LLMs-Augmented Knowledge Graph for Semantic-Aware Recommendation

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Knowledge graph (KG) contains rich factual and item-related information, and improves recommendation accuracy and explainability significantly. However, existing KG-based methods mainly focus on utilizing ID-based structural information, which has shown insurmountable performance bottlenecks. Actually, there is also a large amount of textual information in KG. It becomes a great challenge on how to utilize textual information for higher performance. Recently, large language models (LLMs) have been proved to have powerful semantic understanding capabilities and provide a novel approach for enhancing KG. To this end, this paper proposes an LLMs-augmented knowledge graph (LAKG) model for semantic-aware recommendation, which introduces LLMs to extract semantic features from associated texts to enhance the semantic-aware ability. First, semantic features of users and items are extracted from associated texts by the LLMs. Second, high-order structural features, which are modeled with fine-grained user intents, are further extracted from structural information by a graph attention network. Third, the semantic features and structural features are deeply fused to generate high-quality feature representations of users and items. The augmented feature information is great valuable for recommendation systems to predict the purchasing behavior of users. Experimental results show that the proposed model outperforms existing state-of-the-art baseline models, and the average accuracy improves by 4.46% on three public benchmarks.