---
_id: '65105'
author:
- first_name: Philipp
full_name: zur Heiden, Philipp
id: '64394'
last_name: zur Heiden
- first_name: Haya
full_name: Halimeh, Haya
id: '87673'
last_name: Halimeh
- first_name: Philipp
full_name: Hansmeier, Philipp
id: '55603'
last_name: Hansmeier
- first_name: Christian
full_name: Vorbohle, Christian
id: '29951'
last_name: Vorbohle
- first_name: Maike
full_name: Althaus, Maike
id: '61896'
last_name: Althaus
- first_name: Daniel
full_name: Beverungen, Daniel
id: '59677'
last_name: Beverungen
- first_name: Dennis
full_name: Kundisch, Dennis
id: '21117'
last_name: Kundisch
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
citation:
ama: zur Heiden P, Halimeh H, Hansmeier P, et al. Data Spaces for Heterogeneous
Data Ecosystems – Findings from a Design Study in the Cultural Sector. Communications
of the Association for Information Systems.
apa: zur Heiden, P., Halimeh, H., Hansmeier, P., Vorbohle, C., Althaus, M., Beverungen,
D., Kundisch, D., & Müller, O. (n.d.). Data Spaces for Heterogeneous Data
Ecosystems – Findings from a Design Study in the Cultural Sector. Communications
of the Association for Information Systems.
bibtex: '@article{zur Heiden_Halimeh_Hansmeier_Vorbohle_Althaus_Beverungen_Kundisch_Müller,
title={Data Spaces for Heterogeneous Data Ecosystems – Findings from a Design
Study in the Cultural Sector}, journal={Communications of the Association for
Information Systems}, author={zur Heiden, Philipp and Halimeh, Haya and Hansmeier,
Philipp and Vorbohle, Christian and Althaus, Maike and Beverungen, Daniel and
Kundisch, Dennis and Müller, Oliver} }'
chicago: Heiden, Philipp zur, Haya Halimeh, Philipp Hansmeier, Christian Vorbohle,
Maike Althaus, Daniel Beverungen, Dennis Kundisch, and Oliver Müller. “Data Spaces
for Heterogeneous Data Ecosystems – Findings from a Design Study in the Cultural
Sector.” Communications of the Association for Information Systems, n.d.
ieee: P. zur Heiden et al., “Data Spaces for Heterogeneous Data Ecosystems
– Findings from a Design Study in the Cultural Sector,” Communications of the
Association for Information Systems.
mla: zur Heiden, Philipp, et al. “Data Spaces for Heterogeneous Data Ecosystems
– Findings from a Design Study in the Cultural Sector.” Communications of the
Association for Information Systems.
short: P. zur Heiden, H. Halimeh, P. Hansmeier, C. Vorbohle, M. Althaus, D. Beverungen,
D. Kundisch, O. Müller, Communications of the Association for Information Systems
(n.d.).
date_created: 2026-03-24T13:31:24Z
date_updated: 2026-03-27T08:24:49Z
department:
- _id: '276'
- _id: '526'
- _id: '196'
language:
- iso: eng
project:
- _id: '160'
name: 'DatenraumKultur: Use Case 1 - Kulturplattformen - Datenraum Kultur'
publication: Communications of the Association for Information Systems
publication_status: accepted
status: public
title: Data Spaces for Heterogeneous Data Ecosystems – Findings from a Design Study
in the Cultural Sector
type: journal_article
user_id: '16205'
year: '2026'
...
---
_id: '65810'
article_number: '127984'
author:
- first_name: David Ricardo
full_name: Zapata Gonzalez, David Ricardo
id: '105506'
last_name: Zapata Gonzalez
- first_name: Marcel
full_name: Meyer, Marcel
id: '105120'
last_name: Meyer
orcid: ' 0009-0005-9136-8525'
- first_name: Kevin
full_name: Zalipski, Kevin
id: '62222'
last_name: Zalipski
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
citation:
ama: Zapata Gonzalez DR, Meyer M, Zalipski K, Müller O. Temporal causal feature
selection for robust machine learning modelling of data center operations. Applied
Energy. 2026;417. doi:10.1016/j.apenergy.2026.127984
apa: Zapata Gonzalez, D. R., Meyer, M., Zalipski, K., & Müller, O. (2026). Temporal
causal feature selection for robust machine learning modelling of data center
operations. Applied Energy, 417, Article 127984. https://doi.org/10.1016/j.apenergy.2026.127984
bibtex: '@article{Zapata Gonzalez_Meyer_Zalipski_Müller_2026, title={Temporal causal
feature selection for robust machine learning modelling of data center operations},
volume={417}, DOI={10.1016/j.apenergy.2026.127984},
number={127984}, journal={Applied Energy}, publisher={Elsevier BV}, author={Zapata
Gonzalez, David Ricardo and Meyer, Marcel and Zalipski, Kevin and Müller, Oliver},
year={2026} }'
chicago: Zapata Gonzalez, David Ricardo, Marcel Meyer, Kevin Zalipski, and Oliver
Müller. “Temporal Causal Feature Selection for Robust Machine Learning Modelling
of Data Center Operations.” Applied Energy 417 (2026). https://doi.org/10.1016/j.apenergy.2026.127984.
ieee: 'D. R. Zapata Gonzalez, M. Meyer, K. Zalipski, and O. Müller, “Temporal causal
feature selection for robust machine learning modelling of data center operations,”
Applied Energy, vol. 417, Art. no. 127984, 2026, doi: 10.1016/j.apenergy.2026.127984.'
mla: Zapata Gonzalez, David Ricardo, et al. “Temporal Causal Feature Selection for
Robust Machine Learning Modelling of Data Center Operations.” Applied Energy,
vol. 417, 127984, Elsevier BV, 2026, doi:10.1016/j.apenergy.2026.127984.
short: D.R. Zapata Gonzalez, M. Meyer, K. Zalipski, O. Müller, Applied Energy 417
(2026).
date_created: 2026-06-09T07:14:48Z
date_updated: 2026-09-01T11:17:24Z
department:
- _id: '196'
doi: 10.1016/j.apenergy.2026.127984
intvolume: ' 417'
language:
- iso: eng
project:
- _id: '824'
name: DC2HEAT - Data Centre HEat Recovery with AI-Technologies
publication: Applied Energy
publication_identifier:
issn:
- 0306-2619
publication_status: published
publisher: Elsevier BV
status: public
title: Temporal causal feature selection for robust machine learning modelling of
data center operations
type: journal_article
user_id: '105120'
volume: 417
year: '2026'
...
---
_id: '66903'
author:
- first_name: Marcel
full_name: Meyer, Marcel
id: '105120'
last_name: Meyer
orcid: ' 0009-0005-9136-8525'
- first_name: Sascha Benjamin
full_name: Kaltenpoth, Sascha Benjamin
id: '50640'
last_name: Kaltenpoth
- first_name: Henrik
full_name: Albers, Henrik
id: '57403'
last_name: Albers
orcid: https://orcid.org/0009-0004-8388-619X
- first_name: Kevin
full_name: Zalipski, Kevin
id: '62222'
last_name: Zalipski
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
citation:
ama: 'Meyer M, Kaltenpoth SB, Albers H, Zalipski K, Müller O. TS-Arena -- A Live
Forecast Pre-Registration Platform. In: Proceedings of the 32nd ACM SIGKDD
Conference on Knowledge Discovery and Data Mining V.2. ACM; 2026. doi:10.1145/3770855.3817515'
apa: Meyer, M., Kaltenpoth, S. B., Albers, H., Zalipski, K., & Müller, O. (2026).
TS-Arena -- A Live Forecast Pre-Registration Platform. Proceedings of the 32nd
ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2. https://doi.org/10.1145/3770855.3817515
bibtex: '@inproceedings{Meyer_Kaltenpoth_Albers_Zalipski_Müller_2026, title={TS-Arena
-- A Live Forecast Pre-Registration Platform}, DOI={10.1145/3770855.3817515},
booktitle={Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery
and Data Mining V.2}, publisher={ACM}, author={Meyer, Marcel and Kaltenpoth, Sascha
Benjamin and Albers, Henrik and Zalipski, Kevin and Müller, Oliver}, year={2026}
}'
chicago: Meyer, Marcel, Sascha Benjamin Kaltenpoth, Henrik Albers, Kevin Zalipski,
and Oliver Müller. “TS-Arena -- A Live Forecast Pre-Registration Platform.” In
Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data
Mining V.2. ACM, 2026. https://doi.org/10.1145/3770855.3817515.
ieee: 'M. Meyer, S. B. Kaltenpoth, H. Albers, K. Zalipski, and O. Müller, “TS-Arena
-- A Live Forecast Pre-Registration Platform,” 2026, doi: 10.1145/3770855.3817515.'
mla: Meyer, Marcel, et al. “TS-Arena -- A Live Forecast Pre-Registration Platform.”
Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data
Mining V.2, ACM, 2026, doi:10.1145/3770855.3817515.
short: 'M. Meyer, S.B. Kaltenpoth, H. Albers, K. Zalipski, O. Müller, in: Proceedings
of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2,
ACM, 2026.'
date_created: 2026-09-01T09:46:25Z
date_updated: 2026-09-01T13:09:27Z
department:
- _id: '196'
doi: 10.1145/3770855.3817515
language:
- iso: eng
project:
- _id: '824'
name: DC2HEAT - Data Centre HEat Recovery with AI-Technologies
publication: Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery
and Data Mining V.2
publication_status: published
publisher: ACM
status: public
title: TS-Arena -- A Live Forecast Pre-Registration Platform
type: conference
user_id: '105120'
year: '2026'
...
---
_id: '58724'
author:
- first_name: Katharina
full_name: Brennig, Katharina
last_name: Brennig
- first_name: Sascha Benjamin
full_name: Kaltenpoth, Sascha Benjamin
id: '50640'
last_name: Kaltenpoth
- first_name: Oliver
full_name: Müller, Oliver
last_name: Müller
citation:
ama: 'Brennig K, Kaltenpoth SB, Müller O. Straight Outta Logs: Can Large Language
Models Overcome Preprocessing in Next Event Prediction? In: Lecture Notes in
Business Information Processing. Springer Nature Switzerland; 2025. doi:10.1007/978-3-031-78666-2_15'
apa: 'Brennig, K., Kaltenpoth, S. B., & Müller, O. (2025). Straight Outta Logs:
Can Large Language Models Overcome Preprocessing in Next Event Prediction? In
Lecture Notes in Business Information Processing. Springer Nature Switzerland.
https://doi.org/10.1007/978-3-031-78666-2_15'
bibtex: '@inbook{Brennig_Kaltenpoth_Müller_2025, place={Cham}, title={Straight Outta
Logs: Can Large Language Models Overcome Preprocessing in Next Event Prediction?},
DOI={10.1007/978-3-031-78666-2_15},
booktitle={Lecture Notes in Business Information Processing}, publisher={Springer
Nature Switzerland}, author={Brennig, Katharina and Kaltenpoth, Sascha Benjamin
and Müller, Oliver}, year={2025} }'
chicago: 'Brennig, Katharina, Sascha Benjamin Kaltenpoth, and Oliver Müller. “Straight
Outta Logs: Can Large Language Models Overcome Preprocessing in Next Event Prediction?”
In Lecture Notes in Business Information Processing. Cham: Springer Nature
Switzerland, 2025. https://doi.org/10.1007/978-3-031-78666-2_15.'
ieee: 'K. Brennig, S. B. Kaltenpoth, and O. Müller, “Straight Outta Logs: Can Large
Language Models Overcome Preprocessing in Next Event Prediction?,” in Lecture
Notes in Business Information Processing, Cham: Springer Nature Switzerland,
2025.'
mla: 'Brennig, Katharina, et al. “Straight Outta Logs: Can Large Language Models
Overcome Preprocessing in Next Event Prediction?” Lecture Notes in Business
Information Processing, Springer Nature Switzerland, 2025, doi:10.1007/978-3-031-78666-2_15.'
short: 'K. Brennig, S.B. Kaltenpoth, O. Müller, in: Lecture Notes in Business Information
Processing, Springer Nature Switzerland, Cham, 2025.'
date_created: 2025-02-20T09:11:59Z
date_updated: 2025-05-07T14:19:42Z
department:
- _id: '196'
doi: 10.1007/978-3-031-78666-2_15
language:
- iso: eng
place: Cham
publication: Lecture Notes in Business Information Processing
publication_identifier:
isbn:
- '9783031786655'
- '9783031786662'
issn:
- 1865-1348
- 1865-1356
publication_status: published
publisher: Springer Nature Switzerland
status: public
title: 'Straight Outta Logs: Can Large Language Models Overcome Preprocessing in Next
Event Prediction?'
type: book_chapter
user_id: '50640'
year: '2025'
...
---
_id: '63524'
abstract:
- lang: eng
text: Recommendation systems are essential for delivering personalized content across
e-commerce and streaming services. However, traditional methods often fail in
cold-start scenarios where new items lack prior interactions. Recent advances
in large language models (LLMs) offer a promising alternative. In this paper,
we adopt the retrieve-and-recommend framework and propose to fine-tune the LLM
jointly on warm-and cold-start next-item recommendation tasks, thus, mitigating
the need for separate models for both item types. We computationally compare zero-shot
prompting, in-context learning, and fine-tuning using the same LLM backbone, and
benchmark them against strong PLM-based baselines. Our findings provide practical
insights into the trade-offs between accuracy and computational cost of these
methods for next-item recommendation. To enhance reproducibility, we release the
source code under https://github. com/HayaHalimeh/LLMs-For-Next-Item-Recommendation.git.
author:
- first_name: Haya
full_name: Halimeh, Haya
id: '87673'
last_name: Halimeh
- first_name: Florian
full_name: Freese, Florian
last_name: Freese
- first_name: Oliver
full_name: Müller, Oliver
last_name: Müller
citation:
ama: 'Halimeh H, Freese F, Müller O. LLMs For Warm and Cold Next-Item Recommendation:
A Comparative Study across Zero-Shot Prompting, In-Context Learning and Fine-Tuning.
In: International Conference on Information Systems Development. University
of Gdansk, Department of Business Informatics & University of Belgrade, Faculty
of Organizational Sciences; 2025. doi:10.62036/isd.2025.68'
apa: 'Halimeh, H., Freese, F., & Müller, O. (2025). LLMs For Warm and Cold Next-Item
Recommendation: A Comparative Study across Zero-Shot Prompting, In-Context Learning
and Fine-Tuning. International Conference on Information Systems Development.
https://doi.org/10.62036/isd.2025.68'
bibtex: '@inproceedings{Halimeh_Freese_Müller_2025, title={LLMs For Warm and Cold
Next-Item Recommendation: A Comparative Study across Zero-Shot Prompting, In-Context
Learning and Fine-Tuning}, DOI={10.62036/isd.2025.68},
booktitle={International Conference on Information Systems Development}, publisher={University
of Gdansk, Department of Business Informatics & University of Belgrade, Faculty
of Organizational Sciences}, author={Halimeh, Haya and Freese, Florian and Müller,
Oliver}, year={2025} }'
chicago: 'Halimeh, Haya, Florian Freese, and Oliver Müller. “LLMs For Warm and Cold
Next-Item Recommendation: A Comparative Study across Zero-Shot Prompting, In-Context
Learning and Fine-Tuning.” In International Conference on Information Systems
Development. University of Gdansk, Department of Business Informatics &
University of Belgrade, Faculty of Organizational Sciences, 2025. https://doi.org/10.62036/isd.2025.68.'
ieee: 'H. Halimeh, F. Freese, and O. Müller, “LLMs For Warm and Cold Next-Item Recommendation:
A Comparative Study across Zero-Shot Prompting, In-Context Learning and Fine-Tuning,”
2025, doi: 10.62036/isd.2025.68.'
mla: 'Halimeh, Haya, et al. “LLMs For Warm and Cold Next-Item Recommendation: A
Comparative Study across Zero-Shot Prompting, In-Context Learning and Fine-Tuning.”
International Conference on Information Systems Development, University
of Gdansk, Department of Business Informatics & University of Belgrade, Faculty
of Organizational Sciences, 2025, doi:10.62036/isd.2025.68.'
short: 'H. Halimeh, F. Freese, O. Müller, in: International Conference on Information
Systems Development, University of Gdansk, Department of Business Informatics
& University of Belgrade, Faculty of Organizational Sciences, 2025.'
date_created: 2026-01-07T13:36:53Z
date_updated: 2026-01-07T13:47:43Z
department:
- _id: '195'
- _id: '196'
doi: 10.62036/isd.2025.68
language:
- iso: eng
main_file_link:
- open_access: '1'
oa: '1'
publication: International Conference on Information Systems Development
publication_identifier:
issn:
- 2938-5202
publication_status: published
publisher: University of Gdansk, Department of Business Informatics & University of
Belgrade, Faculty of Organizational Sciences
status: public
title: 'LLMs For Warm and Cold Next-Item Recommendation: A Comparative Study across
Zero-Shot Prompting, In-Context Learning and Fine-Tuning'
type: conference
user_id: '87673'
year: '2025'
...
