  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. Specifically, we first devise a
hierarchical quantization method Graph RQ-VAE to assign Rec-ID for each item
from its multimodal and CF information. Consisting of a tuple of semantically
meaningful tokens, Rec-ID serves as the unique identifier of each item.
Afterward, we train a Transformer-based recommender to generate the Rec-IDs of
user-preferred items based on historical interaction sequences. The generative
paradigm is qualified since this model systematically predicts the tuple of
tokens identifying the recommended item in an autoregressive manner. Moreover,
a relation-aware self-attention mechanism is devised for the Transformer to
handle non-sequential interaction sequences, which explores the element
pairwise relation to replace absolute positional encoding. Extensive
experiments evaluate MMGRec's effectiveness compared with state-of-the-art
methods.
