Team

@inproceedings{neidhardt2026when,

title = {When Should Recommender Systems Not Act?},

author = {Julia Neidhardt and Thomas Elmar Kolb and Ahmadou Wagne and Gwendolyn Rippberger and Ricardo Baeza-Yates},

year  = {2026},

date = {2026-09-01},

urldate = {2026-09-01},

booktitle = {Proceedings of the 20th ACM Conference on Recommender Systems (RecSys '26)},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

keywords = {},

pubstate = {forthcoming},

tppubtype = {inproceedings}

}

@inproceedings{Modre2026,

title = {Interaction Modality and Trust: Investigating System-Driven Conversational Recommendation and Faceted Search},

author = {Laura Modre and Ahmadou Wagne and Thomas Elmar Kolb and Julia Neidhardt},

year  = {2026},

date = {2026-08-24},

booktitle = {Proceedings of the 20th ACM Conference on Recommender Systems},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

keywords = {},

pubstate = {forthcoming},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3742414.3795097,

title = {Prompt to Press: Evaluating Human Perception of AI Involvement in News Writing Across Prompt Specificity},

author = {Uroš Sergaš and Ahmadou Wagne and Thomas Elmar Kolb and Julia Neidhardt and Bruce Ferwerda and Marko Tkalcic},

url = {https://doi.org/10.1145/3742414.3795097},

doi = {10.1145/3742414.3795097},

isbn = {9798400719851},

year  = {2026},

date = {2026-01-01},

booktitle = {Companion Proceedings of the 31st International Conference on Intelligent User Interfaces},

pages = {89–92},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

series = {IUI '26 Companion},

abstract = {Large language models (LLMs) are becoming a common feature in content creation tools, prompting important questions about how design choices influence user trust and engagement in AI-assisted journalism. Beyond output quality, factors such as prompt specificity, model choice, and authorship disclosure are themselves interaction design parameters that influence how users interpret and evaluate AI contributions. Yet, little is known about how these design decisions affect reader perceptions in journalistic contexts. To address this gap, we conducted an experiment with 150 participants who evaluated news articles on the sensitive topic of assisted suicide. The articles systematically varied in authorship (human-written, AI-edited, or AI-generated), stance (pro- or anti-legalization), and prompt specificity (vague, moderate, or highly detailed). Participants rated each article on engagement, subjectivity, and perceived AI involvement, and also provided open-ended justifications for their authorship judgments. Our findings show that prompt specificity and model choice significantly influence perceptions of authorship, underscoring how technical design decisions in AI tools can shape public trust in journalism.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3774935.3802536,

title = {LLM4Good: The 2nd Workshop on Sustainable and Trustworthy Large Language Models for Personalization},

author = {Ahmadou Wagne and Thomas Elmar Kolb and Ashmi Banerjee and Julia Neidhardt and Yashar Deldjoo},

url = {https://doi.org/10.1145/3774935.3802536},

doi = {10.1145/3774935.3802536},

isbn = {9798400723117},

year  = {2026},

date = {2026-01-01},

booktitle = {Proceedings of the 34th ACM Conference on User Modeling, Adaptation and Personalization},

pages = {684–687},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

series = {UMAP '26},

abstract = {Large Language Models (LLMs) are transforming personalized services by enabling adaptive, context-aware recommendations and interactions. However, deploying these models at scale raises significant concerns about environmental impact, fairness, privacy, and trustworthiness, including high energy consumption, biased outputs, privacy breaches, and hallucinations. The LLM4Good workshop was already hosted at UMAP’251 and is a half-day workshop that addresses these challenges by fostering dialogue on sustainable and ethical approaches to LLM-based personalization. Participants will explore energy-efficient techniques, bias mitigation, privacy-preserving methods, and responsible deployment strategies. The workshop aligns with Sustainable Development Goals and Digital Humanism principles. It aims to guide the development of trustworthy, human-centric LLM systems that positively impact education, healthcare, and other domains.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3774935.3803059,

title = {Investigating Preference Elicitation Strategies in Conversational Recommender Systems},

author = {Ahmadou Wagne},

url = {https://doi.org/10.1145/3774935.3803059},

doi = {10.1145/3774935.3803059},

isbn = {9798400723117},

year  = {2026},

date = {2026-01-01},

booktitle = {Proceedings of the 34th ACM Conference on User Modeling, Adaptation and Personalization},

