X-Reflect: Cross-Reflection Prompting for Multimodal Recommendation

TL;DR

X-Reflect is a novel multimodal prompting framework that enhances recommendation accuracy by explicitly identifying and reconciling textual and visual information, with a lightweight variant for efficient real-time inference.

Contribution

The paper introduces X-Reflect, a new multimodal prompting method that improves recommendation performance by capturing nuanced cross-modal insights and includes a lightweight version for practical deployment.

Findings

Outperforms existing prompting baselines in recommendation accuracy

Identifies a U-shaped relationship between text-image dissimilarity and performance

Lightweight X-Reflect-keyword reduces input length by nearly 50%

Abstract

Large Language Models (LLMs) have been shown to enhance the effectiveness of enriching item descriptions, thereby improving the accuracy of recommendation systems. However, most existing approaches either rely on text-only prompting or employ basic multimodal strategies that do not fully exploit the complementary information available from both textual and visual modalities. This paper introduces a novel framework, Cross-Reflection Prompting, termed X-Reflect, designed to address these limitations by prompting Multimodal Large Language Models (MLLMs) to explicitly identify and reconcile supportive and conflicting information between text and images. By capturing nuanced insights from both modalities, this approach generates more comprehensive and contextually rich item representations. Extensive experiments conducted on two widely used benchmarks demonstrate that our method outperforms existing prompting baselines in downstream recommendation accuracy. Furthermore, we identify a U-shaped relationship between text-image dissimilarity and recommendation performance, suggesting the benefit of applying multimodal prompting selectively. To support efficient real-time inference, we also introduce X-Reflect-keyword, a lightweight variant that summarizes image content using keywords and replaces the base model with a smaller backbone, achieving nearly 50% reduction in input length while maintaining competitive performance. This work underscores the importance of integrating multimodal information and presents an effective solution for improving item understanding in multimodal recommendation systems.