MMGRec: Multimodal Generative Recommendation with Transformer Model

TL;DR

This paper introduces MMGRec, a novel multimodal recommendation model that employs a generative approach with a Transformer and hierarchical quantization to improve recommendation accuracy and efficiency.

Contribution

The paper proposes a generative paradigm for multimodal recommendation using a Transformer and a new hierarchical quantization method, addressing limitations of previous embed-and-retrieve models.

Findings

Outperforms state-of-the-art methods in recommendation accuracy

Reduces inference cost compared to traditional embed-and-retrieve models

Effectively models non-sequential interaction data with relation-aware self-attention

Abstract

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.