A book recommendation algorithm based on neural network models

This paper proposes a book recommendation algorithm that integrates knowledge graphs with collaborative filtering based on artificial intelligence neural network models, providing personalized book recommendations for users. Initially, the collaborative filtering technique is used to extract features from user behavior data, exploring similarities among users and their preference patterns towards items. By incorporating semantic information of books through knowledge graph technology, entities and relationships are embedded into a continuous vector space, capturing the structural and semantic information within the graph. Subsequently, a neural network model is employed to achieve a high degree of integration between collaborative filtering features and knowledge graph features. Experimental results demonstrate that the neural network model-based integration algorithm can fully leverage the advantages of both collaborative filtering and knowledge graphs, showing enhanced effectiveness and stability in book recommendation tasks.