MLLMRec: A Preference Reasoning Paradigm with Graph Refinement for Multimodal Recommendation

TL;DR

MLLMRec introduces a novel multimodal recommendation framework that leverages large language models for semantic understanding and graph refinement techniques, significantly enhancing recommendation accuracy over existing methods.

Contribution

It proposes a new paradigm combining multimodal large language models with graph refinement to improve user and item representations in recommendation systems.

Findings

Achieves 21.48% average improvement over baselines.

Effectively refines item-item graphs to reduce noise.

Enhances user preference modeling with semantic descriptions.

Abstract

Multimodal recommendation combines the user historical behaviors with the modal features of items to capture the tangible user preferences, presenting superior performance compared to the conventional ID-based recommender systems. However, existing methods still encounter two key problems in the representation learning of users and items, respectively: (1) the initialization of multimodal user representations is either agnostic to historical behaviors or contaminated by irrelevant modal noise, and (2) the widely used KNN-based item-item graph contains noisy edges with low similarities and lacks audience co-occurrence relationships. To address such issues, we propose MLLMRec, a novel preference reasoning paradigm with graph refinement for multimodal recommendation. Specifically, on the one hand, the item images are first converted into high-quality semantic descriptions using a multimodal large language model (MLLM), thereby bridging the semantic gap between visual and textual modalities. Then, we construct a behavioral description list for each user and feed it into the MLLM to reason about the purified user preference profiles that contain the latent interaction intents. On the other hand, we develop the threshold-controlled denoising and topology-aware enhancement strategies to refine the suboptimal item-item graph, thereby improving the accuracy of item representation learning. Extensive experiments on three publicly available datasets demonstrate that MLLMRec achieves the state-of-the-art performance with an average improvement of 21.48% over the optimal baselines. The source code is provided at https://github.com/Yuzhuo-Dang/MLLMRec.git.