Publications
Explore the publications from our RecSys laboratory.
1.
Wagne, Ahmadou; Foll, Elen Le; Frantz, Florentine; Lasser, Jana
Giving the outrage a name – how researchers are challenging employment conditions under the hashtags #IchBinHanna and #IchBinReyhan Journal Article
In: Information, Communication & Society, pp. 1-27, 2025.
@article{Wagne_2024,
title = {Giving the outrage a name – how researchers are challenging employment conditions under the hashtags #IchBinHanna and #IchBinReyhan},
author = {Ahmadou Wagne and Elen Le Foll and Florentine Frantz and Jana Lasser},
editor = {Dan Mercea},
doi = {https://doi.org/10.1080/1369118X.2025.2452273},
year = {2025},
date = {2025-01-20},
urldate = {2025-01-20},
journal = {Information, Communication & Society},
pages = {1-27},
abstract = {Outraged by the release of a ministerial video in which short-term employment contracts in German academia were lauded through the embodiment of fictitious doctoral researcher Hanna, thousands of researchers rallied behind the hashtags #IchBinHanna & #IchBinReyhan to vent their frustrations about precarious academic employment in Germany. The emerging connective action attracted a comparatively large number of researchers in a short period of time, stayed active for over two years, elicited reactions from policymakers and the media, and influenced current legislative developments in Germany. We analyse the discourse of over 45,000 tweets related to the movement from its onset in June 2021 to March 2023. Using a mixed-methods approach that combines machine-learning, corpus-linguistic, and qualitative analysis methods, we aim to distil the factors that led to the movement’s considerable success. The fast growth of the movement was likely driven by the use of an easy-to-personalise action frame and the large variety of discussion topics, facilitating the involvement of different groups of academics across career levels and employment conditions. Our analysis of the linguistic characteristics of the discourse reveals largely positive, constructive, and active exchanges, in which many of the most salient keywords are lexical verbs. Our analyses offer an explanation for the continued involvement of many activists and the successful translation of solutions developed by the movement to news reports and proposed law amendments, despite an absence of coordination by any formal organisation.},
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Outraged by the release of a ministerial video in which short-term employment contracts in German academia were lauded through the embodiment of fictitious doctoral researcher Hanna, thousands of researchers rallied behind the hashtags #IchBinHanna & #IchBinReyhan to vent their frustrations about precarious academic employment in Germany. The emerging connective action attracted a comparatively large number of researchers in a short period of time, stayed active for over two years, elicited reactions from policymakers and the media, and influenced current legislative developments in Germany. We analyse the discourse of over 45,000 tweets related to the movement from its onset in June 2021 to March 2023. Using a mixed-methods approach that combines machine-learning, corpus-linguistic, and qualitative analysis methods, we aim to distil the factors that led to the movement’s considerable success. The fast growth of the movement was likely driven by the use of an easy-to-personalise action frame and the large variety of discussion topics, facilitating the involvement of different groups of academics across career levels and employment conditions. Our analysis of the linguistic characteristics of the discourse reveals largely positive, constructive, and active exchanges, in which many of the most salient keywords are lexical verbs. Our analyses offer an explanation for the continued involvement of many activists and the successful translation of solutions developed by the movement to news reports and proposed law amendments, despite an absence of coordination by any formal organisation.
2.
Steindl, Bernhard; Kolb, Thomas Elmar; Neidhardt, Julia
Beyond Demographics: Evaluating News Recommender Systems Fairness Through Behavioural Communities Proceedings Article
In: Adjunct Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization, pp. 13–17, Association for Computing Machinery, New York, NY, USA, 2025, ISBN: 9798400713996.
@inproceedings{10.1145/3708319.3733694,
title = {Beyond Demographics: Evaluating News Recommender Systems Fairness Through Behavioural Communities},
author = {Bernhard Steindl and Thomas Elmar Kolb and Julia Neidhardt},
url = {https://doi.org/10.1145/3708319.3733694},
doi = {10.1145/3708319.3733694},
isbn = {9798400713996},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
booktitle = {Adjunct Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization},
pages = {13–17},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
series = {UMAP Adjunct '25},
abstract = {Fairness in recommender systems is often framed around demographic attributes. In this work, we explore a novel direction—evaluating fairness across latent behavioural communities derived from user interactions on a real-world news platform. Using graph-based community detection (Louvain and Infomap), we identify large user groups and examine how different network modelling choices affect fairness outcomes in both traditional and fairness-aware recommender systems. Experiments on an Austrian news dataset reveal that small changes in graph construction considerably impact community formation and recommendation quality. Notably, fairness-aware algorithms show only marginal improvements over standard approaches, underscoring the complexity of achieving equitable outcomes in real-world systems and raising important questions for future research.},
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Fairness in recommender systems is often framed around demographic attributes. In this work, we explore a novel direction—evaluating fairness across latent behavioural communities derived from user interactions on a real-world news platform. Using graph-based community detection (Louvain and Infomap), we identify large user groups and examine how different network modelling choices affect fairness outcomes in both traditional and fairness-aware recommender systems. Experiments on an Austrian news dataset reveal that small changes in graph construction considerably impact community formation and recommendation quality. Notably, fairness-aware algorithms show only marginal improvements over standard approaches, underscoring the complexity of achieving equitable outcomes in real-world systems and raising important questions for future research.
