构建电商多模态知识图谱，统一提升推荐与搜索效果
E-MMKGR: A Unified Multimodal Knowledge Graph Framework for E-commerce Applications
- 基于图神经网络构建专用多模态知识图谱
- 推荐召回率最高提升10.18%，搜索准确率提升21.72%
- 适合需要多任务融合的电商系统研发人员
多模态推荐系统通过融合商品侧的多种模态信息增强协同过滤效果，但其对固定模态集和特定任务目标的依赖限制了模态扩展性和任务泛化能力。本文提出E-MMKGR框架，构建面向电商场景的多模态知识图谱E-MMKG，通过图神经网络传播与知识图谱导向优化，学习统一的商品表征。该表征提供共享语义基础，适用于多种下游任务。在真实亚马逊数据集上的实验表明，推荐任务中Recall@10最高提升10.18%，产品搜索任务中相比向量检索准确率提升达21.72%，验证了方法的有效性与可扩展性。
原文摘要 · Abstract (English)
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.
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