A Contextual-Enhanced LightGCN for Movie Recommendation Systems

Abstract

In the context of the digital information explosion, recommender systems have been widely deployed to mitigate information overload through personalized information filtering. Traditional methods, such as collaborative filtering and content-based filtering, established the foundation for this field. Recently, advancements in deep learning particularly Graph Convolutional Network-based models such as LightGCN have demonstrated superior effectiveness in learning user and item representations from high-order interaction graph structures. To alleviate this limitation, this paper proposes a recommendation method titled Contextual-enhanced LightGCN[1]. This approach enhances the LightGCN model by simultaneously leveraging movie content features and user demographic information to aggregate information during the training process. Our ablation study further clarifies that while item content features enhance recommendation quality, the simple integration of user demographics introduces noise and degrades performance. Comprehensive experiments on MovieLens 100K and MovieLens 1M datasets, averaged over three independent runs, indicate that CF-LightGCN consistently outperforms the LightGCN baseline, achieving a Recall@20 improvement of up to 1.5%.