Exploiting distribution-based confidence integration in graph neural network recommenders

Recommender systems assist users in navigating information-rich environments by delivering personalized content. While model-based collaborative filtering approaches, such as matrix factorization (MF) and graph neural networks (GNN), are widely adopted, the inherent uncertainty in user preferences and the sparsity of data can lead to unreliable predictions. Confidence estimation has emerged as a strategy to quantify prediction reliability, yet its integration remains unexplored in GNN-based models, and prior methods often degrade accuracy or suffer from convergence issues. This study benchmarks four prominent confidence-aware models—OrdRec, Confidence-aware Probabilistic Matrix Factorization, Confidence-aware Bayesian Probabilistic Matrix Factorization, and Lightweight Beta Distribution across three public datasets: Amazon Movies and TVs, Jester Joke, and Movie Lens. We evaluate these models in terms of rating accuracy, ranking quality, and confidence estimate quality. In addition, we propose a novel confidence-integrated model based on a deep graph attention network architecture. Experimental results reveal that while distribution-based confidence methods are highly sensitive to dataset characteristics and harm accuracy, the proposed method demonstrates consistent performance across all datasets and metrics, outperforming prior distribution-based models. Nevertheless, challenges remain in aligning confidence estimates with prediction error.