Enhancing E-commerce Representation Learning via Hypergraph Contrastive Learning and Interpretable LLM-Driven Analysis

E-commerce has experienced significant growth recently, generating vast amounts of data on user preferences, interactions, and purchase patterns.Effectively modeling and representing users and products in these online ecosystems is crucial for various applications.However, existing approaches for e-commerce representation learning face several limitations: (i) they primarily consider user behavior patterns while ignoring rich group-wise relationships; (ii) some works focus on either user or product representations, failing to learn both simultaneously; (iii) results on downstream tasks are generated by "black box" models, making it difficult to interpret prediction results.To address these challenges, we propose RelationExpert, a general e-commerce representation learning