KLLMs4Rec: Knowledge graph-enhanced LLMs sentiment extraction for personalized recommendations

Recommendation algorithms typically leverage auxiliary information such as user reviews and knowledge graphs to enhance algorithm performance, thereby alleviating data sparsity and cold start issues. Recently, researchers have increasingly employed large language models, which boast powerful natural language understanding capabilities, to further improve recommendation systems. However, these models often suffer from hallucination problems. Moreover, integrating heterogeneous information, such as reviews and knowledge graphs, can introduce new noise, potentially impairing recommendation performance. Knowledge graphs, as tightly organized structured knowledge bases, can assist in addressing the hallucination problem and heterogeneous information fusion problem of LLMs. To effectively address the aforementioned issues, we propose the Knowledge Graph-Enhanced Large Language Model Sentiment Extraction for the Personalized Recommendation Model (KLLMs4Rec). It aims to solve the LLMs hallucination problem and the noise problem caused by the fusion of heterogeneous information in recommender systems, and provide users with more accurate, diverse and novel personalized recommendations. To address the hallucination problem when extracting user sentiments from reviews with LLMs, we designed a knowledge graph-enhanced prompt template. It is worth noting that this scheme also solves the noise issue of heterogeneous information fusion. Additionally, to further expand user preferences extracted from reviews, this paper proposes a new hierarchical sentiment attention graph convolutional network, which utilizes three sentiment weight schemes to propagate user personalized preferences on the knowledge graph. Extensive experiments on the Movielens-20 m, Amazon-book, and Yelp datasets demonstrate that our model surpasses current leading methods while effectively addressing the hallucination problem of LLMs and the noise problem of heterogeneous information fusion.