Research on recommendation system based on knowledge graph and large language model enhancement

The core of recommendation system is user and commodity, the relationship between user and commodity can be abstracted as graph structure, so graph neural network has been widely used in recommendation field. However, graph-based recommendation interaction data is sparse, relying heavily on numbering information and graph structure information, ignoring valuable text information related to user and commodity, and less representational information. At the same time, there are some noise and bias in the implicit feedback data, which bring challenges for recommendation system to understand user behavior and preferences. To solve these problems, this paper proposes a recommendation system based on knowledge graph and large language model enhancement. Knowledge graphs can provide structured information about commodities, enabling models to learn potential relationships between commodities and understand user behavior and preferences. Large language models have excellent ability to generate and understand, and can use prompt engineering techniques to deeply analyze and mine text information, and deduce the features of commoditiy and user portraits. The features enhanced by these auxiliary information are encoded, and the representations are enhanced to align with the ID representations obtained by the graph neural network to complete the downstream recommendation task. The experimental results show that the proposed system can help to comprehensively characterize users and commodities, and has good performance.