Breaking the Barrier: Utilizing Large Language Models for Industrial Recommendation Systems through an Inferential Knowledge Graph

arXiv:2402.13750v1 Announce Type: cross
Abstract: Recommendation systems are widely used in e-commerce websites and online platforms to address information overload. However, existing systems primarily rely on historical data and user feedback, making it difficult to capture user intent transitions. Recently, Knowledge Base (KB)-based models are proposed to incorporate expert knowledge, but it struggle to adapt to new items and the evolving e-commerce environment. To address these challenges, we propose a novel Large Language Model based Complementary Knowledge Enhanced Recommendation System …

abstract arxiv breaking commerce cs.ai cs.cl cs.ir data e-commerce feedback graph historical data industrial information knowledge knowledge base knowledge graph language language models large language large language models making online platforms overload platforms recommendation recommendation systems systems through transitions type user feedback websites