Enriching Semantic Profiles into Knowledge Graph for Recommender Systems Using Large Language Models
This addresses the challenge of enhancing recommender systems for users by providing a novel hybrid approach, though it appears incremental as it builds on existing profiling and knowledge graph techniques.
The paper tackled the problem of constructing and utilizing rich user profiles to improve recommendation quality by proposing SPiKE, a model that uses large language models to generate semantic profiles and integrates them into knowledge graphs, resulting in consistent outperformance of state-of-the-art methods in real-world settings.
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.