用大模型统一提取知识图谱中的通用与特定主题，提升推荐系统互操作性。
Topic-Aware Knowledge Graph with Large Language Models for Interoperability in Recommender Systems
- 通过大模型从侧信息中迭代提取通用主题
- 结合上下文信息提取具体主题，显著提升推荐效果
- 自研精炼算法解决同义主题问题，适合跨系统推荐场景
知识图谱在推荐系统中被广泛用于缓解数据稀疏和冷启动问题。近年来，大语言模型（LLMs）为处理知识图谱中的侧信息和上下文信息提供了新可能。然而，由于需领域专家干预且系统特性差异，跨系统的一致集成仍具挑战。为此，我们提出一种一致方法：利用大模型从侧信息中迭代提取通用主题，再结合上下文信息提取具体主题。针对具体主题生成过程中产生的同义主题问题，设计精炼算法进行有效处理。通用主题捕捉多样商品的广义特征，具体主题强调细节属性，从而更全面理解物品语义与用户偏好。实验表明，该方法在多种知识图谱上均显著提升推荐性能。
原文摘要 · Abstract (English)
The use of knowledge graphs in recommender systems has become one of the common approaches to addressing data sparsity and cold start problems. Recent advances in large language models (LLMs) offer new possibilities for processing side and context information within knowledge graphs. However, consistent integration across various systems remains challenging due to the need for domain expert intervention and differences in system characteristics. To address these issues, we propose a consistent approach that extracts both general and specific topics from both side and context information using LLMs. First, general topics are iteratively extracted and updated from side information. Then, specific topics are extracted using context information. Finally, to address synonymous topics generated during the specific topic extraction process, a refining algorithm processes and resolves these issues effectively. This approach allows general topics to capture broad knowledge across diverse item characteristics, while specific topics emphasize detailed attributes, providing a more comprehensive understanding of the semantic features of items and the preferences of users. Experimental results demonstrate significant improvements in recommendation performance across diverse knowledge graphs.
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