Enriching Semantic Profiles into Knowledge Graph for Recommender Systems Using Large Language Models

TL;DR

This paper introduces SPiKE, a novel recommendation model that combines large language models and knowledge graphs to generate and propagate rich user profiles, significantly improving recommendation accuracy.

Contribution

The paper proposes a new approach integrating LLMs and knowledge graphs for user profiling in recommender systems, enhancing profile richness and recommendation performance.

Findings

SPiKE outperforms existing KG- and LLM-based recommenders in real-world tests.

LLMs effectively generate semantic profiles for KG entities.

Knowledge graphs facilitate the propagation of enriched profiles.

Abstract

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.