This talk presents algorithmic approaches to the Combinatorial Contextual Bandit problem, aimed at enhancing efficiency and practicality in sequential learning and decision-making. Through real-world applications such as content recommendation and healthcare, the talk demonstrates the model’s theoretical guarantees and strong empirical performance. The findings offer a robust framework for optimizing user experience and real-time decision-making based on streaming data.