Traditional recommendation systems rely on a vast amount of granular customer data to provide effective personalised recommendations to users. In a world where customers are becoming increasingly privacy conscious, how can organisations continue to provide effective recommendations to drive engagement and revenue?

Faculty and Springer Nature solved this exact problem, developing a novel reinforcement learning-powered recommendation system to generate effective content recommendations for users without a requirement for personal data.

Technical level: Introductory level/students (some technical knowledge needed)