  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.
