Acknowledgments¶
We extend our sincere gratitude to all individuals and organizations who contributed to the success of this research project.
๐ Supervision & Guidance¶
- Prof. Mikoลaj Morzy: Our supervisor, whose expertise in data mining and recommendation systems provided invaluable direction. His insights into algorithm evaluation and ethical considerations were fundamental to shaping this work.
- Prof. Jerzy Nawrocki: For the "Pre-diploma and Diploma seminar" and the materials, which provided essential insights and foundational knowledge.
๐ ๏ธ Foundational Works & Resources¶
This project builds upon the work of the open-source community. We are particularly grateful for:
- Microsoft Recommenders Team: For their comprehensive library that served as the foundation for our collaborative filtering implementations.
- PGPR Authors: For making their policy gradient recommendation framework available, enabling our reinforcement learning experiments.
- Kaggle Community & MovieLens Project: For providing the high-quality, accessible datasets that were crucial for our evaluation.
๐ป Tools & Infrastructure¶
The development and documentation of this project were made possible by several key tools:
- MLflow: For experiment tracking and ensuring reproducible research.
- Material for MkDocs: For enabling this professional documentation.
- GitHub: For hosting our open-source implementation and enabling collaborative development.
- Cookiecutter Data Science: For the project template that provided a solid foundation for reproducible research.
๐๏ธ Institutional Support¶
We thank Poznan University of Technology for providing the academic environment, computational resources, and institutional support necessary for conducting this research.
๐จโ๐ฉโ๐งโ๐ฆ Personal Acknowledgments¶
Finally, we extend our heartfelt thanks to our families and friends for their unwavering support, encouragement, and understanding throughout this intensive research period.