Multi-purpose technology commercialization recommender system with large-scale Korean language model

Abstract: Large Language Models (LLMs) have demonstrated significant performance improvements and are widely applied across various domains. However, their application in technology commercialization (TC) remains largely unexplored. Effectively utilizing LLMs for TC requires a deep understanding of TC-related documents, which is crucial for accurately classifying relevant sentences and recommending documents that align with consumers’ needs. To address this, we construct a diversified TC-related tagging dataset by defining four new tagging structures and collecting 34,298 Korean tagged sentences. Additionally, we propose a novel multi-purpose TC recommendation algorithm that considers the diverse objectives of consumers, ensuring a more flexible and practical recommendation system. Graphic abstract: (Figure presented.)