TY  - EJOU
AU  - Peng, Sony 
AU  - Siet, Sophort 
AU  - Sadriddinov, Ilkhomjon 
AU  - Kim, Dae-Young 
AU  - Park, Kyuwon 
AU  - Park, Doo-Soon 

TI  - Integration of Federated Learning and Graph Convolutional Networks for Movie Recommendation Systems
T2  - Computers, Materials \& Continua

PY  - 2025
VL  - 83
IS  - 2
SN  - 1546-2226

AB  - Recommendation systems (RSs) are crucial in personalizing user experiences in digital environments by suggesting relevant content or items. Collaborative filtering (CF) is a widely used personalization technique that leverages user-item interactions to generate recommendations. However, it struggles with challenges like the cold-start problem, scalability issues, and data sparsity. To address these limitations, we develop a Graph Convolutional Networks (GCNs) model that captures the complex network of interactions between users and items, identifying subtle patterns that traditional methods may overlook. We integrate this GCNs model into a federated learning (FL) framework, enabling the model to learn from decentralized datasets. This not only significantly enhances user privacy— a significant improvement over conventional models but also reassures users about the safety of their data. Additionally, by securely incorporating demographic information, our approach further personalizes recommendations and mitigates the cold-start issue without compromising user data. We validate our RSs model using the open MovieLens dataset and evaluate its performance across six key metrics: Precision, Recall, Area Under the Receiver Operating Characteristic Curve (ROC-AUC), F1 Score, Normalized Discounted Cumulative Gain (NDCG), and Mean Reciprocal Rank (MRR). The experimental results demonstrate significant enhancements in recommendation quality, underscoring that combining GCNs with CF in a federated setting provides a transformative solution for advanced recommendation systems.
KW  - Recommendation systems; collaborative filtering; graph convolutional networks; federated learning framework

DO  - 10.32604/cmc.2025.061166