用大模型构建可解释的商品推荐知识图谱，提升电商转化率
Enabling Explainable Recommendation in E-commerce with LLM-powered Product Knowledge Graph
- 将大模型能力蒸馏到商品知识图谱中，实现可解释推荐
- 在真实电商场景的A/B测试中显著提升用户参与和交易量
- 通过严格评估与过滤降低大模型幻觉风险，保障知识图谱可靠性
如何利用大语言模型在电商推荐中的优势已成为热门话题。本文提出LLM-PKG，一种高效方法：将大模型的知识蒸馏到商品知识图谱（PKG）中，并基于此提供可解释推荐。具体而言，先通过精心设计的提示词引导大模型构建PKG，再将模型输出映射到实际企业商品。为缓解大模型幻觉带来的风险，采用严格的评估与剪枝方法，确保知识图谱的可靠性与可用性。在某电商平台进行的A/B测试表明，LLM-PKG能显著提升用户参与度与交易量。
原文摘要 · Abstract (English)
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.
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