Multi-Value-Product Retrieval-Augmented Generation for Industrial Product Attribute Value Identification
This work solves a domain-specific problem for e-commerce platforms by improving product search and recommendation, but it is incremental as it builds on existing paradigms like retrieval-augmented generation.
The paper tackles product attribute value identification in e-commerce by proposing MVP-RAG, which combines retrieval, generation, and classification to address challenges like out-of-distribution values and cascading errors, achieving better performance than state-of-the-art baselines in real-world deployment.
Identifying attribute values from product profiles is a key task for improving product search, recommendation, and business analytics on e-commerce platforms, which we called Product Attribute Value Identification (PAVI) . However, existing PAVI methods face critical challenges, such as cascading errors, inability to handle out-of-distribution (OOD) attribute values, and lack of generalization capability. To address these limitations, we introduce Multi-Value-Product Retrieval-Augmented Generation (MVP-RAG), combining the strengths of retrieval, generation, and classification paradigms. MVP-RAG defines PAVI as a retrieval-generation task, where the product title description serves as the query, and products and attribute values act as the corpus. It first retrieves similar products of the same category and candidate attribute values, and then generates the standardized attribute values. The key advantages of this work are: (1) the proposal of a multi-level retrieval scheme, with products and attribute values as distinct hierarchical levels in PAVI domain (2) attribute value generation of large language model to significantly alleviate the OOD problem and (3) its successful deployment in a real-world industrial environment. Extensive experimental results demonstrate that MVP-RAG performs better than the state-of-the-art baselines.