Michal focuses on recommender systems, machine learning, user modeling, and information retrieval. His research is focused on predictive modeling and customer behavior (e.g., churn prediction, next-item recommendation), as well as content-based adaptive models. He specifically addresses recommender system in e-commerce, for instance the complementary products recommendations. As a teacher, he has supervised more than 50 Bachelor’s and Master’s theses. To support the community, he has served as a reviewer or/and program committee member at several international conferences, such as RecSys, SIGIR, WWW, ADBIS, Hypertext, UMAP and SMAP. He is a reviewer for several international journals in Springer, IEEE, ACM, Taylor&Francis and Inderscience.