Neighborhood-Aware Unlearning for Generative Recommendation
Pierre Lubitzsch, Yubao Tang, Maarten de Rijke, Sebastian Schelter. (2026). "Neighborhood-Aware Unlearning for Generative Recommendation." arXiv preprint arXiv:2610.27232
CV
Education
- Ph.D. in Computer Science, Technische Universität Berlin / BIFOLD, 2025 – present
- Supervisors: Prof. Dr.-Ing. Sebastian Schelter (TU Berlin) and Prof. Dr. Maarten de Rijke (UvA)
- Topic: Machine Unlearning for Recommender Systems
- M.Sc. in Computer Science, Freie Universität Berlin, 2021 – 2024
- Thesis: Neural Network Post-Filters for Video Coding (in cooperation with Fraunhofer HHI)
- B.Sc. in Computer Science (grade 1.4), Freie Universität Berlin, 2018 – 2021
- Abitur (grade 1.7), Werner-von-Siemens-Gymnasium Berlin, 2012 – 2018
- Advanced courses: Computer Science, Mathematics
Work Experience
- Feb 2025 – present: PhD Student
- BIFOLD – Berlin Institute for the Foundations of Learning and Data / TU Berlin, Berlin, Germany
- Research on Machine Unlearning for Recommender Systems
- Supervisors: Sebastian Schelter and Maarten de Rijke
- Dec 2022 – Dec 2024: Student Assistant
- Fraunhofer Heinrich Hertz Institute (HHI), Berlin, Germany
- Work on neural network coding for video compression
- Wrote master’s thesis: Neural Network Post-Filters for Video Coding
- Aug 2020 – Oct 2020: Web Development Intern
- writeaguide, Berlin, Germany
- Fullstack web development internship
Skills
- Machine Learning & Deep Learning
- PyTorch, scikit-learn
- Recommender Systems & Machine Unlearning
- Video Coding & Neural Network Post-Processing
- Programming: Python, C, C++
- Web Development: HTML, CSS, JavaScript (Fullstack)
Languages
- German (Native)
- English (Professional working proficiency)
- Spanish (Limited working proficiency)
Academic Service
Journals
- ACM Transactions on Information Systems (TOIS)
- IEEE Transactions on Knowledge and Data Engineering (TKDE)
Conferences
- SIGIR 2026 (Resources Track)
Publications
ERASE - A Real-World Aligned Benchmark for Unlearning in Recommender Systems
Pierre Lubitzsch, Maarten de Rijke, Sebastian Schelter. (2026). "ERASE - A Real-World Aligned Benchmark for Unlearning in Recommender Systems." In Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '26), pp. 3310–3318.
Towards a Real-World Aligned Benchmark for Unlearning in Recommender Systems
Pierre Lubitzsch, Olga Ovcharenko, Hao Chen, Maarten de Rijke, Sebastian Schelter. (2025). "Towards a Real-World Aligned Benchmark for Unlearning in Recommender Systems." FAccTRec@RecSys 2025
Talks
Conference Proceeding talk 3 on Relevant Topic in Your Field
Conference proceedings talk at Testing Institute of America 2014 Annual Conference, Los Angeles, CA, USA
Talk 2 on Relevant Topic in Your Field
Talk at London School of Testing, London, UK
Tutorial 1 on Relevant Topic in Your Field
Tutorial at UC-Berkeley Institute for Testing Science, Berkeley, CA, USA
Talk 1 on Relevant Topic in Your Field
Talk at UC San Francisco, Department of Testing, San Francisco, CA, USA
Teaching
Projects
- Classification of reflective sentences (Apr–Jul 2022)
- NLP project within the PetraKIP research project at FU Berlin
- Annotation and classification of sentences according to the cognitive Gibbs cycle using machine learning
- Digit recognition neural net (Dec 2019)
- Neural network for digit classification trained on MNIST (~97.5% accuracy), written in Python
- Joodle Dump (Mar 2019)
- Clone of Doodle Jump written in C using the SDL2 graphics library (group project, 6 members)
- Mandelbrot Set (Jul 2018)
- Visualization of the Mandelbrot set in C++ using the SFML graphics library
- Pong (Nov–Dec 2017)
- Clone of Pong written in Python