---
_id: '63523'
abstract:
- lang: eng
text: 'Data spaces have become a strategic pillar of Europe''s digital agenda, enabling
sovereign, legally compliant data sharing within decentralized ecosystems. As
data space initiatives evolve, personalized recommendations are increasingly recognized
as key use cases. However, traditional recommendation approaches typically rely
on centralized aggregation of user behavior data-directly conflicting with the
core ethos of data spaces: sovereignty, privacy, and trust. Federated recommendation
systems offer a promising alternative by training models locally and exchanging
only intermediate parameters to build a global model. Despite this potential,
the integration of federated recommendation techniques and data space architectures
remains largely underexplored in research and practice. This paper addresses this
gap by designing and evaluating a prototype of a federated recommendation system
specifically tailored for data spaces and compliant with their underlying infrastructure.
Our findings highlight the viability of developing privacy-preserving, collaborative
recommendation systems within data spaces, and contribute to the broader adoption
of AI across these emerging ecosystems.'
author:
- first_name: Haya
full_name: Halimeh, Haya
id: '87673'
last_name: Halimeh
- first_name: Philipp
full_name: zur Heiden, Philipp
id: '64394'
last_name: zur Heiden
citation:
ama: 'Halimeh H, zur Heiden P. Preserving Sovereignty and Privacy for Personalization:
Designing a Federated Recommendation System for Data Spaces. In: 2025 27th
International Conference on Business Informatics (CBI). IEEE; 2025. doi:10.1109/cbi68102.2025.00019'
apa: 'Halimeh, H., & zur Heiden, P. (2025). Preserving Sovereignty and Privacy
for Personalization: Designing a Federated Recommendation System for Data Spaces.
2025 27th International Conference on Business Informatics (CBI). https://doi.org/10.1109/cbi68102.2025.00019'
bibtex: '@inproceedings{Halimeh_zur Heiden_2025, title={Preserving Sovereignty and
Privacy for Personalization: Designing a Federated Recommendation System for Data
Spaces}, DOI={10.1109/cbi68102.2025.00019},
booktitle={2025 27th International Conference on Business Informatics (CBI)},
publisher={IEEE}, author={Halimeh, Haya and zur Heiden, Philipp}, year={2025}
}'
chicago: 'Halimeh, Haya, and Philipp zur Heiden. “Preserving Sovereignty and Privacy
for Personalization: Designing a Federated Recommendation System for Data Spaces.”
In 2025 27th International Conference on Business Informatics (CBI). IEEE,
2025. https://doi.org/10.1109/cbi68102.2025.00019.'
ieee: 'H. Halimeh and P. zur Heiden, “Preserving Sovereignty and Privacy for Personalization:
Designing a Federated Recommendation System for Data Spaces,” 2025, doi: 10.1109/cbi68102.2025.00019.'
mla: 'Halimeh, Haya, and Philipp zur Heiden. “Preserving Sovereignty and Privacy
for Personalization: Designing a Federated Recommendation System for Data Spaces.”
2025 27th International Conference on Business Informatics (CBI), IEEE,
2025, doi:10.1109/cbi68102.2025.00019.'
short: 'H. Halimeh, P. zur Heiden, in: 2025 27th International Conference on Business
Informatics (CBI), IEEE, 2025.'
date_created: 2026-01-07T13:34:02Z
date_updated: 2026-01-07T13:49:45Z
department:
- _id: '195'
- _id: '196'
doi: 10.1109/cbi68102.2025.00019
language:
- iso: eng
main_file_link:
- open_access: '1'
oa: '1'
publication: 2025 27th International Conference on Business Informatics (CBI)
publication_status: published
publisher: IEEE
status: public
title: 'Preserving Sovereignty and Privacy for Personalization: Designing a Federated
Recommendation System for Data Spaces'
type: conference
user_id: '87673'
year: '2025'
...
---
_id: '63525'
abstract:
- lang: eng
text: "Recommender systems (RS) can support sustainable development by steering
users toward more sustainable choices. Sustainability-aware explanations represent
one avenue for contributing to this goal by foregrounding the environmental and
social aspects of the recommended products or services. This paper advances the
line of research on sustainability-aware explanations by integrating nudging mechanisms
into their design and by evaluating their effectiveness through a randomized between-subjects
online vignette experiment across two item domains (). Our findings offer actionable
design guidelines for building RS that foster sustainability-aware decision making
and enrich the empirical foundation for impact-oriented research on explanation
in RS.\r\n"
author:
- first_name: Haya
full_name: Halimeh, Haya
id: '87673'
last_name: Halimeh
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
citation:
ama: 'Halimeh H, Müller O. Towards Greener Choices: Decision Information Nudging
for Sustainability-Aware Recommender Explanations. In: ; 2025. doi:10.1007/978-3-032-13342-7'
apa: 'Halimeh, H., & Müller, O. (2025). Towards Greener Choices: Decision
Information Nudging for Sustainability-Aware Recommender Explanations. The
Second International Workshop on Recommender Systems for Sustainability and Social
Good, RecSoGood 2025, Prague, Czech Republic. https://doi.org/10.1007/978-3-032-13342-7'
bibtex: '@inproceedings{Halimeh_Müller_2025, title={Towards Greener Choices: Decision
Information Nudging for Sustainability-Aware Recommender Explanations}, DOI={10.1007/978-3-032-13342-7},
author={Halimeh, Haya and Müller, Oliver}, year={2025} }'
chicago: 'Halimeh, Haya, and Oliver Müller. “Towards Greener Choices: Decision Information
Nudging for Sustainability-Aware Recommender Explanations,” 2025. https://doi.org/10.1007/978-3-032-13342-7.'
ieee: 'H. Halimeh and O. Müller, “Towards Greener Choices: Decision Information
Nudging for Sustainability-Aware Recommender Explanations,” presented at the The
Second International Workshop on Recommender Systems for Sustainability and Social
Good, RecSoGood 2025, Prague, Czech Republic, 2025, doi: 10.1007/978-3-032-13342-7.'
mla: 'Halimeh, Haya, and Oliver Müller. Towards Greener Choices: Decision Information
Nudging for Sustainability-Aware Recommender Explanations. 2025, doi:10.1007/978-3-032-13342-7.'
short: 'H. Halimeh, O. Müller, in: 2025.'
conference:
location: Prague, Czech Republic
name: ' The Second International Workshop on Recommender Systems for Sustainability
and Social Good, RecSoGood 2025'
start_date: 2025-09-26
date_created: 2026-01-07T13:40:05Z
date_updated: 2026-01-07T13:46:50Z
department:
- _id: '195'
- _id: '196'
doi: 10.1007/978-3-032-13342-7
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://link.springer.com/book/10.1007/978-3-032-13342-7
oa: '1'
status: public
title: 'Towards Greener Choices: Decision Information Nudging for Sustainability-Aware
Recommender Explanations'
type: conference
user_id: '87673'
year: '2025'
...
---
_id: '63026'
author:
- first_name: Maike
full_name: Althaus, Maike
id: '61896'
last_name: Althaus
- first_name: Daniel
full_name: Beverungen, Daniel
id: '59677'
last_name: Beverungen
- first_name: Beate
full_name: Flath, Beate
id: '58896'
last_name: Flath
orcid: https://orcid.org/0000-0002-1648-0796
- first_name: Haya
full_name: Halimeh, Haya
id: '87673'
last_name: Halimeh
- first_name: Philipp
full_name: Hansmeier, Philipp
id: '55603'
last_name: Hansmeier
- first_name: Philipp
full_name: zur Heiden, Philipp
id: '64394'
last_name: zur Heiden
- first_name: Dennis
full_name: Kundisch, Dennis
id: '21117'
last_name: Kundisch
- first_name: Michelle
full_name: Müller, Michelle
id: '50286'
last_name: Müller
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
- first_name: Simon
full_name: Oberthür, Simon
id: '383'
last_name: Oberthür
- first_name: Christian
full_name: Vorbohle, Christian
id: '29951'
last_name: Vorbohle
- first_name: Maryam
full_name: Momen Pour Tafreshi, Maryam
last_name: Momen Pour Tafreshi
- first_name: Sebastian
full_name: Mauß, Sebastian
last_name: Mauß
- first_name: Alina
full_name: Mücke, Alina
last_name: Mücke
- first_name: Jörg
full_name: Müller, Jörg
last_name: Müller
- first_name: Malte
full_name: Peter, Malte
last_name: Peter
- first_name: Ariane
full_name: Schmitt-Chandon, Ariane
last_name: Schmitt-Chandon
- first_name: Kerstin
full_name: Sellerberg, Kerstin
last_name: Sellerberg
- first_name: Moritz
full_name: Steinhäuser, Moritz
last_name: Steinhäuser
citation:
ama: 'Althaus M, Beverungen D, Flath B, et al. Positionspapier Use Case 1: Vernetzte
Kulturplattformen.; 2025.'
apa: 'Althaus, M., Beverungen, D., Flath, B., Halimeh, H., Hansmeier, P., zur Heiden,
P., Kundisch, D., Müller, M., Müller, O., Oberthür, S., Vorbohle, C., Momen Pour
Tafreshi, M., Mauß, S., Mücke, A., Müller, J., Peter, M., Schmitt-Chandon, A.,
Sellerberg, K., & Steinhäuser, M. (2025). Positionspapier Use Case 1: Vernetzte
Kulturplattformen.'
bibtex: '@book{Althaus_Beverungen_Flath_Halimeh_Hansmeier_zur Heiden_Kundisch_Müller_Müller_Oberthür_et
al._2025, place={Universität Paderborn, SICP}, title={Positionspapier Use Case
1: Vernetzte Kulturplattformen}, author={Althaus, Maike and Beverungen, Daniel
and Flath, Beate and Halimeh, Haya and Hansmeier, Philipp and zur Heiden, Philipp
and Kundisch, Dennis and Müller, Michelle and Müller, Oliver and Oberthür, Simon
and et al.}, year={2025} }'
chicago: 'Althaus, Maike, Daniel Beverungen, Beate Flath, Haya Halimeh, Philipp
Hansmeier, Philipp zur Heiden, Dennis Kundisch, et al. Positionspapier Use
Case 1: Vernetzte Kulturplattformen. Universität Paderborn, SICP, 2025.'
ieee: 'M. Althaus et al., Positionspapier Use Case 1: Vernetzte Kulturplattformen.
