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.

翻译：通过预训练跨领域交互来建模用户偏好已成为推荐系统领域的研究热点。然而，现有预训练多领域推荐方法大多选择物品文本作为跨领域桥梁，仅简单探索目标域中的用户行为。这类方法忽略了其他信息丰富的多模态物品内容（如视觉信息），也缺乏对所有交互领域用户行为的全面考量。为解决这些问题，本文提出面向多领域推荐的通用多模态物品内容预训练方法UniM^2Rec，该方法能够从所有领域中平滑学习多模态物品内容表示与多模态用户偏好。基于预训练的多领域推荐模型，UniM^2Rec可在实际场景中高效迁移至新目标域。在五个真实世界目标域数据集上的大量实验表明，所提方法性能优于现有竞争方法，尤其适用于面临严重物品内容缺失或噪声问题的真实推荐场景。