pages = {619–622},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

series = {UMAP '26},

abstract = {This research develops theoretical and methodological foundations for modular, mixed-initiative conversational recommender systems (CRS). It investigates how such systems can be conceptualized and systematically evaluated. The aim is to improve preference elicitation, dialogue management and intent detection by investigating the role of large language models (LLMs) in these tasks. First, a literature review is provided to conceptualize the use of generative models in CRS, and behavioral archetypes in user sessions that inform system design are identified. A user study compares a traditional faceted search interface with system-driven CRS to assess trust, usability and preference extraction quality in order to assess system acceptance. The outlined work extends this prototype to support mixed-initiative dialogues, with components for intent detection, question generation and state-tracking and focuses on a modular evaluation, often missing in the current literature. The project aims to deliver a holistic CRS designed and evaluated with consultancy of end users and platform stakeholders.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3705328.3748010,

title = {A Tutorial on Recent Advances in Generative Conversational Recommender Systems},

author = {Thomas Elmar Kolb and Ahmadou Wagne and Ashmi Banerjee and Fatemeh Nazary and Julia Neidhardt and Yashar Deldjoo and Tommaso Di Noia},

url = {https://doi.org/10.1145/3705328.3748010},

doi = {10.1145/3705328.3748010},

isbn = {9798400713644},

year  = {2025},

date = {2025-01-01},

booktitle = {Proceedings of the Nineteenth ACM Conference on Recommender Systems},

pages = {1420–1422},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

series = {RecSys '25},

abstract = {Conversational recommender systems (CRSs) are increasingly vital for delivering multi-turn, context-aware recommendations. This tutorial provides a concise yet comprehensive exploration of modern generative CRSs, highlighting recent advances in generative AI—such as breakthroughs in large language models and neural generation pipelines, that enhance dialogue management, user modeling, and response generation. In addition, the tutorial addresses core challenges, including data acquisition, multi-turn personalization, and evaluation issues, such as controlling hallucinations, accounting for social factors, and managing ethical considerations, while also discussing emerging risks and novel solutions. Ultimately, participants will be equipped with actionable insights and practical tools for building new conversational recommender systems powered by generative models.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3708319.3727560,

title = {LLM4Good: The 1st Workshop on Sustainable and Trustworthy Large Language Models for Personalization},

author = {Thomas Elmar Kolb and Ashmi Banerjee and Ahmadou Wagne and Julia Neidhardt and Yashar Deldjoo},

url = {https://doi.org/10.1145/3708319.3727560},

doi = {10.1145/3708319.3727560},

isbn = {9798400713996},

year  = {2025},

date = {2025-01-01},

booktitle = {Adjunct Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization},

pages = {385–387},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

series = {UMAP Adjunct '25},

abstract = {Large Language Models (LLMs) are transforming personalized services by enabling adaptive, context-aware recommendations and interactions. However, deploying these models at scale raises significant concerns about environmental impact, fairness, privacy, and trustworthiness, including high energy consumption, biased outputs, privacy breaches, and hallucinations. The LLM4Good workshop is a half-day workshop that addresses these challenges by fostering dialogue on sustainable and ethical approaches to LLM-based personalization. Participants will explore energy-efficient techniques, bias mitigation, privacy-preserving methods, and responsible deployment strategies. The workshop aligns with Sustainable Development Goals and Digital Humanism principles. It aims to guide the development of trustworthy, human-centric LLM systems that positively impact education, healthcare, and other domains.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Wagne_Neidhardt_2024_2,

title = {Can We Integrate Items into Models? Knowledge Editing to Align LLMs with Product Catalogs},

author = {Ahmadou Wagne and Julia Neidhardt},

editor = {Vito Walter Anelli and Pierpaolo Basile and Tommaso Di Noia and Francesco Maria Donini and Antonio Ferrara and Cataldo Musto and Fedelucio Narducci and Azzurra Ragone and Markus Zanker},

year  = {2024},

date = {2024-10-31},

urldate = {2024-10-31},

booktitle = {Sixth Knowledge-aware and Conversational Recommender Systems (KaRS) Workshop @ RecSys 2024},

volume = {3817},

pages = {56-65},

publisher = {CEUR-WS.org},

address = {Bari, Italy},

abstract = {This study explores the potential of knowledge editing techniques to enhance Large Language Models (LLMs)

for Conversational Recommender Systems (CRS). While LLMs like GPT, Llama, and Gemini have advanced

conversational capabilities, they face challenges in representing dynamic, real-world item catalogs, often leading to

inaccuracies and hallucinations in recommendations. This research preliminarily investigates whether knowledge

editing can address these limitations by updating the internal knowledge of LLMs, thereby improving the

accuracy of product information without full model retraining. Using the open-source Llama2 model, we apply

