Research on the Self Evolution Construction and Interpretable Recommendation of Cross Domain Knowledge Graph for E-Commerce Products Based on Multimodal Large Language Model

Abstract

Cross-domain e-commerce recommendation faces challenges from multimodal product heterogeneity, sparse intercategory associations, and opaque recommendation reasoning. To improve accuracy and interpretability, this study proposes a multimodal large-language-model-driven framework for self-evolving cross-domain product knowledge graphs and explainable recommendation. Product images, titles, and attributes from apparel, home-furnishing, and digital-product domains are encoded using a multimodal large model, mapped into a shared latent space, and aligned through contrastive learning for cross-domain entity and semantic association extraction. A self-evolution mechanism uses the large language model as a relation verifier and reasoning engine to validate, buffer, prune, or extend graph edges according to confidence and interaction feedback. A path-aware graph neural network then samples multi-hop cross-domain paths, encodes product sequences through gated recurrent units, and fuses graph representations with original multimodal embeddings. Recommendation explanations are generated from high-weight inference chains under loyalty constraints to ensure factual consistency. Experiments on a large e-commerce dataset show that the proposed method achieves Hits@10 of 0.892 and MRR of 0.537 for relation completion, while CTR, CVR, and Recall@20 reach 12.4%, 6.2%, and 22.1%, respectively. Explanation fidelity and perceived usefulness score 4.23 and 4.15. The framework supports semantic alignment, graph-based reasoning, and interpretable recommendation in multimodal information systems.