Universität Paderborn, SICP, 2025.'
mla: 'Althaus, Maike, et al. Positionspapier Use Case 1: Vernetzte Kulturplattformen.
2025.'
short: 'M. Althaus, D. Beverungen, B. Flath, H. Halimeh, P. Hansmeier, P. zur Heiden,
D. Kundisch, M. Müller, O. Müller, S. Oberthür, C. Vorbohle, M. Momen Pour Tafreshi,
S. Mauß, A. Mücke, J. Müller, M. Peter, A. Schmitt-Chandon, K. Sellerberg, M.
Steinhäuser, Positionspapier Use Case 1: Vernetzte Kulturplattformen, Universität
Paderborn, SICP, 2025.'
date_created: 2025-12-10T15:50:35Z
date_updated: 2026-01-07T13:41:32Z
department:
- _id: '276'
- _id: '526'
- _id: '196'
- _id: '735'
language:
- iso: ger
place: Universität Paderborn, SICP
project:
- _id: '160'
name: 'DatenraumKultur: Use Case 1 - Kulturplattformen - Datenraum Kultur'
publication_status: published
status: public
title: 'Positionspapier Use Case 1: Vernetzte Kulturplattformen'
type: working_paper
user_id: '87673'
year: '2025'
...
---
_id: '60680'
abstract:
- lang: eng
text: "Classical machine learning techniques often struggle with overfitting and
unreliable predictions when exposed to novel conditions. Introducing causality
into the modelling process offers a promising way to mitigate these challenges
by enhancing predictive robustness. However, constructing an initial causal graph
manually using domain knowledge is time-consuming, particularly in complex time
series with numerous variables. To address this, causal discovery algorithms can
provide a preliminary causal structure that domain experts can refine. This study
investigates causal feature selection with domain knowledge using a data center
system as an example. We use simulated time-series data to compare \r\ndifferent
causal feature selection with traditional machine-learning feature selection methods.
Our results show that predictions based on causal features are more robust compared
to those derived from traditional methods. These findings underscore the potential
of combining causal discovery algorithms with human expertise to improve machine
learning applications."
author:
- first_name: David Ricardo
full_name: Zapata Gonzalez, David Ricardo
id: '105506'
last_name: Zapata Gonzalez
- first_name: Marcel
full_name: Meyer, Marcel
id: '105120'
last_name: Meyer
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
citation:
ama: 'Zapata Gonzalez DR, Meyer M, Müller O. Bridging the gap between data-driven
and theory-driven modelling – leveraging causal machine learning for integrative
modelling of dynamical systems. In: ; 2025.'
apa: Zapata Gonzalez, D. R., Meyer, M., & Müller, O. (2025). Bridging the
gap between data-driven and theory-driven modelling – leveraging causal machine
learning for integrative modelling of dynamical systems. European Conference
on Information Systems, Amman, Jordan.
bibtex: '@inproceedings{Zapata Gonzalez_Meyer_Müller_2025, title={Bridging the gap
between data-driven and theory-driven modelling – leveraging causal machine learning
for integrative modelling of dynamical systems}, author={Zapata Gonzalez, David
Ricardo and Meyer, Marcel and Müller, Oliver}, year={2025} }'
chicago: Zapata Gonzalez, David Ricardo, Marcel Meyer, and Oliver Müller. “Bridging
the Gap between Data-Driven and Theory-Driven Modelling – Leveraging Causal Machine
Learning for Integrative Modelling of Dynamical Systems,” 2025.
ieee: D. R. Zapata Gonzalez, M. Meyer, and O. Müller, “Bridging the gap between
data-driven and theory-driven modelling – leveraging causal machine learning for
integrative modelling of dynamical systems,” presented at the European Conference
on Information Systems, Amman, Jordan, 2025.
mla: Zapata Gonzalez, David Ricardo, et al. Bridging the Gap between Data-Driven
and Theory-Driven Modelling – Leveraging Causal Machine Learning for Integrative
Modelling of Dynamical Systems. 2025.
short: 'D.R. Zapata Gonzalez, M. Meyer, O. Müller, in: 2025.'
conference:
end_date: 18.06.2025
location: Amman, Jordan
name: European Conference on Information Systems
start_date: 16.06.2025
date_created: 2025-07-21T07:52:03Z
date_updated: 2025-07-22T06:30:37Z
department:
- _id: '196'
keyword:
- Causal Machine Learning
- Causality in Time Series
- Causal Discovery
- Human-Machine Collaboration
language:
- iso: eng
main_file_link:
- url: https://aisel.aisnet.org/ecis2025/bus_analytics/bus_analytics/2/
status: public
title: Bridging the gap between data-driven and theory-driven modelling – leveraging
causal machine learning for integrative modelling of dynamical systems
type: conference
user_id: '72849'
year: '2025'
...
---
_id: '60958'
abstract:
- lang: eng
text: Large Language Models (LLMs) excel in understanding, generating, and processing
human language, with growing adoption in process mining. Process mining relies
on event logs that capture explicit process knowledge; however, knowledge-intensive
processes (KIPs) in domains such as healthcare and product development depend
on tacit knowledge, which is often absent from event logs. To bridge this gap,
this study proposes a LLM-based framework for mobilizing tacit process knowledge
and enriching event logs. A proof-of-concept is demonstrated using a KIP-specific
LLM-driven conversational agent built on GPT-4o. The results indicate that LLMs
can capture tacit process knowledge through targeted queries and systematically
integrate it into event logs. This study presents a novel approach combining LLMs,
knowledge management, and process mining, advancing the understanding and management
of KIPs by enhancing knowledge accessibility and documentation.
author:
- first_name: Katharina
full_name: Brennig, Katharina
last_name: Brennig
citation:
ama: 'Brennig K. Revealing the Unspoken: Using LLMs to Mobilize and Enrich Tacit
Knowledge in Event Logs of Knowledge-Intensive Processes. In: AMCIS 2025 Proceedings.
11. ; 2025.'
apa: 'Brennig, K. (2025). Revealing the Unspoken: Using LLMs to Mobilize and Enrich
Tacit Knowledge in Event Logs of Knowledge-Intensive Processes. AMCIS 2025
Proceedings. 11. Americas Conference on Information Systems, Montréal.'
bibtex: '@inproceedings{Brennig_2025, title={Revealing the Unspoken: Using LLMs
to Mobilize and Enrich Tacit Knowledge in Event Logs of Knowledge-Intensive Processes},
booktitle={AMCIS 2025 Proceedings. 11.}, author={Brennig, Katharina}, year={2025}
}'
chicago: 'Brennig, Katharina. “Revealing the Unspoken: Using LLMs to Mobilize and
Enrich Tacit Knowledge in Event Logs of Knowledge-Intensive Processes.” In AMCIS
2025 Proceedings. 11., 2025.'
ieee: 'K. Brennig, “Revealing the Unspoken: Using LLMs to Mobilize and Enrich Tacit
Knowledge in Event Logs of Knowledge-Intensive Processes,” presented at the Americas
Conference on Information Systems, Montréal, 2025.'
mla: 'Brennig, Katharina. “Revealing the Unspoken: Using LLMs to Mobilize and Enrich
Tacit Knowledge in Event Logs of Knowledge-Intensive Processes.” AMCIS 2025
Proceedings. 11., 2025.'
short: 'K. Brennig, in: AMCIS 2025 Proceedings. 11., 2025.'
conference:
end_date: 2025-08-16
location: Montréal
name: Americas Conference on Information Systems
start_date: 2025-08-14
date_created: 2025-08-20T07:03:37Z
date_updated: 2025-08-20T07:06:16Z
department:
- _id: '196'
keyword:
- Process Mining
- Large Language Model
- Knowledge Management
- Knowledge-Intensive Process
- Tacit Knowledge
language:
- iso: eng
main_file_link:
- url: https://aisel.aisnet.org/amcis2025/sig_svc/sig_svc/11/
publication: AMCIS 2025 Proceedings. 11.
related_material:
link:
- relation: confirmation
url: https://aisel.aisnet.org/amcis2025/sig_svc/sig_svc/11/
status: public
title: 'Revealing the Unspoken: Using LLMs to Mobilize and Enrich Tacit Knowledge
in Event Logs of Knowledge-Intensive Processes'
type: conference
user_id: '51905'
year: '2025'
...
