E-MMKGR: A Unified Multimodal Knowledge Graph Framework for E-commerce Applications

Research Summary

Contribution Summary

• 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 generalizati
• 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.

Related Papers

• BURMESE-SAN: Burmese NLP Benchmark for Evaluating Large Language Models Embedding Neighbor
• MERRY: Semantically Decoupled Evaluation of Multimodal Emotional and Role Consistencies of Role-Playing Agents Embedding Neighbor
• Both Ends Count! Just How Good are LLM Agents at "Text-to-Big SQL"? Embedding Neighbor
• CodePDE: An Inference Framework for LLM-driven PDE Solver Generation Embedding Neighbor
• CogniAlign: Survivability-Grounded Multi-Agent Moral Reasoning for Safe and Transparent AI Embedding Neighbor
• Personalized Prediction of Perceived Message Effectiveness Using Large Language Model Based Digital Twins Embedding Neighbor
• LiLo-VLA: Compositional Long-Horizon Manipulation via Linked Object-Centric Policies Embedding Neighbor
• Transforming Science Learning Materials in the Era of Artificial Intelligence Embedding Neighbor
• DocSplit: A Comprehensive Benchmark Dataset and Evaluation Approach for Document Packet Recognition and Splitting Embedding Neighbor
• The Headless Firm: How AI Reshapes Enterprise Boundaries Tag Overlap
• Measuring AI Ability to Complete Long Software Tasks Tag Overlap
• Residual Connections and the Causal Shift: Uncovering a Structural Misalignment in Transformers Tag Overlap