Do Recommender Systems Really Leverage Multimodal Content? A Comprehensive Analysis on Multimodal Representations for Recommendation

2025-08-06

AI Summary

The performance gains of existing multimodal recommendation systems remain ambiguous—whether attributable to genuine cross-modal understanding or merely increased model complexity. Their core limitation lies in reliance on modality-specific encoders and heuristic fusion strategies, lacking controllable cross-modal alignment mechanisms. Method: We propose a novel multimodal item embedding approach grounded in large vision-language models (VLMs). It employs structured prompting to directly generate semantically aligned, unified embeddings—bypassing explicit modality fusion—and supports decoding into structured textual descriptions for interpretable assessment of multimodal understanding. Contribution/Results: Extensive experiments across multiple benchmarks demonstrate that our VLM-based embeddings significantly outperform standard extractors (e.g., ResNet50 + Sentence-BERT) in recommendation accuracy. This validates that VLM-derived embeddings possess superior semantic depth and cross-modal consistency, establishing a more principled foundation for multimodal recommendation.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

Multimodal Recommender Systems aim to improve recommendation accuracy by integrating heterogeneous content, such as images and textual metadata. While effective, it remains unclear whether their gains stem from true multimodal understanding or increased model complexity. This work investigates the role of multimodal item embeddings, emphasizing the semantic informativeness of the representations. Initial experiments reveal that embeddings from standard extractors (e.g., ResNet50, Sentence-Bert) enhance performance, but rely on modality-specific encoders and ad hoc fusion strategies that lack control over cross-modal alignment. To overcome these limitations, we leverage Large Vision-Language Models (LVLMs) to generate multimodal-by-design embeddings via structured prompts. This approach yields semantically aligned representations without requiring any fusion. Experiments across multiple settings show notable performance improvements. Furthermore, LVLMs embeddings offer a distinctive advantage: they can be decoded into structured textual descriptions, enabling direct assessment of their multimodal comprehension. When such descriptions are incorporated as side content into recommender systems, they improve recommendation performance, empirically validating the semantic depth and alignment encoded within LVLMs outputs. Our study highlights the importance of semantically rich representations and positions LVLMs as a compelling foundation for building robust and meaningful multimodal representations in recommendation tasks.

Problem

• Assessing true multimodal understanding vs model complexity in recommender systems
• Improving cross-modal alignment in multimodal item embeddings
• Enhancing recommendation performance with semantically aligned LVLMs embeddings

Innovation

• Leveraging Large Vision-Language Models for embeddings
• Generating multimodal-by-design embeddings via prompts
• Decoding embeddings into structured textual descriptions