---
_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. <i>Communications
    of the Association for Information Systems</i>.
  apa: zur Heiden, P., Halimeh, H., Hansmeier, P., Vorbohle, C., Althaus, M., Beverungen,
    D., Kundisch, D., &#38; Müller, O. (n.d.). Data Spaces for Heterogeneous Data
    Ecosystems – Findings from a Design Study in the Cultural Sector. <i>Communications
    of the Association for Information Systems</i>.
  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.” <i>Communications of the Association for Information Systems</i>, n.d.
  ieee: P. zur Heiden <i>et al.</i>, “Data Spaces for Heterogeneous Data Ecosystems
    – Findings from a Design Study in the Cultural Sector,” <i>Communications of the
    Association for Information Systems</i>.
  mla: zur Heiden, Philipp, et al. “Data Spaces for Heterogeneous Data Ecosystems
    – Findings from a Design Study in the Cultural Sector.” <i>Communications of the
    Association for Information Systems</i>.
  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: '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: <i>International Conference on Information Systems Development</i>. University
    of Gdansk, Department of Business Informatics &#38; University of Belgrade, Faculty
    of Organizational Sciences; 2025. doi:<a href="https://doi.org/10.62036/isd.2025.68">10.62036/isd.2025.68</a>'
  apa: 'Halimeh, H., Freese, F., &#38; Müller, O. (2025). LLMs For Warm and Cold Next-Item
    Recommendation: A Comparative Study across Zero-Shot Prompting, In-Context Learning
    and Fine-Tuning. <i>International Conference on Information Systems Development</i>.
    <a href="https://doi.org/10.62036/isd.2025.68">https://doi.org/10.62036/isd.2025.68</a>'
  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={<a href="https://doi.org/10.62036/isd.2025.68">10.62036/isd.2025.68</a>},
    booktitle={International Conference on Information Systems Development}, publisher={University
    of Gdansk, Department of Business Informatics &#38; 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 <i>International Conference on Information Systems
    Development</i>. University of Gdansk, Department of Business Informatics &#38;
    University of Belgrade, Faculty of Organizational Sciences, 2025. <a href="https://doi.org/10.62036/isd.2025.68">https://doi.org/10.62036/isd.2025.68</a>.'
  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: <a href="https://doi.org/10.62036/isd.2025.68">10.62036/isd.2025.68</a>.'
  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.”
    <i>International Conference on Information Systems Development</i>, University
    of Gdansk, Department of Business Informatics &#38; University of Belgrade, Faculty
    of Organizational Sciences, 2025, doi:<a href="https://doi.org/10.62036/isd.2025.68">10.62036/isd.2025.68</a>.'
  short: 'H. Halimeh, F. Freese, O. Müller, in: International Conference on Information
    Systems Development, University of Gdansk, Department of Business Informatics
    &#38; 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: <i>2025 27th
    International Conference on Business Informatics (CBI)</i>. IEEE; 2025. doi:<a
    href="https://doi.org/10.1109/cbi68102.2025.00019">10.1109/cbi68102.2025.00019</a>'
  apa: 'Halimeh, H., &#38; zur Heiden, P. (2025). Preserving Sovereignty and Privacy
    for Personalization: Designing a Federated Recommendation System for Data Spaces.
    <i>2025 27th International Conference on Business Informatics (CBI)</i>. <a href="https://doi.org/10.1109/cbi68102.2025.00019">https://doi.org/10.1109/cbi68102.2025.00019</a>'
  bibtex: '@inproceedings{Halimeh_zur Heiden_2025, title={Preserving Sovereignty and
    Privacy for Personalization: Designing a Federated Recommendation System for Data
    Spaces}, DOI={<a href="https://doi.org/10.1109/cbi68102.2025.00019">10.1109/cbi68102.2025.00019</a>},
    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 <i>2025 27th International Conference on Business Informatics (CBI)</i>. IEEE,
    2025. <a href="https://doi.org/10.1109/cbi68102.2025.00019">https://doi.org/10.1109/cbi68102.2025.00019</a>.'
  ieee: 'H. Halimeh and P. zur Heiden, “Preserving Sovereignty and Privacy for Personalization:
    Designing a Federated Recommendation System for Data Spaces,” 2025, doi: <a href="https://doi.org/10.1109/cbi68102.2025.00019">10.1109/cbi68102.2025.00019</a>.'
  mla: 'Halimeh, Haya, and Philipp zur Heiden. “Preserving Sovereignty and Privacy
    for Personalization: Designing a Federated Recommendation System for Data Spaces.”
