Integrating Stochastic Models into News Recommender Systems: A Comparative Study with Hybrid Approaches - Veloso, Bráulio M.; Ferreira, Anderson A. (Anais do Simpósio Brasileiro de Banco de Dados (SBBD); 2026: Anais do XLI Simpósio Brasileiro de Bancos de Dados, 2026)
Recommender systems are essential for handling information overload in online newspapers, where the lack of explicit feedback and the need for real-time updates limit computationally expensive methods. This work investigates how contextualization based on stochastic models that capture the sequential and uncertain nature of reading behavior improves news recommender systems. Using real access logs and a simulation environment, we evaluate these models across multiple metrics. The results show that stochastic models outperform traditional approaches, especially in personalization and diversity, and that combining them with hybrid methods yields the best performance.