ID Embedding as Subtle Features of Content and Structure for Multimodal Recommendation

Understand

Multimodal recommendation aims to model user and item representations comprehensively with the involvement of multimedia content for effective recommendations.

• Existing research has shown that it is beneficial for recommendation performance to combine (user- and item-) ID embeddings with multimodal salient features, indicating the value of IDs.
• However, there is a lack of a thorough analysis of the ID embeddings in terms of feature semantics in the literature.
• In this paper, we revisit the value of ID embeddings for multimodal recommendation and conduct a thorough study regarding its semantics, which we recognize as subtle features of \emph{content} and \emph{structure}.