Michael Müller, MSc.

@article{mohammadi2025quantitative,
  title={Yearning for Better Metrics: Revisiting the ReDial Dataset for Evaluating Conversational Recommender Systems},
  author={M{\"u}ller, Michael M and Mohammadi, Amir Reza and Peintner, Andreas and Gstrein, Beatriz Barroso and Zangerle, Eva and Specht, G{\"u}nther},
  year={2025},
  booktitle={Proceedings of the 36th GI-Workshop Grundlagen von Datenbanken (GvDB'25), Regensburg, Germany},
}

@article{mohammadi2025quantitative,
  title={Quantitative Assessment of GNN Counterfactual Explanation Robustness and Reproducibility under Adversarial Influence},
  author={Mohammadi, Amir Reza and M{\"u}ller, Michael M and Peintner, Andreas and Gstrein, Beatriz Barroso and Zangerle, Eva and Specht, G{\"u}nther},
  year={2025},
  booktitle    = {Proceedings of the 36th GI-Workshop Grundlagen von Datenbanken (GvDB'25), Regensburg, Germany},
}

@inproceedings{mohammadi:recsys:2025,
author = {Mohammadi, Amir Reza and Peintner, Andreas and M\"{u}ller, Michael and Zangerle, Eva},
title = {Beyond Top-1: Addressing Inconsistencies in Evaluating Counterfactual Explanations for Recommender Systems},
year = {2025},
isbn = {9798400713644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3705328.3748028},
doi = {10.1145/3705328.3748028},
abstract = {Explainability in recommender systems (RS) remains a pivotal yet challenging research frontier. Among state-of-the-art techniques, counterfactual explanations stand out for their effectiveness, as they show how small changes to input data can alter recommendations, providing actionable insights that build user trust and enhance transparency. Despite their growing prominence, the evaluation of counterfactual explanations in RS is far from standardized. Specifically, existing metrics show inconsistency since they are affected by variations in the performance of the underlying recommenders. Hence, we critically examine the evaluation of counterfactual explainers through consistency as the key principle of effective evaluation. Through extensive experiments, we assess how going beyond top-1 recommendation and incorporating top-k recommendations impacts the consistency of existing evaluation metrics. Our findings reveal factors that impact the consistency of existing evaluation metrics and offer a step toward effectively mitigating the inconsistency problem in counterfactual explanation evaluation.},
booktitle = {Proceedings of the Nineteenth ACM Conference on Recommender Systems},
pages = {515–520},
numpages = {6},
keywords = {Recommender Systems, Counterfactual Explanation, Evaluation},
location = {
},
series = {RecSys '25}
}

@inproceedings{peintner2025hyperhawkes,
  author       = {Andreas Peintner and
                  Amir Reza Mohammadi and
                  Michael M{\"{u}}ller and
                  Eva Zangerle},
  title        = {Hypergraph-based Temporal Modelling of Repeated Intent for Sequential
                  Recommendation},
  booktitle    = {Proceedings of the {ACM} on Web Conference 2025, {WWW} 2025, Sydney,
                  NSW, Australia, 28 April 2025- 2 May 2025},
  pages        = {3809--3818},
  publisher    = {{ACM}},
  year         = {2025},
  url          = {https://doi.org/10.1145/3696410.3714896},
  doi          = {10.1145/3696410.3714896}
}

@InProceedings{murauer2022dtgrams,
  author       = {Michael M. M{\"{u}}ller and
                  G{\"{u}}nther Specht and
                  Lukas Kleinheinz and
                  Janette F. Walde},
  booktitle = {Proceedings of the 35nd GI-Workshop Grundlagen von Datenbanksysteme (GvDB'24) },
  title        = {Bias Correction and Machine Learning in {AR(1)} Estimation: Bridging
                  Traditional and Modern Techniques},
  year      = {2024},
}