A Hybrid Graph Convolutional Networks (GCN)-Collaborative Filtering Recommender System

This study proposes a hybrid recommendation system that integrates Graph Convolutional Networks (GCN) and collaborative filtering to improve the accuracy and performance of university library book recommendation systems. The goal is to develop a comprehensive evaluation method for assessing the effectiveness of recommendation algorithms in university libraries. A combination of GCN and collaborative filtering algorithms was employed to enhance recommendation accuracy. GCN was used to capture complex relationships in user data, while collaborative filtering focused on user preferences. Performance evaluation was conducted using a set of functional indicators, and the system was tested using real library data. The evaluation metrics included Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and evaluation time. The GCN-based evaluation model significantly outperformed traditional methods. It achieved a MAPE of 0.7597 and an RMSE of 0.3775, both superior to BP, CNN, and DBN algorithms. In terms of evaluation time, the GCN algorithm showed moderate performance (0.44s) compared to BP (0.32s), but better than DBN (0.87s) and CNN (0.67s). These results demonstrate the robustness and efficiency of the GCN model in predicting library recommendations. The proposed hybrid system effectively improves the accuracy and evaluation of university library recommendation systems. The GCN-based model outperformed other methods in terms of error rates and evaluation time, making it a valuable tool for enhancing personalized recommendations in library systems. Future research will focus on optimizing the computational efficiency of the GCN model.

@article{Zhang2025,
  title     = {A Hybrid Graph Convolutional Networks (GCN)-Collaborative Filtering Recommender System},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {5},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Qingfeng Zhang},
  doi       = {10.14569/IJACSA.2025.0160552},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160552}
}