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

作　　者：Sony Peng Sophort Siet Ilkhomjon Sadriddinov Dae-Young Kim Kyuwon Park Doo-Soon Park 

机构地区：[1]Department of Software Convergence,Soonchunhyang University,Asan,31538,Republic of Korea [2]Department of Computer Software and Engineering,Soonchunhyang University,Asan,31538,Republic of Korea [3]AI⋅SW Education Institute,Soonchunhyang University,Asan,31538,Republic of Korea

出　　处：《Computers, Materials & Continua》2025年第5期2041-2057,共17页计算机、材料和连续体(英文)

基　　金：funded by Soonchunhyang University,Grant Numbers 20241422;BK21 FOUR(Fostering Outstanding Universities for Research,Grant Number 5199990914048).

摘　　要：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 themodel to learn fromdecentralized datasets.This not only significantly enhances user privacy—a significant improvement over conventionalmodels 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 openMovieLens 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.

关 键 词：Recommendation systems collaborative filtering graph convolutional networks federated learning framework 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]