Recommender Systems with Evolutionary Algorithms: Many­-Objective Optimization for Large­-Scale Music Recommendation: A demonstration showing the reliability of serving users recommendations with trade-­off for large music collections, by leveraging diverse Recommender Systems and Evolutionary Algorithms

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...

Tensor Network Kalman Filter for Large-Scale MIMO Systems: With Application to Adaptive Optics

For large-scale system with tens of thousands of states and outputs the computation in the conventional Kalman filter becomes time-consuming such that Kalman filtering in large-scale real-time application is practically infeasible. A possible mathematical framework to lift the curse of dimensionality is to lift the problem in higher dimensions...