---
_id: '63400'
abstract:
- lang: eng
text: Data centers (DCs) form the backbone of our growing digital economy, but their
rising energy demands pose challenges to our environment. At the same time, reusing
waste heat from DCs also represents an opportunity, for example, for more sustainable
heating of residential buildings. Modeling and optimizing these coupled and dynamic
systems of heat generation and reuse is complex. On the one hand, physical simulations
can be used to model these systems, but they are time-consuming to develop and
run. Machine learning (ML), on the other hand, allows efficient data-driven modeling,
but conventional correlation-based approaches struggle with the prediction of
interventions and out-of-distribution generalization. Recent advances in causal
ML, which combine principles from causal inference with flexible ML methods, are
a promising approach for more robust predictions. Due to their focus on modeling
interventions and cause-and-effect relationships, it is difficult to evaluate
causal ML approaches rigorously. To address this challenge, we built a testbed
of a miniature DC with an integrated waste heat network, equipped with sensors
and actuators. This testbed allows conducting controlled experiments and automatic
collection of realistic data, which can then be used to benchmark conventional
and causal ML methods. Our experimental results highlight the strengths and weaknesses
of each modeling approach, providing valuable insights on how to appropriately
apply different types of machine learning to optimize data center operations and
enhance their sustainability.
author:
- first_name: David Ricardo
full_name: Zapata Gonzalez, David Ricardo
id: '105506'
last_name: Zapata Gonzalez
- first_name: Marcel
full_name: Meyer, Marcel
id: '105120'
last_name: Meyer
orcid: ' 0009-0005-9136-8525'
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
citation:
ama: 'Zapata Gonzalez DR, Meyer M, Müller O. Causal Machine Learning Approaches
for Modelling Data Center Heat Recovery: A Physical Testbed Study. In: ACM
SIGEnergy Energy Informatics Review. Vol 5. Association for Computing Machinery
(ACM); 2025:4-10. doi:10.1145/3757892.3757893'
apa: 'Zapata Gonzalez, D. R., Meyer, M., & Müller, O. (2025). Causal Machine
Learning Approaches for Modelling Data Center Heat Recovery: A Physical Testbed
Study. ACM SIGEnergy Energy Informatics Review, 5(2), 4–10. https://doi.org/10.1145/3757892.3757893'
bibtex: '@inproceedings{Zapata Gonzalez_Meyer_Müller_2025, title={Causal Machine
Learning Approaches for Modelling Data Center Heat Recovery: A Physical Testbed
Study}, volume={5}, DOI={10.1145/3757892.3757893},
number={2}, booktitle={ACM SIGEnergy Energy Informatics Review}, publisher={Association
for Computing Machinery (ACM)}, author={Zapata Gonzalez, David Ricardo and Meyer,
Marcel and Müller, Oliver}, year={2025}, pages={4–10} }'
chicago: 'Zapata Gonzalez, David Ricardo, Marcel Meyer, and Oliver Müller. “Causal
Machine Learning Approaches for Modelling Data Center Heat Recovery: A Physical
Testbed Study.” In ACM SIGEnergy Energy Informatics Review, 5:4–10. Association
for Computing Machinery (ACM), 2025. https://doi.org/10.1145/3757892.3757893.'
ieee: 'D. R. Zapata Gonzalez, M. Meyer, and O. Müller, “Causal Machine Learning
Approaches for Modelling Data Center Heat Recovery: A Physical Testbed Study,”
in ACM SIGEnergy Energy Informatics Review, 2025, vol. 5, no. 2, pp. 4–10,
doi: 10.1145/3757892.3757893.'
mla: 'Zapata Gonzalez, David Ricardo, et al. “Causal Machine Learning Approaches
for Modelling Data Center Heat Recovery: A Physical Testbed Study.” ACM SIGEnergy
Energy Informatics Review, vol. 5, no. 2, Association for Computing Machinery
(ACM), 2025, pp. 4–10, doi:10.1145/3757892.3757893.'
short: 'D.R. Zapata Gonzalez, M. Meyer, O. Müller, in: ACM SIGEnergy Energy Informatics
Review, Association for Computing Machinery (ACM), 2025, pp. 4–10.'
date_created: 2025-12-22T13:19:06Z
date_updated: 2026-09-01T11:18:27Z
department:
- _id: '196'
doi: 10.1145/3757892.3757893
intvolume: ' 5'
issue: '2'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://dl.acm.org/doi/abs/10.1145/3757892.3757893
oa: '1'
page: 4-10
project:
- _id: '824'
name: DC2HEAT - Data Centre HEat Recovery with AI-Technologies
publication: ACM SIGEnergy Energy Informatics Review
publication_identifier:
issn:
- 2770-5331
- 2770-5331
publication_status: published
publisher: Association for Computing Machinery (ACM)
status: public
title: 'Causal Machine Learning Approaches for Modelling Data Center Heat Recovery:
A Physical Testbed Study'
type: conference
user_id: '105120'
volume: 5
year: '2025'
...
---
_id: '64870'
author:
- first_name: Marcel
full_name: Meyer, Marcel
id: '105120'
last_name: Meyer
orcid: ' 0009-0005-9136-8525'
- first_name: David Ricardo
full_name: Zapata Gonzalez, David Ricardo
id: '105506'
last_name: Zapata Gonzalez
- first_name: Sascha Benjamin
full_name: Kaltenpoth, Sascha Benjamin
id: '50640'
last_name: Kaltenpoth
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
citation:
ama: Meyer M, Zapata Gonzalez DR, Kaltenpoth SB, Müller O. Benchmarking Time Series
Foundation Models for Short-Term Household Electricity Load Forecasting. IEEE
Access. 2025;13:218141-218153. doi:10.1109/access.2025.3648056
apa: Meyer, M., Zapata Gonzalez, D. R., Kaltenpoth, S. B., & Müller, O. (2025).
Benchmarking Time Series Foundation Models for Short-Term Household Electricity
Load Forecasting. IEEE Access, 13, 218141–218153. https://doi.org/10.1109/access.2025.3648056
bibtex: '@article{Meyer_Zapata Gonzalez_Kaltenpoth_Müller_2025, title={Benchmarking
Time Series Foundation Models for Short-Term Household Electricity Load Forecasting},
volume={13}, DOI={10.1109/access.2025.3648056},
journal={IEEE Access}, publisher={Institute of Electrical and Electronics Engineers
(IEEE)}, author={Meyer, Marcel and Zapata Gonzalez, David Ricardo and Kaltenpoth,
Sascha Benjamin and Müller, Oliver}, year={2025}, pages={218141–218153} }'
chicago: 'Meyer, Marcel, David Ricardo Zapata Gonzalez, Sascha Benjamin Kaltenpoth,
and Oliver Müller. “Benchmarking Time Series Foundation Models for Short-Term
Household Electricity Load Forecasting.” IEEE Access 13 (2025): 218141–53.
https://doi.org/10.1109/access.2025.3648056.'
ieee: 'M. Meyer, D. R. Zapata Gonzalez, S. B. Kaltenpoth, and O. Müller, “Benchmarking
Time Series Foundation Models for Short-Term Household Electricity Load Forecasting,”
IEEE Access, vol. 13, pp. 218141–218153, 2025, doi: 10.1109/access.2025.3648056.'
mla: Meyer, Marcel, et al. “Benchmarking Time Series Foundation Models for Short-Term
Household Electricity Load Forecasting.” IEEE Access, vol. 13, Institute
of Electrical and Electronics Engineers (IEEE), 2025, pp. 218141–53, doi:10.1109/access.2025.3648056.
short: M. Meyer, D.R. Zapata Gonzalez, S.B. Kaltenpoth, O. Müller, IEEE Access 13
(2025) 218141–218153.
date_created: 2026-03-09T16:58:28Z
date_updated: 2026-09-01T09:43:34Z
department:
- _id: '196'
doi: 10.1109/access.2025.3648056
intvolume: ' 13'
language:
- iso: eng
page: 218141-218153
project:
- _id: '824'
name: DC2HEAT - Data Centre HEat Recovery with AI-Technologies
publication: IEEE Access
publication_identifier:
issn:
- 2169-3536
publication_status: published
publisher: Institute of Electrical and Electronics Engineers (IEEE)
status: public
title: Benchmarking Time Series Foundation Models for Short-Term Household Electricity
Load Forecasting
type: journal_article
user_id: '105120'
volume: 13
year: '2025'
...