    <i>2025 27th International Conference on Business Informatics (CBI)</i>, IEEE,
    2025, doi:<a href="https://doi.org/10.1109/cbi68102.2025.00019">10.1109/cbi68102.2025.00019</a>.'
  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:<a href="https://doi.org/10.1007/978-3-032-13342-7">10.1007/978-3-032-13342-7</a>'
  apa: 'Halimeh, H., &#38; Müller, O. (2025). <i>Towards Greener Choices: Decision
    Information Nudging for Sustainability-Aware Recommender Explanations</i>.  The
    Second International Workshop on Recommender Systems for Sustainability and Social
    Good, RecSoGood 2025, Prague, Czech Republic. <a href="https://doi.org/10.1007/978-3-032-13342-7">https://doi.org/10.1007/978-3-032-13342-7</a>'
  bibtex: '@inproceedings{Halimeh_Müller_2025, title={Towards Greener Choices: Decision
    Information Nudging for Sustainability-Aware Recommender Explanations}, DOI={<a
    href="https://doi.org/10.1007/978-3-032-13342-7">10.1007/978-3-032-13342-7</a>},
    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. <a href="https://doi.org/10.1007/978-3-032-13342-7">https://doi.org/10.1007/978-3-032-13342-7</a>.'
  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: <a href="https://doi.org/10.1007/978-3-032-13342-7">10.1007/978-3-032-13342-7</a>.'
  mla: 'Halimeh, Haya, and Oliver Müller. <i>Towards Greener Choices: Decision Information
    Nudging for Sustainability-Aware Recommender Explanations</i>. 2025, doi:<a href="https://doi.org/10.1007/978-3-032-13342-7">10.1007/978-3-032-13342-7</a>.'
  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. <i>Positionspapier Use Case 1: Vernetzte
    Kulturplattformen</i>.; 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., &#38; Steinhäuser, M. (2025). <i>Positionspapier Use Case 1: Vernetzte
    Kulturplattformen</i>.'
  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. <i>Positionspapier Use
    Case 1: Vernetzte Kulturplattformen</i>. Universität Paderborn, SICP, 2025.'
  ieee: 'M. Althaus <i>et al.</i>, <i>Positionspapier Use Case 1: Vernetzte Kulturplattformen</i>.
    Universität Paderborn, SICP, 2025.'
  mla: 'Althaus, Maike, et al. <i>Positionspapier Use Case 1: Vernetzte Kulturplattformen</i>.
    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: '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: <i>Workshop on Recommenders in Tourism, Co-Located
    with the 17th ACM Conference on Recommender Systems</i>. ; 2023.'
  apa: Halimeh, H., Freese, F., &#38; Müller, O. (2023). Event Recommendations through
    the Lens of Vision and Language Foundation Models. <i>Workshop on Recommenders
    in Tourism, Co-Located with the 17th ACM Conference on Recommender Systems</i>.
    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 <i>Workshop on
    Recommenders in Tourism, Co-Located with the 17th ACM Conference on Recommender
    Systems</i>, 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.” <i>Workshop on Recommenders in Tourism, Co-Located
    with the 17th ACM Conference on Recommender Systems</i>, 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: <i>Hawaii International Conference on System Sciences</i>. ; 2023.'
  apa: 'Halimeh, H., Caron, M., &#38; Müller, O. (2023). Early Depression Detection
    with Transformer Models: Analyzing the Relationship between Linguistic and Psychology-Based
    Features. <i>Hawaii International Conference on System Sciences</i>. 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 <i>Hawaii International Conference on System Sciences</i>, 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.”
    <i>Hawaii International Conference on System Sciences</i>, 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'
...