Using Recommender Systems with Evolutionary Algorithms is an extremely niche domain. It holds the key to enabling new user interaction designs, where users can effectively configure their experience with a Recommender System. This thesis answers important questions about the scientific aspects of its application to large-scale data through a rigorous experimental design. We use one of the largest publicly accessible music listening histories dataset to analyse if the methodology works well for large real-world tasks. The dataset has been used to simulate various real-world scenarios for the experimental design.

The methodology fuses the recommendations generated by an unspecified number of recommenders. In this study, we have used three recommenders, which have specialised goals in terms of user-centric metrics. We use three different Evolutionary Algorithms to analyse the capability of different EA strategies for generating a near-optimal set of trade-offs on user-centric metrics. We have performed elaborate qualitative and quantitative analyses of the system to understand how various aspects of the system affect the final set of solutions.