---
_id: '53130'
author:
- first_name: Miriam
full_name: Stumpe, Miriam
id: '64135'
last_name: Stumpe
- first_name: Peter
full_name: Dieter, Peter
id: '88592'
last_name: Dieter
- first_name: Guido
full_name: Schryen, Guido
id: '72850'
last_name: Schryen
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
- first_name: Daniel
full_name: Beverungen, Daniel
id: '59677'
last_name: Beverungen
citation:
ama: 'Stumpe M, Dieter P, Schryen G, Müller O, Beverungen D. Designing taxi ridesharing
systems with shared pick-up and drop-off locations: Insights from a computational
study. Transportation Research Part A: Policy and Practice. Published online
2024.'
apa: 'Stumpe, M., Dieter, P., Schryen, G., Müller, O., & Beverungen, D. (2024).
Designing taxi ridesharing systems with shared pick-up and drop-off locations:
Insights from a computational study. Transportation Research Part A: Policy
and Practice.'
bibtex: '@article{Stumpe_Dieter_Schryen_Müller_Beverungen_2024, title={Designing
taxi ridesharing systems with shared pick-up and drop-off locations: Insights
from a computational study}, journal={Transportation Research Part A: Policy and
Practice}, author={Stumpe, Miriam and Dieter, Peter and Schryen, Guido and Müller,
Oliver and Beverungen, Daniel}, year={2024} }'
chicago: 'Stumpe, Miriam, Peter Dieter, Guido Schryen, Oliver Müller, and Daniel
Beverungen. “Designing Taxi Ridesharing Systems with Shared Pick-up and Drop-off
Locations: Insights from a Computational Study.” Transportation Research Part
A: Policy and Practice, 2024.'
ieee: 'M. Stumpe, P. Dieter, G. Schryen, O. Müller, and D. Beverungen, “Designing
taxi ridesharing systems with shared pick-up and drop-off locations: Insights
from a computational study,” Transportation Research Part A: Policy and Practice,
2024.'
mla: 'Stumpe, Miriam, et al. “Designing Taxi Ridesharing Systems with Shared Pick-up
and Drop-off Locations: Insights from a Computational Study.” Transportation
Research Part A: Policy and Practice, 2024.'
short: 'M. Stumpe, P. Dieter, G. Schryen, O. Müller, D. Beverungen, Transportation
Research Part A: Policy and Practice (2024).'
date_created: 2024-04-02T12:10:56Z
date_updated: 2024-04-02T12:29:47Z
ddc:
- '000'
department:
- _id: '277'
- _id: '526'
- _id: '196'
file:
- access_level: open_access
content_type: application/pdf
creator: mateskam
date_created: 2024-04-02T12:10:18Z
date_updated: 2024-04-02T12:10:18Z
file_id: '53131'
file_name: Designing_TRS-systems_Final.pdf
file_size: 835032
relation: main_file
file_date_updated: 2024-04-02T12:10:18Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
publication: 'Transportation Research Part A: Policy and Practice'
status: public
title: 'Designing taxi ridesharing systems with shared pick-up and drop-off locations:
Insights from a computational study'
type: journal_article
user_id: '51811'
year: '2024'
...
---
_id: '55096'
author:
- first_name: Kevin
full_name: Bösch, Kevin
last_name: Bösch
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
- first_name: Markus
full_name: Weinmann, Markus
last_name: Weinmann
citation:
ama: 'Bösch K, Müller O, Weinmann M. Not your Average Digital Nudge: Heterogeneous
Effects of Personalized Nudges with CausalML. In: Proceedings of the Symposium
on Statistical Challenges in Electronic Commerce Research. ; 2024.'
apa: 'Bösch, K., Müller, O., & Weinmann, M. (2024). Not your Average Digital
Nudge: Heterogeneous Effects of Personalized Nudges with CausalML. Proceedings
of the Symposium on Statistical Challenges in Electronic Commerce Research.'
bibtex: '@inproceedings{Bösch_Müller_Weinmann_2024, title={Not your Average Digital
Nudge: Heterogeneous Effects of Personalized Nudges with CausalML}, booktitle={Proceedings
of the Symposium on Statistical Challenges in Electronic Commerce Research}, author={Bösch,
Kevin and Müller, Oliver and Weinmann, Markus}, year={2024} }'
chicago: 'Bösch, Kevin, Oliver Müller, and Markus Weinmann. “Not Your Average Digital
Nudge: Heterogeneous Effects of Personalized Nudges with CausalML.” In Proceedings
of the Symposium on Statistical Challenges in Electronic Commerce Research,
2024.'
ieee: 'K. Bösch, O. Müller, and M. Weinmann, “Not your Average Digital Nudge: Heterogeneous
Effects of Personalized Nudges with CausalML,” 2024.'
mla: 'Bösch, Kevin, et al. “Not Your Average Digital Nudge: Heterogeneous Effects
of Personalized Nudges with CausalML.” Proceedings of the Symposium on Statistical
Challenges in Electronic Commerce Research, 2024.'
short: 'K. Bösch, O. Müller, M. Weinmann, in: Proceedings of the Symposium on Statistical
Challenges in Electronic Commerce Research, 2024.'
date_created: 2024-07-05T15:17:43Z
date_updated: 2024-07-05T15:19:28Z
department:
- _id: '196'
language:
- iso: eng
publication: Proceedings of the Symposium on Statistical Challenges in Electronic
Commerce Research
status: public
title: 'Not your Average Digital Nudge: Heterogeneous Effects of Personalized Nudges
with CausalML'
type: conference_abstract
user_id: '72849'
year: '2024'
...
---
_id: '56945'
abstract:
- lang: eng
text: Adopting Large language models (LLMs) in organizations potentially revolutionizes
our lives and work. However, they can generate off-topic, discriminating, or harmful
content. This AI alignment problem often stems from misspecifications during the
LLM adoption, unnoticed by the principal due to the LLM’s black-box nature. While
various research disciplines investigated AI alignment, they neither address the
information asymmetries between organizational adopters and black-box LLM agents
nor consider organizational AI adoption processes. Therefore, we propose LLM ATLAS
(LLM Agency Theory-Led Alignment Strategy) a conceptual framework grounded in
agency (contract) theory, to mitigate alignment problems during organizational
LLM adoption. We conduct a conceptual literature analysis using the organizational
LLM adoption phases and the agency theory as concepts. Our approach results in
(1) providing an extended literature analysis process specific to AI alignment
methods during organizational LLM adoption and (2) providing a first LLM alignment
problem-solutionspace.
author:
- first_name: Sascha Benjamin
full_name: Kaltenpoth, Sascha Benjamin
id: '50640'
last_name: Kaltenpoth
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
citation:
ama: 'Kaltenpoth SB, Müller O. Getting in Contract with Large Language Models -
An Agency Theory Perspective On Large Language Model Alignment. In: Wirtschaftsinformatik
2024 Proceedings. ; 2024.'
apa: Kaltenpoth, S. B., & Müller, O. (2024). Getting in Contract with Large
Language Models - An Agency Theory Perspective On Large Language Model Alignment.
Wirtschaftsinformatik 2024 Proceedings.
bibtex: '@inproceedings{Kaltenpoth_Müller_2024, title={Getting in Contract with
Large Language Models - An Agency Theory Perspective On Large Language Model Alignment},
booktitle={Wirtschaftsinformatik 2024 Proceedings}, author={Kaltenpoth, Sascha
Benjamin and Müller, Oliver}, year={2024} }'
chicago: Kaltenpoth, Sascha Benjamin, and Oliver Müller. “Getting in Contract with
Large Language Models - An Agency Theory Perspective On Large Language Model Alignment.”
In Wirtschaftsinformatik 2024 Proceedings, 2024.
ieee: S. B. Kaltenpoth and O. Müller, “Getting in Contract with Large Language Models
- An Agency Theory Perspective On Large Language Model Alignment,” 2024.
mla: Kaltenpoth, Sascha Benjamin, and Oliver Müller. “Getting in Contract with Large
Language Models - An Agency Theory Perspective On Large Language Model Alignment.”
Wirtschaftsinformatik 2024 Proceedings, 2024.
short: 'S.B. Kaltenpoth, O. Müller, in: Wirtschaftsinformatik 2024 Proceedings,
2024.'
conference:
end_date: 19.09.2024
start_date: 16.09.2024
date_created: 2024-11-07T16:23:23Z
date_updated: 2024-11-11T16:45:34Z
department:
- _id: '196'
language:
- iso: eng
main_file_link:
- url: https://aisel.aisnet.org/wi2024/91/
publication: Wirtschaftsinformatik 2024 Proceedings
status: public
title: Getting in Contract with Large Language Models - An Agency Theory Perspective
On Large Language Model Alignment
type: conference
user_id: '50640'
year: '2024'
...
