Enabling Explainable Recommendation in E-commerce with LLM-powered Product Knowledge Graph
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866912141568638976 |
|---|---|
| author | Wang, Menghan Guo, Yuchen Zhang, Duanfeng Jin, Jianian Li, Minnie Schonfeld, Dan Zhou, Shawn |
| author_facet | Wang, Menghan Guo, Yuchen Zhang, Duanfeng Jin, Jianian Li, Minnie Schonfeld, Dan Zhou, Shawn |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_01837 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enabling Explainable Recommendation in E-commerce with LLM-powered Product Knowledge Graph Wang, Menghan Guo, Yuchen Zhang, Duanfeng Jin, Jianian Li, Minnie Schonfeld, Dan Zhou, Shawn Information Retrieval Machine Learning 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. |
| title | Enabling Explainable Recommendation in E-commerce with LLM-powered Product Knowledge Graph |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2412.01837 |