# The Best is Yet to Come: Graph Convolution in the Testing Phase for Multimodal Recommendation

arXiv: https://arxiv.org/abs/2507.18489 · HTML: https://mlsanity.com/paper/2507.18489/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2025.

## Built on by (most cited first, 4 of 4)

- [RecGOAT: Graph Optimal Adaptive Transport for LLM-Enhanced Multimodal Recommendation with Dual Semantic Alignment](https://mlsanity.com/paper/2602.00682/) (2026)
- [CAMMSR: Category-Guided Attentive Mixture of Experts for Multimodal Sequential Recommendation](https://mlsanity.com/paper/2603.04320/) (2026)
- [Agents as Knowledge Integrator and Utilizer in Multimodal Recommendation](https://mlsanity.com/paper/2608.29410/) (2026)
- [Bridging the Topological Divide: Dual-Branch Graph Distillation with Semantic Priors for Robust Cold-Start Recommendation](https://mlsanity.com/paper/2610.19578/) (2026)

## Abstract (arXiv)

The efficiency and scalability of graph convolution networks (GCNs) in training recommender systems remain critical challenges, hindering their practical deployment in real-world scenarios. In the multimodal recommendation (MMRec) field, training GCNs requires more expensive time and space costs and exacerbates the gap between different modalities, resulting in sub-optimal recommendation accuracy. This paper critically points out the inherent challenges associated with adopting GCNs during the training phase in MMRec, revealing that GCNs inevitably create unhelpful and even harmful pairs during model optimization and isolate different modalities. To this end, we propose FastMMRec, a highly efficient multimodal recommendation framework that deploys graph convolutions exclusively during the testing phase, bypassing their use in training. We demonstrate that adopting GCNs solely in the testing phase significantly improves the model's efficiency and scalability while alleviating the modality isolation problem often caused by using GCNs during the training phase. We conduct extensive experiments on three public datasets, consistently demonstrating the performance superiority of FastMMRec over competitive baselines while achieving efficiency and scalability.