PhD themes 2024: Recommender and Adaptive Web-based Systems

Supervising team: Michal Kompan (supervisor, KInIT), Peter Brusilovsky (University of Pittsburgh), Branislav Kveton (Google Research), Peter Dolog (Aalborg University),
Keywords: personalised recommendation, biases, machine learning, user model, fairness, off-policy   

The recommender systems are an integral part of almost every modern Web application. Personalized, or at least adaptive, services have become a standard that is expected by the users in almost every domain (e.g., news, chatbots, social media, or search).

Obviously, personalization has a great impact on the everyday life of hundreds of million users across many domains and applications. This results in a major challenge – to propose methods that are not only accurate but also trustworthy and fair. Such a goal offers plenty of research opportunities in many directions:

  • Novel machine learning approaches for adaptive and recommender systems
  • Trustworthy recommendation methods for multi-objective and multi-stakeholder environments
  • Explaining recommendations
  • Fairness and justice in recommendations
  • Biases in the recommendations

There are several application domains where these research problems can be addressed, e.g., search, e-commerce, social networks, news, and many others.

Relevant publications:

  • V. Bogina, T. Kuflik, D. Jannach, M. Bielikova, M. Kompan, C. Trattner. Considering temporal aspects in recommender systems: a survey. User Modeling and User-Adapted Interaction, 1-39, 2022. 
  • I. Srba, R. Moro, M. Tomlein, B. Pecher, J. Simko, E. Stefancova, M. Kompan, A. Hrckova, J. Podrouzek, A. Gavornik, and M. Bielikova. Auditing YouTube’s Recommendation Algorithm for Misinformation Filter Bubbles. ACM Trans. Recomm. Syst. 1, 1, Article 6, March 2023. 

The research will be performed at the Kempelen Institute of Intelligent Technologies (KInIT, in Bratislava in cooperation with industrial partners or researchers from highly respected research units from abroad. A combined (external) form of study and full employment at KInIT is expected. 

Supervising team

Michal Kompan Lead researcher, KInIT More info
Close Michal Kompan Lead researcher, KInIT

Michal Kompan is an expert researcher at KInIT. He 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. Michal serves as a reviewer or/and program committee member at several international conferences, such as RecSys, SIGIR, WWW, ADBIS, Hypertext, UMAP and SMAP.

Peter Brusilovsky Professor, University of Pittsburgh, USA More info
Close Peter Brusilovsky Professor, University of Pittsburgh, USA

Peter Brusilovsky is a Professor at the School of Computing and Information, University of Pittsburgh, where he directs the Personalized Adaptive Web Systems (PAWS) lab. His research is focused on user-centered intelligent systems in the areas of adaptive learning, recommender systems, and personalized health. He is a recipient of Alexander von Humboldt Fellowship, NSF CAREER Award, and Fulbright-Nokia Distinguished Chair. Peter served as the Editor-in-Chief of IEEE  Trans. on Learning Technologies, and a program chair for several conferences including RecSys.

Branislav Kveton Principal Scientist, Amazon’s lab, USA More info
Close Branislav Kveton Principal Scientist, Amazon’s lab, USA

Branislav Kveton is a Principal Scientist at Amazon’s lab in Berkeley. He proposes, analyzes, and applies algorithms that learn incrementally, run in real time, and converge to near-optimal solutions as they learn. He made several fundamental contributions to the field of multi-armed bandits. His earlier work focused on structured bandit problems with graphs, submodularity, and low-rank matrices, and ranked lists. His recent work focuses on making bandit algorithms practical

Peter Dolog Associate Professor, Aalborg University, Denmark More info
Close Peter Dolog Associate Professor, Aalborg University, Denmark

Peter Dolog is an Associate Professor at the Department of Computer Science, Aalborg University, Denmark. His current research interests include machine learning and data mining in the areas of user behavior analysis and prediction, recommender systems, preference learning, and personalization. Peter is a senior member of ACM, served as a senior program commitee member of AI related conferences as well as a general chair of UMAP, HT and Web Engineering conferences.