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.

翻译：构建丰富且信息量大的用户偏好画像对于提升推荐质量至关重要。然而，关于如何最优地构建和利用此类画像，目前尚未达成共识。为此，我们从四个维度重新审视了近期推荐系统中基于画像的方法：1) 知识库，2) 偏好指示器，3) 影响范围，以及 4) 主体。我们认为，大型语言模型（LLMs）能够有效地从多样化的知识源中提取压缩的推理依据，而知识图谱（KGs）则更适合于传播这些画像以扩展其覆盖范围。基于这一见解，我们提出了一种新的推荐模型，称为 SPiKE。SPiKE 包含三个核心组件：i) 实体画像生成，使用 LLMs 为所有 KG 实体生成语义画像；ii) 画像感知的 KG 聚合，将这些画像整合到 KG 中；以及 iii) 成对画像偏好匹配，在训练过程中对齐基于 LLM 和基于 KG 的表征。在实验中，我们证明 SPiKE 在现实场景中持续优于最先进的基于 KG 和基于 LLM 的推荐模型。