How to Cite This Work¶

This page provides standardized formats for citing the QPERA project, its associated Master's Thesis, and the foundational libraries it builds upon.

1. Primary Citation¶

If you use this project, its findings, or its code in your research, please cite the Master's Thesis.

BibTeX Format¶

@mastersthesis{podsadna_chwilkowski2025qpera,
  title     = {Quality of Personalization, Explainability and Robustness of Recommendation Algorithms},
  author    = {Podsadna, Julia and Chwi{\l}kowski, Bartosz},
  year      = {2025},
  school    = {Poznan University of Technology},
  address   = {Poznan, Poland},
  supervisor= {Morzy, Miko{\l}aj},
  type      = {Master's Thesis},
  note      = {Faculty of Computing and Telecommunications},
  url       = {https://github.com/PUT-RecSys-Research/qpera-thesis}
}

2. Software Citation¶

To cite the software implementation directly, you can use the following format.

BibTeX Format¶

@software{qpera_software_2025,
  author    = {Podsadna, Julia and Chwi{\l}kowski, Bartosz},
  title     = {QPERA: A Project for Evaluating Quality, Personalization, Explainability, and Robustness of Recommendation Algorithms},
  year      = {2025},
  publisher = {GitHub},
  version   = {1.0.0},
  url       = {https://github.com/PUT-RecSys-Research/qpera-thesis},
  license   = {MIT}
  // TODO: When a Zenodo release is created, add the DOI here.
  // doi    = {10.5281/zenodo.XXXXXXX}
}

3. Citing Foundational Works¶

This project builds directly upon pioneering open-source libraries. We strongly encourage you to also cite their original papers and software to give proper credit to the foundational work.

Microsoft Recommenders¶

Used for the collaborative filtering and content-based filtering implementations.

@inproceedings{recommenders2019,
  author    = {Graham, Scott and Min, Jun Ki and Wu, Tao and Soni, Anish},
  title     = {Microsoft Recommenders: Tools to Accelerate Developing Recommender Systems},
  year      = {2019},
  booktitle = {Proceedings of the 13th ACM Conference on Recommender Systems (RecSys '19)},
  pages     = {542--543},
  publisher = {Association for Computing Machinery},
  doi       = {10.1145/3298689.3346967}
}

PGPR (Reinforcement Learning)¶

The basis for our reinforcement learning and explainability implementation.

@inproceedings{xian2019pgpr,
  author    = {Xian, Yikun and Fu, Zuohui and Muthukrishnan, S. and de Melo, Gerard and Zhang, Yongfeng},
  title     = {Reinforcement Knowledge Graph Reasoning for Explainable Recommendation},
  year      = {2019},
  booktitle = {Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '19)},
  pages     = {285--294},
  publisher = {Association for Computing Machinery},
  doi       = {10.1145/3331184.3331203}
}

Recmetrics¶

Used for calculating the personalization and intra_list_similarity metrics.

@software{recmetrics2020,
  author    = {Longo, Claire},
  title     = {Recmetrics: A library of metrics for evaluating recommender systems},
  year      = {2020},
  version   = {0.1.3},
  publisher = {GitHub},
  url       = {https://github.com/statisticianinstilettos/recmetrics}
}

4. License¶

This project is licensed under the MIT License. You are free to use, modify, and distribute the code, but you must include the original copyright notice and license file in any derivative works. Please see the LICENSE file for full details.