打破壁垒：利用大语言模型通过推理知识图谱构建工业推荐系统
Breaking the Barrier: Utilizing Large Language Models for Industrial Recommendation Systems through an Inferential Knowledge Graph
AI总结：
本文提出LLM-KERec系统，通过大语言模型构建互补知识图谱并引入互补召回与E-E-I权重决策模型，以捕捉用户意图转变、适应新物品并提升工业推荐效果。
摘要：
推荐系统被广泛应用于电子商务网站和在线平台，以解决信息过载问题。然而，现有系统主要依赖历史数据和用户反馈，难以捕捉用户意图的转变。最近，基于知识库（Knowledge Base, KB）的模型被提出以引入专家知识，但它们难以适应新物品和不断变化的电子商务环境。为应对这些挑战，我们提出了一种新颖的基于大语言模型的互补知识增强推荐系统（LLM-KERec）。该系统引入了一个实体抽取器，从物品和用户信息中提取统一的概念术语。为了提供具有成本效益且可靠的先验知识，系统基于实体流行度和特定策略生成实体对。大语言模型判断每个实体对中的互补关系，从而构建互补知识图谱。此外，一个新的互补召回模块和一个实体-实体-物品（Entity-Entity-Item, E-E-I）权重决策模型利用真实的互补曝光-点击样本对排序模型的评分进行细化。在三个工业数据集上进行的大量实验表明，与现有方法相比，我们的模型取得了显著的性能提升。此外，详细分析显示，LLM-KERec通过推荐互补物品提升了用户的消费热情。总之，LLM-KERec通过引入互补知识并利用大语言模型捕捉用户意图转变、适应新物品，并在不断演变的电子商务环境中提升推荐效率，解决了传统推荐系统的局限性。
英文摘要：
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 (LLM-KERec). It introduces an entity extractor that extracts unified concept terms from item and user information. To provide cost-effective and reliable prior knowledge, entity pairs are generated based on entity popularity and specific strategies. The large language model determines complementary relationships in each entity pair, constructing a complementary knowledge graph. Furthermore, a new complementary recall module and an Entity-Entity-Item (E-E-I) weight decision model refine the scoring of the ranking model using real complementary exposure-click samples. Extensive experiments conducted on three industry datasets demonstrate the significant performance improvement of our model compared to existing approaches. Additionally, detailed analysis shows that LLM-KERec enhances users' enthusiasm for consumption by recommending complementary items. In summary, LLM-KERec addresses the limitations of traditional recommendation systems by incorporating complementary knowledge and utilizing a large language model to capture user intent transitions, adapt to new items, and enhance recommendation efficiency in the evolving e-commerce landscape.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