---
_id: '37312'
abstract:
- lang: eng
text: Optimal decision making requires appropriate evaluation of advice. Recent
literature reports that algorithm aversion reduces the effectiveness of predictive
algorithms. However, it remains unclear how people recover from bad advice given
by an otherwise good advisor. Previous work has focused on algorithm aversion
at a single time point. We extend this work by examining successive decisions
in a time series forecasting task using an online between-subjects experiment
(N = 87). Our empirical results do not confirm algorithm aversion immediately
after bad advice. The estimated effect suggests an increasing algorithm appreciation
over time. Our work extends the current knowledge on algorithm aversion with insights
into how weight on advice is adjusted over consecutive tasks. Since most forecasting
tasks are not one-off decisions, this also has implications for practitioners.
author:
- first_name: Dirk
full_name: Leffrang, Dirk
id: '51271'
last_name: Leffrang
orcid: 0000-0001-9004-2391
- first_name: Kevin
full_name: Bösch, Kevin
last_name: Bösch
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
citation:
ama: 'Leffrang D, Bösch K, Müller O. Do People Recover from Algorithm Aversion?
An Experimental Study of Algorithm Aversion over Time. In: Hawaii International
Conference on System Sciences. ; 2023.'
apa: Leffrang, D., Bösch, K., & Müller, O. (2023). Do People Recover from Algorithm
Aversion? An Experimental Study of Algorithm Aversion over Time. Hawaii International
Conference on System Sciences. Hawaii International Conference on System Sciences.
bibtex: '@inproceedings{Leffrang_Bösch_Müller_2023, title={Do People Recover from
Algorithm Aversion? An Experimental Study of Algorithm Aversion over Time}, booktitle={Hawaii
International Conference on System Sciences}, author={Leffrang, Dirk and Bösch,
Kevin and Müller, Oliver}, year={2023} }'
chicago: Leffrang, Dirk, Kevin Bösch, and Oliver Müller. “Do People Recover from
Algorithm Aversion? An Experimental Study of Algorithm Aversion over Time.” In
Hawaii International Conference on System Sciences, 2023.
ieee: D. Leffrang, K. Bösch, and O. Müller, “Do People Recover from Algorithm Aversion?
An Experimental Study of Algorithm Aversion over Time,” presented at the Hawaii
International Conference on System Sciences, 2023.
mla: Leffrang, Dirk, et al. “Do People Recover from Algorithm Aversion? An Experimental
Study of Algorithm Aversion over Time.” Hawaii International Conference on
System Sciences, 2023.
short: 'D. Leffrang, K. Bösch, O. Müller, in: Hawaii International Conference on
System Sciences, 2023.'
conference:
name: Hawaii International Conference on System Sciences
date_created: 2023-01-18T10:53:51Z
date_updated: 2024-01-10T09:52:59Z
department:
- _id: '196'
keyword:
- Algorithm aversion
- Time series
- Decision making
- Advice taking
- Forecasting
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://scholarspace.manoa.hawaii.edu/items/62b58ddc-895c-48c3-8194-522a1758a26f
oa: '1'
publication: Hawaii International Conference on System Sciences
status: public
title: Do People Recover from Algorithm Aversion? An Experimental Study of Algorithm
Aversion over Time
type: conference
user_id: '51271'
year: '2023'
...
---
_id: '50121'
abstract:
- lang: eng
text: Many researchers and practitioners see artificial intelligence as a game changer
compared to classical statistical models. However, some software providers engage
in “AI washing”, relabeling solutions that use simple statistical models as AI
systems. By contrast, research on algorithm aversion unsystematically varied the
labels for advisors and treated labels such as "artificial intelligence" and "statistical
model" synonymously. This study investigates the effect of individual labels on
users' actual advice utilization behavior. Through two incentivized online within-subjects
experiments on regression tasks, we find that labeling human advisors with labels
that suggest higher expertise leads to an increase in advice-taking, even though
the content of the advice remains the same. In contrast, our results do not suggest
such an expert effect for advice-taking from algorithms, despite differences in
self-reported perception. These findings challenge the effectiveness of framing
intelligent systems as AI-based systems and have important implications for both
research and practice.
author:
- first_name: Dirk
full_name: Leffrang, Dirk
id: '51271'
last_name: Leffrang
orcid: 0000-0001-9004-2391
citation:
ama: 'Leffrang D. AI Washing: The Framing Effect of Labels on Algorithmic Advice
Utilization. In: International Conference on Information Systems. ; 2023.'
apa: 'Leffrang, D. (2023). AI Washing: The Framing Effect of Labels on Algorithmic
Advice Utilization. International Conference on Information Systems, 10.'
bibtex: '@inproceedings{Leffrang_2023, title={AI Washing: The Framing Effect of
Labels on Algorithmic Advice Utilization}, number={10}, booktitle={International
Conference on Information Systems}, author={Leffrang, Dirk}, year={2023} }'
chicago: 'Leffrang, Dirk. “AI Washing: The Framing Effect of Labels on Algorithmic
Advice Utilization.” In International Conference on Information Systems,
2023.'
ieee: 'D. Leffrang, “AI Washing: The Framing Effect of Labels on Algorithmic Advice
Utilization,” in International Conference on Information Systems, Hyderabad,
India, 2023, no. 10.'
mla: 'Leffrang, Dirk. “AI Washing: The Framing Effect of Labels on Algorithmic Advice
Utilization.” International Conference on Information Systems, no. 10,
2023.'
short: 'D. Leffrang, in: International Conference on Information Systems, 2023.'
conference:
location: Hyderabad, India
name: International Conference on Information Systems (ICIS)
date_created: 2024-01-03T09:54:00Z
date_updated: 2024-01-10T09:53:41Z
department:
- _id: '196'
issue: '10'
keyword:
- Artificial Intelligence
- Algorithm Appreciation
- Framing
- Advice-taking
- Expertise
language:
- iso: eng
main_file_link:
- url: https://aisel.aisnet.org/icis2023/aiinbus/aiinbus/10
publication: International Conference on Information Systems
status: public
title: 'AI Washing: The Framing Effect of Labels on Algorithmic Advice Utilization'
type: conference
user_id: '51271'
year: '2023'
...
---
_id: '50118'
abstract:
- lang: eng
text: Despite the widespread use of machine learning algorithms, their effectiveness
is limited by a phenomenon known as algorithm aversion. Recent research concluded
that unobserved variables can cause algorithm aversion. However, the impact of
an unobserved variable on algorithm aversion remains unclear. Previous studies
focused on situations where humans had more variables available than algorithms.
We extend this research by conducting an online experiment with 94 participants,
systematically varying the number of observable variables to the advisor and the
advisor type. Surprisingly, our results did not confirm that an unobserved variable
had a negative effect on advice-taking. Instead, we found a positive impact in
an algorithm appreciation scenario. This study provides new insights into the
paradoxical behavior in which people weigh advice more despite having fewer variables,
as they correct for the advisor's errors. Practitioners should consider this behavior
when designing algorithms and account for user correction behavior.
author:
- first_name: Dirk
full_name: Leffrang, Dirk
id: '51271'
last_name: Leffrang
orcid: 0000-0001-9004-2391
citation:
ama: 'Leffrang D. The Broken Leg of Algorithm Appreciation: An Experimental Study
on the Effect of Unobserved Variables on Advice Utilization. In: Wirtschaftsinformatik
Conference. ; 2023.'
apa: 'Leffrang, D. (2023). The Broken Leg of Algorithm Appreciation: An Experimental
Study on the Effect of Unobserved Variables on Advice Utilization. Wirtschaftsinformatik
Conference, 19.'
bibtex: '@inproceedings{Leffrang_2023, title={The Broken Leg of Algorithm Appreciation:
An Experimental Study on the Effect of Unobserved Variables on Advice Utilization},
number={19}, booktitle={Wirtschaftsinformatik Conference}, author={Leffrang, Dirk},
year={2023} }'
chicago: 'Leffrang, Dirk. “The Broken Leg of Algorithm Appreciation: An Experimental
Study on the Effect of Unobserved Variables on Advice Utilization.” In Wirtschaftsinformatik
Conference, 2023.'
ieee: 'D. Leffrang, “The Broken Leg of Algorithm Appreciation: An Experimental Study
on the Effect of Unobserved Variables on Advice Utilization,” in Wirtschaftsinformatik
Conference, Paderborn, 2023, no. 19.'
mla: 'Leffrang, Dirk. “The Broken Leg of Algorithm Appreciation: An Experimental
Study on the Effect of Unobserved Variables on Advice Utilization.” Wirtschaftsinformatik
Conference, no. 19, 2023.'
short: 'D. Leffrang, in: Wirtschaftsinformatik Conference, 2023.'
conference:
location: Paderborn
name: Wirtschaftsinformatik
date_created: 2024-01-03T09:50:06Z
date_updated: 2024-01-10T09:53:24Z
department:
- _id: '196'
issue: '19'
keyword:
- Algorithm aversion
- Data
- Decision-making
- Advice-taking
- Human-Computer Interaction
language:
- iso: eng
main_file_link:
- url: 'https://aisel.aisnet.org/wi2023/19 '
publication: Wirtschaftsinformatik Conference
status: public
title: 'The Broken Leg of Algorithm Appreciation: An Experimental Study on the Effect
of Unobserved Variables on Advice Utilization'
type: conference
user_id: '51271'
year: '2023'
...
