized Fusion](https://mlsanity.com/paper/2410.08478/) (2024)

## Abstract (arXiv)

There is a rapidly-growing research interest in modeling user preferences via pre-training multi-domain interactions for recommender systems. However, Existing pre-trained multi-domain recommendations mostly select the item texts to be bridges across domains, and simply explore the user behaviors in target domains. Hence, they ignore other informative multi-modal item contents (e.g., visual information), and also lack of thorough consideration of user behaviors from all interactive domains. To address these issues, in this paper, we propose to pre-train universal multi-modal item content presentation for multi-domain recommendation, called UniM^2Rec, which could smoothly learn the multi-modal item content presentations and the multi-modal user preferences from all domains. With the pre-trained multi-domain recommendation model, UniM^2Rec could be efficiently and effectively transferred to new target domains in practice. Extensive experiments conducted on five real-world datasets in target domains demonstrate the superiority of the proposed method over existing competitive methods, especially for the real-world recommendation scenarios that usually struggle with seriously missing or noisy item contents.