About me (work)
CV ~ LinkedIn ~ Google Scholar ~ GitHub
Recommender systems shape what billions of people see when they open an app. They can develop a remarkably good understanding of people's taste, which is what I love about working on them. Yet most systems offer little room for users to express their intent: what they want right now, what they want to explore, or how they want their recommendations to change. My goal is to combine taste and intent to make recommender systems more steerable and controllable.
Until September 2026, I was the founding ML scientist at a Paris-based travel tech startup in stealth, where I built personalized voice agents for hotel recommendations. Before that, I was the first employee and Principal Applied Scientist at Albatross AI in Berlin/Zürich, building agentic recommender systems and multimodal search systems at scale.
Previously I was a Senior Applied Scientist at Amazon Music, where I worked on the fascinating topic of personalizing music recommendations. I developed an LLM-based music recommender system called Maestro and was the project's science lead from inception to launch. I also worked on offline policy evaluation and learning how to rank items from different content types.
I completed my PhD at the University of Bath, working on bounded rationality in reinforcement learning with Özgür Şimşek. Before that I worked at the Center for Adaptive Behaviour and Cognition, directed by Gerd Gigerenzer, at the Max Planck Institute for Human Development in Berlin. In my research, I try to inform machine learning models with insights from human decision making research, in particular from the study of simple decision heuristics.
Publications & Talks & Tutorials
Jan Malte Lichtenberg and Aleksandr V. Petrov
Proceedings of the 20th ACM Conference on Recommender Systems (tutorial), 2026
[tutorial materials] [ACM paper] [Google Scholar]
Marta Moscati, Jan Malte Lichtenberg, Davide Abbattista, Antonio De Candia, Laura Boggia, and Matteo Ruffini
DaQuaMRec@RecSys, 2026
[pdf] [Google Scholar]
Jan Malte Lichtenberg and Matteo Ruffini
Proceedings of DaQuaMRec 2025, CEUR Workshop Proceedings, 2026
Keynote presented at DaQuaMRec@RecSys, 2025
[extended abstract] [Google Scholar]
Sequential Transformer-based Recommendation
Jan Malte Lichtenberg, Alessandro De Candia, and Matteo Ruffini
EARL@RecSys, 2025 [pdf] [Google Scholar]
The Robust Beauty of Multinomial Blending
Jan Malte Lichtenberg, Giuseppe Di Benedetto, and Matteo Ruffini
Proceedings of the 18th ACM Conference on Recommender Systems, 2024 [pdf] [Google Scholar]
Alexander Buchholz, Ben London, Giuseppe Di Benedetto, Jan Malte Lichtenberg, Yannik Stein, and Thorsten Joachims
Proceedings of the 47th International ACM SIGIR Conference, 2024 [pdf] [Google Scholar]
A Study of Popularity Bias
Jan Malte Lichtenberg, Alexander Buchholz, and Pola Schwöbel
Gen-IR@SIGIR, 2024 [pdf] [Google Scholar]
in Off-Policy Evaluation
Jan Malte Lichtenberg, Alexander Buchholz, Giuseppe Di Benedetto, Matteo Ruffini, and Ben London
CONSEQUENCES@RecSys, 2023 [pdf] [Google Scholar]
Ben London, Alexander Buchholz, Giuseppe Di Benedetto, Jan Malte Lichtenberg, Yannik Stein, and Thorsten Joachims
CONSEQUENCES@RecSys, 2023 [pdf] [Google Scholar]
a Single Stochastic Logging Policy
Giuseppe Di Benedetto, Alexander Buchholz, Ben London, Matej Jakimov, Yannik Stein, Jan Malte Lichtenberg, Vito Bellini, Matteo Ruffini, and Thorsten Joachims
LERI@RecSys, 2023 [pdf] [Google Scholar]
Jan Malte Lichtenberg
University of Bath, UK, 2023 [pdf] [Google Scholar]
via Quasi-Monte Carlo Sampling
Alexander Buchholz, Jan Malte Lichtenberg, Giuseppe Di Benedetto, Yannik Stein, Vito Bellini, and Matteo Ruffini
SIGIR 2022 Workshop on Reaching Efficiency in Neural Information Retrieval, 2022 [pdf] [Google Scholar]
Jan Malte Lichtenberg and Özgür Şimşek
International Conference on Machine Learning (ICML), 2019
[pdf] [code] [Google Scholar]
Reinforcement Learning
Jan Malte Lichtenberg and Özgür Şimşek
NeurIPS workshop on Biological and Artificial Reinforcement Learning, 2019 [pdf] [Google Scholar]
Jan Malte Lichtenberg and Özgür Şimşek
Imperfect Decision Makers: Admitting Real-World Rationality, PMLR, 2017 [pdf] [Google Scholar]
About this website
This website is made using gatsby.js and react.js. It is hosted on netlify.com. Sidenotes and marginnotes use tufte-css. All visualisations are made using p5.js. Help from Corey Gouker in making p5.js and gatsby.js work well together is much appreciated.