Robust Multimodal Recommendation via Graph Retrieval-Enhanced Modality Completion

TL;DR

This paper introduces GRE-MC, a novel framework that enhances multimodal recommendation by retrieving relevant subgraphs and using graph transformers to improve modality completion, especially under incomplete data conditions.

Contribution

GRE-MC leverages a modality-aware subgraph retrieval and joint graph transformer encoding to improve robustness and accuracy in multimodal recommendation with incomplete data.

Findings

GRE-MC outperforms existing methods on benchmark datasets.

The subgraph retrieval mechanism provides richer context for modality completion.

Joint encoding with graph transformers enhances robustness against missing modalities.

Abstract

Multimodal data plays a critical role in web-based recommendation systems, where information from diverse modalities such as vision and text enhances representation learning. However, real-world multimodal datasets often suffer from modality incompleteness due to sensor failures, annotation scarcity, or privacy constraints, which substantially degrade model performance and reliability. One effective solution to address this issue is modality completion, which reconstructs missing features to provide modality-complete graphs for downstream tasks. Given a query node with missing multimodal features, existing modality completion methods typically infer information from the node itself or its neighbors to reconstruct the missing modality. However, these methods may overlook semantically relevant context in the graph, which contains valuable cues that are non-trivial to capture through simple methods like neighborhood aggregation. In this work, we propose GRE-MC, a Graph Retrieval-Enhanced Modality Completion framework, to overcome these limitations. By introducing a modality-aware subgraph retrieval mechanism, GRE-MC selects semantically relevant subgraphs from the entire graph, providing richer contextual information for completing missing modalities. Subsequently, a graph transformer jointly encodes the query node and the retrieved subgraph via global attention to complete the missing features, while a learnable sparse-routing codebook regularizes latent embeddings into compact bases for improved robustness. Extensive experiments on multimodal recommendation benchmarks demonstrate that GRE-MC consistently outperforms state-of-the-art methods, validating the effectiveness of subgraph retrieval and joint-encoding graph transformer for robust modality completion.