doc. Ing. Pavel Kordík, Ph.D.

Theses

Dissertation theses

Algorithms and architectures of recommender systems

Level
Topic of dissertation thesis
Topic description

In the recommendation systems, we are currently focusing research on a few open problems that have deep theoretical underpinnings, but whose solutions also have very concrete practical applications. We are exploring the utilization of deep neural networks to reduce the cold start problem of recommender systems, the design of transformers to predict shopping baskets. In the field of general machine learning, we focus on reinforced learning to optimize longer-term metrics such as user satisfaction, on transfer learning methods to incorporate new referral databases, and on using AutoML to optimize the architecture and hyperparameters of recommender systems.