Integration of Federated Learning and Graph Convolutional Networks for Movie Recommendation Systems

초록

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 coldstart 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.

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