GUME: Graphs and User Modalities Enhancement for Long-Tail Multimodal Recommendation

TL;DR

GUME introduces a novel approach to long-tail multimodal recommendation by enhancing user-item graphs and user modality representations, significantly improving recommendation quality for items with limited interaction data.

Contribution

The paper proposes GUME, a method that enhances long-tail item representations and user modality features using graph propagation and mutual information maximization, addressing key limitations in prior MMRS work.

Findings

Improves long-tail item recommendation accuracy.

Enhances user modality representation robustness.

Outperforms existing methods on four datasets.

Abstract

Multimodal recommendation systems (MMRS) have received considerable attention from the research community due to their ability to jointly utilize information from user behavior and product images and text. Previous research has two main issues. First, many long-tail items in recommendation systems have limited interaction data, making it difficult to learn comprehensive and informative representations. However, past MMRS studies have overlooked this issue. Secondly, users' modality preferences are crucial to their behavior. However, previous research has primarily focused on learning item modality representations, while user modality representations have remained relatively simplistic.To address these challenges, we propose a novel Graphs and User Modalities Enhancement (GUME) for long-tail multimodal recommendation. Specifically, we first enhance the user-item graph using multimodal similarity between items. This improves the connectivity of long-tail items and helps them learn high-quality representations through graph propagation. Then, we construct two types of user modalities: explicit interaction features and extended interest features. By using the user modality enhancement strategy to maximize mutual information between these two features, we improve the generalization ability of user modality representations. Additionally, we design an alignment strategy for modality data to remove noise from both internal and external perspectives. Extensive experiments on four publicly available datasets demonstrate the effectiveness of our approach.