Integrating Confidence into Embedding-Based Models for Learn-to-Rank in Recommender Systems - Pires, Joel Machado; Durão, Frederico Araújo (Anais Estendidos do Simpósio Brasileiro de Banco de Dados (SBBD); 2026: Anais Estendidos do XLI Simpósio Brasileiro de Bancos de Dados, 2026)
Recommender systems produce uncertain predictions due to sparse and noisy user interactions, yet existing confidence estimation methods are mostly limited to matrix factorization models and remain underexplored for graph neural networks and learning-to-rank. This work presents a unified study of confidence estimation in recommender systems by benchmarking prior distribution-based approaches, analyzing their calibration and impact on predictive performance, and proposing probabilistic graph-based models and an uncertainty-aware pairwise ranking formulation based on the Gaussian cumulative distribution function. Experiments on multiple public datasets show that the proposed methods preserve or improve recommendation quality while producing confidence estimates that strongly correlate with prediction errors, providing reliable uncertainty quantification and a general framework for confidence-aware recommendation.