---
_id: '50431'
abstract:
- lang: eng
text: 'Recommender systems now span the entire customer journey. Amid the multitude
of diversified experi- ences, immersing in cultural events has become a key aspect
of tourism. Cultural events, however, suffer from fleeting lifecycles, evade exact
replication, and invariably lie in the future. In addition, their low standardization
makes harnessing historical data regarding event content or past patron evaluations
intricate. The distinctive traits of events thereby compound the challenge of
the cold-start dilemma in event recommenders. Content-based recommendations stand
as a viable avenue to alleviate this issue, functioning even in scenarios where
item-user information is scarce. Still, the effectiveness of content- based recommendations
often hinges on the quality of the data representation they build upon. In this
study, we explore an array of cutting-edge uni- and multimodal vision and language
foundation models (VL-FMs) for this purpose. Next, we derive content-based recommendations
through a straightforward clustering approach that groups akin events together,
and evaluate the efficacy of the models through a series of online user experiments
across three dimensions: similarity-based evaluation, comparison-based evaluation,
and clustering assignment evaluation. Our experiments generated four major findings.
First, we found that all VL-FMs consistently outperformed a naive baseline of
recommending randomly drawn events. Second, unimodal text-based embeddings were
surprisingly on par or in some cases even superior to multimodal embeddings. Third,
multimodal embeddings yielded arguably more fine-grained and diverse clusters
in comparison to their unimodal counterparts. Finally, we could confirm that cross
event interest is indeed reliant on the perceived similarity of events, resonating
with the notion of similarity in content-based recommendations. All in all, we
believe that leveraging the potential of contemporary FMs for content-based event
recommendations would help address the cold-start problem and propel this field
of research forward in new and exciting ways.'
author:
- first_name: Haya
full_name: Halimeh, Haya
id: '87673'
last_name: Halimeh
- first_name: Florian
full_name: Freese, Florian
last_name: Freese
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
citation:
ama: 'Halimeh H, Freese F, Müller O. Event Recommendations through the Lens of Vision
and Language Foundation Models. In: Workshop on Recommenders in Tourism, Co-Located
with the 17th ACM Conference on Recommender Systems. ; 2023.'
apa: Halimeh, H., Freese, F., & Müller, O. (2023). Event Recommendations through
the Lens of Vision and Language Foundation Models. Workshop on Recommenders
in Tourism, Co-Located with the 17th ACM Conference on Recommender Systems.
Workshop on Recommenders in Tourism, co-located with the 17th ACM Conference on
Recommender Systems.
bibtex: '@inproceedings{Halimeh_Freese_Müller_2023, title={Event Recommendations
through the Lens of Vision and Language Foundation Models}, booktitle={Workshop
on Recommenders in Tourism, co-located with the 17th ACM Conference on Recommender
Systems}, author={Halimeh, Haya and Freese, Florian and Müller, Oliver}, year={2023}
}'
chicago: Halimeh, Haya, Florian Freese, and Oliver Müller. “Event Recommendations
through the Lens of Vision and Language Foundation Models.” In Workshop on
Recommenders in Tourism, Co-Located with the 17th ACM Conference on Recommender
Systems, 2023.
ieee: H. Halimeh, F. Freese, and O. Müller, “Event Recommendations through the Lens
of Vision and Language Foundation Models,” presented at the Workshop on Recommenders
in Tourism, co-located with the 17th ACM Conference on Recommender Systems, 2023.
mla: Halimeh, Haya, et al. “Event Recommendations through the Lens of Vision and
Language Foundation Models.” Workshop on Recommenders in Tourism, Co-Located
with the 17th ACM Conference on Recommender Systems, 2023.
short: 'H. Halimeh, F. Freese, O. Müller, in: Workshop on Recommenders in Tourism,
Co-Located with the 17th ACM Conference on Recommender Systems, 2023.'
conference:
end_date: 2023-09-22
name: Workshop on Recommenders in Tourism, co-located with the 17th ACM Conference
on Recommender Systems
start_date: 2023-09-18
date_created: 2024-01-10T14:20:12Z
date_updated: 2024-01-10T16:10:04Z
department:
- _id: '195'
- _id: '196'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://scholar.google.com/citations?view_op=view_citation&hl=en&user=zBlrdP4AAAAJ&citation_for_view=zBlrdP4AAAAJ:UeHWp8X0CEIC
oa: '1'
publication: Workshop on Recommenders in Tourism, co-located with the 17th ACM Conference
on Recommender Systems
status: public
title: Event Recommendations through the Lens of Vision and Language Foundation Models
type: conference
user_id: '87673'
year: '2023'
...
---
_id: '45270'
abstract:
- lang: eng
text: Clinical depression is a serious mental disorder that poses challenges for
both personal and public health. Millions of people struggle with depression each
year, but for many, the disorder goes undiagnosed or untreated. Over the last
decade, early depression detection on social media emerged as an interdisciplinary
research field. However, there is still a gap in detecting hesitant, depression-susceptible
individuals with minimal direct depressive signals at an early stage. We, therefore,
take up this open point and leverage posts from Reddit to fill the addressed gap.
Our results demonstrate the potential of contemporary Transformer architectures
in yielding promising predictive capabilities for mental health research. Furthermore,
we investigate the model’s interpretability using a surrogate and a topic modeling
approach. Based on our findings, we consider this work as a further step towards
developing a better understanding of mental eHealth and hope that our results
can support the development of future technologies.
author:
- first_name: Haya
full_name: Halimeh, Haya
id: '87673'
last_name: Halimeh
- first_name: Matthew
full_name: Caron, Matthew
id: '60721'
last_name: Caron
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
citation:
ama: 'Halimeh H, Caron M, Müller O. Early Depression Detection with Transformer
Models: Analyzing the Relationship between Linguistic and Psychology-Based Features.
In: Hawaii International Conference on System Sciences. ; 2023.'
apa: 'Halimeh, H., Caron, M., & Müller, O. (2023). Early Depression Detection
with Transformer Models: Analyzing the Relationship between Linguistic and Psychology-Based
Features. Hawaii International Conference on System Sciences. Hawaii International
Conference on System Sciences.'
bibtex: '@inproceedings{Halimeh_Caron_Müller_2023, title={Early Depression Detection
with Transformer Models: Analyzing the Relationship between Linguistic and Psychology-Based
Features}, booktitle={Hawaii International Conference on System Sciences}, author={Halimeh,
Haya and Caron, Matthew and Müller, Oliver}, year={2023} }'
chicago: 'Halimeh, Haya, Matthew Caron, and Oliver Müller. “Early Depression Detection
with Transformer Models: Analyzing the Relationship between Linguistic and Psychology-Based
Features.” In Hawaii International Conference on System Sciences, 2023.'
ieee: 'H. Halimeh, M. Caron, and O. Müller, “Early Depression Detection with Transformer
Models: Analyzing the Relationship between Linguistic and Psychology-Based Features,”
presented at the Hawaii International Conference on System Sciences, 2023.'
mla: 'Halimeh, Haya, et al. “Early Depression Detection with Transformer Models:
Analyzing the Relationship between Linguistic and Psychology-Based Features.”
Hawaii International Conference on System Sciences, 2023.'
short: 'H. Halimeh, M. Caron, O. Müller, in: Hawaii International Conference on
System Sciences, 2023.'
conference:
end_date: 2023-01-06
name: Hawaii International Conference on System Sciences
start_date: 2023-01-03
date_created: 2023-05-25T10:25:21Z
date_updated: 2024-01-10T15:16:37Z
department:
- _id: '195'
- _id: '196'
keyword:
- Social Media and Healthcare Technology
- early depression detection
- liwc
- mental health
- transfer learning
- transformer architectures
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://scholarspace.manoa.hawaii.edu/items/2ddab486-5d2f-4302-8de3-a8b24017da3d
oa: '1'
publication: Hawaii International Conference on System Sciences
publication_status: published
related_material:
link:
- relation: confirmation
url: https://hdl.handle.net/10125/103046
status: public
title: 'Early Depression Detection with Transformer Models: Analyzing the Relationship
between Linguistic and Psychology-Based Features'
type: conference
user_id: '60721'
year: '2023'
...