E-MMKGR: A Unified Multimodal Knowledge Graph Framework for E-commerce Applications
This addresses the problem of modality extensibility and task generalization for e-commerce applications, representing an incremental advance.
The paper tackled the limitations of multimodal recommender systems in e-commerce by proposing E-MMKGR, a framework that constructs a multimodal knowledge graph and learns unified item representations, resulting in improvements of up to 10.18% in Recall@10 for recommendation and up to 21.72% for product search.
Multimodal recommender systems (MMRSs) enhance collaborative filtering by leveraging item-side modalities, but their reliance on a fixed set of modalities and task-specific objectives limits both modality extensibility and task generalization. We propose E-MMKGR, a framework that constructs an e-commerce-specific Multimodal Knowledge Graph E-MMKG and learns unified item representations through GNN-based propagation and KG-oriented optimization. These representations provide a shared semantic foundation applicable to diverse tasks. Experiments on real-world Amazon datasets show improvements of up to 10.18% in Recall@10 for recommendation and up to 21.72% over vector-based retrieval for product search, demonstrating the effectiveness and extensibility of our approach.