two knowledge editing methods (GRACE and r-ROME) on a dataset of notebook listings. Our findings demonstrate

improvements in the model’s ability to accurately represent product features, with r-ROME achieving the highest

gain, while not decreasing model efficiency. The study highlights the perspective of utilizing knowledge editing

to enhance CRS and suggests future work to explore broader applications and impacts on recommender systems

performance},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Wagne_Neidhardt_2024,

title = {What to compare? Towards understanding user sessions on price comparison platforms},

author = {Ahmadou Wagne and Julia Neidhardt},

editor = {Tommaso Di Noia and Pasquale Lops and Thorsten Joachims and Katrien Verbert and Pablo Castells and Zhenhua Dong and Ben London},

doi = {https://doi.org/10.1145/3640457.3691717},

isbn = {979-8-4007-1127-5},

year  = {2024},

date = {2024-10-08},

urldate = {2024-10-08},

booktitle = {RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems},

pages = {1158 - 1162},

publisher = {Association for Computing Machinery},

address = {New York, NY, United States},

abstract = {E-commerce and online shopping have become integral to the lives of many, with various user behavior types historically identified. Beyond deciding what to buy, determining where to make a purchase has led to the importance of price comparison platforms. However, user behavior on these platforms remains underexplored. Furthermore, web analytics often struggle with tracking users over time and deriving meaningful user types from data. This paper addresses these gaps by defining session types through the analysis and clustering of user logs from a major price comparison platform. The study identifies six distinct session clusters: quick peek, major purchase, constraint-based browsing, knowledge seeking, search and browse and heavy browsing. These findings are intended to inform the design and development of a conversational recommender system (CRS). Often, CRS development occurs without adequate consideration of the existing system into which it will be integrated. The study’s findings, derived from both quantitative analysis and expert interviews, provide valuable contributions, including identified session clusters, their interpretation and indicators on which users might benefit from a CRS on these platforms.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Wagne_Neidhardt_Kolb_2024,

title = {PopAut: An Annotated Corpus for Populism Detection in Austrian News Comments},

author = {Ahmadou Wagne and Julia Neidhardt and Thomas Elmar Kolb},

editor = {Nicoletta Calzolari and Min-Yen Kan and Veronique Hoste and Alessandro Lenci and Sakriani Sakti and Nianwen Xue},

url = {https://aclanthology.org/2024.lrec-main.1128},

year  = {2024},

date = {2024-05-01},

urldate = {2024-05-01},

booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},

pages = {12879–12892},

publisher = {ELRA and ICCL},

address = {Torino, Italy},

abstract = {Populism is a phenomenon that is noticeably present in the political landscape of various countries over the past decades. While populism expressed by politicians has been thoroughly examined in the literature, populism expressed by citizens is still underresearched, especially when it comes to its automated detection in text. This work presents the PopAut corpus, which is the first annotated corpus of news comments for populism in the German language. It features 1,200 comments collected between 2019-2021 that are annotated for populist motives anti-elitism, people-centrism and people-sovereignty. Following the definition of Cas Mudde, populism is seen as a thin ideology. This work shows that annotators reach a high agreement when labeling news comments for these motives. The data set is collected to serve as the basis for automated populism detection using machine-learning methods. By using transformer-based models, we can outperform existing dictionaries tailored for automated populism detection in German social media content. Therefore our work provides a rich resource for future work on the classification of populist user comments in the German language.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{	20.500.12708_191183,

title = {Potentials of Combining Local Knowledge and LLMs for Recommender Systems},

author = {Thomas Elmar Kolb and Ahmadou Wagne and Mete Sertkan and Julia Neidhardt},

editor = {Vito Walter Anelli and Pierpaolo Basile and Gerard De Melo and Francesco Donini and Antonio Ferrara and Cataldo Musto and Fedelucio Narducci and Azzurra Ragone and Markus Zanker},

doi = {10.34726/5334},

year  = {2023},

date = {2023-01-01},

volume = {3560},

pages = {61–64},

publisher = {CEUR-WS.org},

series = {CEUR Workshop Proceedings},

abstract = {LLMs have revolutionized the understanding and generation of natural language, offering new possibilities for enhancing recommendation systems. In previous studies, LLMs exploit their global knowledge to provide zero- or few-shot recommendations. In this work, we aim to highlight the opportunities that LLMs pose to enrich the field of recommender systems combined with local knowledge. We propose to view recommender systems combined with LLMs from a broader perspective, recognizing them not merely as another method to replace existing recommendation approaches, but rather as a complementary and powerful approach to enhance and augment the overall recommendation process.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}