I’m a research scientist working on the field of machine learning. I have 15+ years of experience in designing algorithms.
My research topics include utilizing deep learning for session-based (sequential) recommendations, evaluation of recommender systems, context-aware tensor factorization on implicit feedback data, and more recently counterfactual learning. I was one of the pioneers of deep learning technology for recommender systems and contributed to evangelizing it in the research community.
For the majority of my career, I have been working on algorithms for recommender systems in the industry. Therefore I have vast experience in a wide variety of recommender systems related topics, and my algorithms are designed with efficient resource utilization, scalability and fast training/inference in mind. Moreover, I have been frequently involved in other aspects of the business - beside research and coding - from strategy to product design to leading innovation, allowing me to see the big picture. I have been leading small machine learning or data science focused teams since 2015.
I received my summa cum laude PhD / MSc / BSc from the Budapest University of Technology.
|Nov 28, 2023
|RecSys 2023 overview
- The Effect of Third Party Implementations on ReproducibilityIn Proceedings of the 17th ACM Conference on Recommender Systems, 2023
- Widespread Flaws in Offline Evaluation of Recommender SystemsIn Proceedings of the 17th ACM Conference on Recommender Systems, 2023
- Recurrent Neural Networks with Top-k Gains for Session-Based RecommendationsIn Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 2018
- Session-based Recommendations with Recurrent Neural NetworksInternational Conference on Learning Representations, 2016
- General factorization framework for context-aware recommendationsData Mining and Knowledge Discovery, 2016First online: 07 May 2015
- Speeding up ALS learning via approximate methods for context-aware recommendationsKnowledge and Information Systems, 2016First online: 14 July 2015
- Fast ALS-Based Tensor Factorization for Context-Aware Recommendation from Implicit FeedbackIn Machine Learning and Knowledge Discovery in Databases, 2012
- ShiftTree: An Interpretable Model-Based Approach for Time Series ClassificationIn Machine Learning and Knowledge Discovery in Databases, 2011