用大模型增强知识图谱语义，提升推荐系统精准度
Enriching Semantic Profiles into Knowledge Graph for Recommender Systems Using Large Language Models
- 用大模型为知识图谱实体生成语义画像
- 融合大模型与知识图谱，推荐效果超越现有方法
- 适合做个性化推荐系统研究的开发者参考
丰富且信息量大的用户画像对提升推荐质量至关重要，但目前尚无统一构建与使用方法。本文从知识库、偏好指标、影响范围和主体四个维度重新审视推荐系统中的画像方法。认为大语言模型（LLM）擅长从多元知识源中提取压缩后的推理依据，而知识图谱（KG）更适于传播这些画像以扩大影响。基于此，提出新模型SPiKE：首先用LLM为所有KG实体生成语义画像；其次将画像融入知识图谱；最后在训练中对齐LLM与KG表示。实验表明，SPiKE在真实场景下持续优于当前最先进的基于KG和大模型的推荐系统。
原文摘要 · Abstract (English)
Rich and informative profiling to capture user preferences is essential for improving recommendation quality. However, there is still no consensus on how best to construct and utilize such profiles. To address this, we revisit recent profiling-based approaches in recommender systems along four dimensions: 1) knowledge base, 2) preference indicator, 3) impact range, and 4) subject. We argue that large language models (LLMs) are effective at extracting compressed rationales from diverse knowledge sources, while knowledge graphs (KGs) are better suited for propagating these profiles to extend their reach. Building on this insight, we propose a new recommendation model, called SPiKE. SPiKE consists of three core components: i) Entity profile generation, which uses LLMs to generate semantic profiles for all KG entities; ii) Profile-aware KG aggregation, which integrates these profiles into the KG; and iii) Pairwise profile preference matching, which aligns LLM- and KG-based representations during training. In experiments, we demonstrate that SPiKE consistently outperforms state-of-the-art KG- and LLM-based recommenders in real-world settings.
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