Bridging the Semantic Gap in Industry–Academia Collaboration: A Two-Stage RAG System for Intelligent Expert Recommendation

Aligning industrial technological demands with academic expertise is critical for effective technology transfer. However, existing Expert Recommendation Systems (ERS) are frequently hindered by a “semantic gap” arising from terminological discrepancies between industry and academia, alongside a reliance on rigid classification taxonomies. To address these limitations, this paper proposes an automated expert finding framework that integrates Large Language Models (LLMs) with a hierarchical Retrieval-Augmented Generation (RAG) mechanism. Initially, we employ LLMs for the unsupervised extraction of research domains and technical keywords from heterogeneous multi-source data. To mitigate terminological diversity, we introduce a vector clustering-based Semantic Normalization module. By mapping diverse keyword variants into unified “Concept Clusters,” this module reduces vocabulary sparsity by 98%. These organized clusters are structured into a “Semantic Tree” to support a hierarchical RAG strategy, enabling a coarse-to-fine retrieval process from broad disciplinary domains down to specific technical achievements. In this paper, RAG refers to a retrieval-augmented expert recommendation workflow, in which the retrieved achievements and expert evidence are used as grounded context for generating an explanatory recommendation report. Evaluations on a real-world dataset show that the framework achieves a Precision@5 of 78.4% and a Recall@10 of 81.2%, outperforming flat vector retrieval baselines by over 20% in precision. Furthermore, hierarchical domain pruning significantly reduces computational overhead, decreasing average query latency by a factor of three (to 115.8 ms). These results demonstrate that the proposed system effectively bridges the industry–academia semantic gap, providing a scalable and accurate solution for expert recommendation.