Editor’s note: Special issue on Fairness-driven User Behavior Modelling and Analysis for Online Recommendation

This special issue on 'Fairness-driven User Behavior Modelling and Analysis for Online Recommendation' presents a collection of seven meticulously reviewed papers that push the boundaries of fairness in recommendation systems. The articles delve into the intricate relationships between user behavior, algorithmic fairness, and the ethical implications of online recommendations. Highlighting innovative methodologies and real-world applications, this issue is a must-read for anyone seeking to understand and advance the state-of-the-art in fair and equitable recommendation algorithms.