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

TL;DR

This paper introduces FastMMRec, a multimodal recommendation framework that applies graph convolutions only during testing, significantly improving efficiency, scalability, and recommendation accuracy by avoiding GCN-related training challenges.

Contribution

The paper proposes a novel testing-phase-only GCN deployment in multimodal recommendation, addressing training inefficiencies and modality isolation issues.

Findings

FastMMRec outperforms baselines in accuracy and efficiency.

Applying GCNs only during testing improves scalability.

Experimental results on three datasets validate the approach.

Abstract

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.