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• E-MMKGR improves recommendation accuracy by up to 10.18% in Recall@10 compared to existing methods on real-world Amazon datasets.result 90%
• E-MMKGR improves product search performance by up to 21.72% over vector-based retrieval methods.result 90%
• E-MMKGR constructs an e-commerce-specific Multimodal Knowledge Graph (E-MMKG) that integrates various product-related modalities.method 85%
• E-MMKGR learns unified item representations through GNN-based propagation and KG-oriented optimization that provide a shared semantic foundation applicable to diverse tasks.technical 85%
• Current multimodal recommender systems are limited by their reliance on fixed modality sets and task-specific objectives.limitation 80%
• E-MMKGR's reliance on current state-of-the-art pretrained models for feature extraction could be a limitation if such models become outdated.limitation 75%
• E-MMKGR enhances item representation extensibility and task generalization by grounding item modalities within a single semantic space.technical 80%
• Changes in the modalities or data quality could affect E-MMKGR's performance consistency.limitation 70%

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