Modeling semantic representation with LLM-enhanced for knowledge-aware recommendation

Knowledge graph-based recommendation systems utilize structured entity and relation representations to better model user preferences. However, many traditional approaches rely primarily on ID-based data and often overlook textual information associated with items and relations, leading to limited semantic understanding. While recent approaches have begun incorporating large language models (LLMs), most focus solely on enhancing relational embeddings and fail to fully exploit the semantic extraction capabilities of LLMs. To address these limitations, we propose LLMKnowRec, a novel LLM-enhanced, knowledge-aware recommendation framework designed to improve the semantic modeling of knowledge graphs. Our approach integrates the powerful language understanding abilities of LLMs with traditional ID-based recommendation by introducing an LLM-based embedding generator. This generator produces semantically rich embeddings using textual descriptions of user profiles and knowledge graph relations. Building on this, we further introduce a semantic user intent modeling module, which leverages LLMs to aggregate multiple intent signals into comprehensive, semantically enriched intent embeddings. Additionally, we develop a relational intent-aware aggregation scheme that effectively combines higher-order representations, capturing both relational structures and user intent, thus enhancing the overall semantic understanding of users and items. Experimental conducted on three public benchmark datasets demonstrate that LLMKnowRec consistently outperforms state-of-the-art methods. Specifically, our model achieves improvements of up to 12.92%, 19.27%, and 8.23% in NDCG@10, and up to 13.41%, 15.62%, and 23.55% in Recall@10 across the three datasets, respectively. These results demonstrate the effectiveness and practical potential of our proposed method. The implementation code is publicly available at: https://github.com/liujianfang2021/LLMKnowRec.