MMGRec: Multimodal Generative Recommendation with Transformer Model

Understand

Multimodal recommendation aims to recommend user-preferred candidates based on her/his historically interacted items and associated multimodal information.

• Previous studies commonly employ an embed-and-retrieve paradigm: learning user and item representations in the same embedding space, then retrieving similar candidate items for a user via embedding inner product.
• However, this paradigm suffers from inference cost, interaction modeling, and false-negative issues.
• Toward this end, we propose a new MMGRec model to introduce a generative paradigm into multimodal recommendation.