Design and implementation of personalised recommendation system for university library based on GNN and data fusion

Traditional libraries face challenges such as sparse data, cold start issues, and insufficient personalisation in resource recommendations due to their resource-centric service model. To address these issues, this study developed a personalised recommendation model using natural language processing and graph neural networks. The model integrates multi-dimensional data from university students and faculty at the feature layer and analyses the influence of neighbours in different graphs to predict user preferences more accurately. Experiments on Yelp and library datasets demonstrated that the proposed model outperformed six other recommendation systems, achieving the lowest mean absolute error (MAE) of 0.149 and a stable root mean square error (RMSE) of 0.2451. By leveraging social data to enhance user behaviour analysis, this approach alleviates cold start problems and improves recommendation accuracy. The method also indirectly boosts reader satisfaction, offering practical value for personalised book recommendations in libraries.