Enabling Explainable Recommendation in E-commerce with LLM-powered Product Knowledge Graph

Autor:	Wang, Menghan, Guo, Yuchen, Zhang, Duanfeng, Jin, Jianian, Li, Minnie, Schonfeld, Dan, Zhou, Shawn
Rok vydání:	2024
Předmět:	Computer Science - Information Retrieval Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	How to leverage large language model's superior capability in e-commerce recommendation has been a hot topic. In this paper, we propose LLM-PKG, an efficient approach that distills the knowledge of LLMs into product knowledge graph (PKG) and then applies PKG to provide explainable recommendations. Specifically, we first build PKG by feeding curated prompts to LLM, and then map LLM response to real enterprise products. To mitigate the risks associated with LLM hallucination, we employ rigorous evaluation and pruning methods to ensure the reliability and availability of the KG. Through an A/B test conducted on an e-commerce website, we demonstrate the effectiveness of LLM-PKG in driving user engagements and transactions significantly. Comment: This paper was accepted by The First International OpenKG Workshop Large Knowledge-Enhanced Models @IJCAI 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.01837 Zobrazit plný text záznamu View this record from Arxiv