3.
Kolb, Thomas Elmar; Wagne, Ahmadou; Banerjee, Ashmi; Nazary, Fatemeh; Neidhardt, Julia; Deldjoo, Yashar; Noia, Tommaso Di
A Tutorial on Recent Advances in Generative Conversational Recommender Systems Proceedings Article
In: Proceedings of the Nineteenth ACM Conference on Recommender Systems, pp. 1420–1422, Association for Computing Machinery, New York, NY, USA, 2025, ISBN: 9798400713644.
@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.},
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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.
4.
Kolb, Thomas Elmar; Nalis, Irina; Neidhardt, Julia
Bridging Preferences: Multi-Stakeholder Insights on Ideal News Recommendations Proceedings Article
In: Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization, pp. 268–272, Association for Computing Machinery, New York, NY, USA, 2025, ISBN: 9798400713132.
@inproceedings{10.1145/3699682.3728355,
title = {Bridging Preferences: Multi-Stakeholder Insights on Ideal News Recommendations},
author = {Thomas Elmar Kolb and Irina Nalis and Julia Neidhardt},
url = {https://doi.org/10.1145/3699682.3728355},
doi = {10.1145/3699682.3728355},
isbn = {9798400713132},
year = {2025},
date = {2025-01-01},
booktitle = {Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization},
pages = {268–272},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
series = {UMAP '25},
abstract = {In the evolving realm of recommender systems, our study contributes to the understanding of potential improvements in news recommendation beyond accuracy. Central to our research is the integration of insights from news industry experts and prospective readers, compared with automated news recommendations. We conducted a labeling study with 168 articles, using Best-Worst Scaling (BWS) for ranking and topic modeling. This approach enabled a thorough examination of stakeholder expectations for ideal reading recommendations, specifically by investigating the gap between stated and revealed preferences. Our findings show alignment in ranking behavior among journalists, prospective readers, and the BM-25 algorithm. However, preferences for different beyond-accuracy measures varied. Accompanying this work, a corpus of news articles and the labeled rankings have been made available.},
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In the evolving realm of recommender systems, our study contributes to the understanding of potential improvements in news recommendation beyond accuracy. Central to our research is the integration of insights from news industry experts and prospective readers, compared with automated news recommendations. We conducted a labeling study with 168 articles, using Best-Worst Scaling (BWS) for ranking and topic modeling. This approach enabled a thorough examination of stakeholder expectations for ideal reading recommendations, specifically by investigating the gap between stated and revealed preferences. Our findings show alignment in ranking behavior among journalists, prospective readers, and the BM-25 algorithm. However, preferences for different beyond-accuracy measures varied. Accompanying this work, a corpus of news articles and the labeled rankings have been made available.
5.
Kolb, Thomas Elmar; Banerjee, Ashmi; Wagne, Ahmadou; Neidhardt, Julia; Deldjoo, Yashar
LLM4Good: The 1st Workshop on Sustainable and Trustworthy Large Language Models for Personalization Proceedings Article
In: Adjunct Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization, pp. 385–387, Association for Computing Machinery, New York, NY, USA, 2025, ISBN: 9798400713996.
@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.},
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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.
6.
Wagne, Ahmadou; Neidhardt, Julia
Can We Integrate Items into Models? Knowledge Editing to Align LLMs with Product Catalogs Proceedings Article
In: Anelli, Vito Walter; Basile, Pierpaolo; Noia, Tommaso Di; Donini, Francesco Maria; Ferrara, Antonio; Musto, Cataldo; Narducci, Fedelucio; Ragone, Azzurra; Zanker, Markus (Ed.): Sixth Knowledge-aware and Conversational Recommender Systems (KaRS) Workshop @ RecSys 2024, pp. 56-65, CEUR-WS.org, Bari, Italy, 2024.
@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}
}
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
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
7.
Wagne, Ahmadou; Neidhardt, Julia
What to compare? Towards understanding user sessions on price comparison platforms Proceedings Article
In: Noia, Tommaso Di; Lops, Pasquale; Joachims, Thorsten; Verbert, Katrien; Castells, Pablo; Dong, Zhenhua; London, Ben (Ed.): RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems, pp. 1158 – 1162, Association for Computing Machinery, New York, NY, United States, 2024, ISBN: 979-8-4007-1127-5.
@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}
}
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.
8.
Wagne, Ahmadou; Neidhardt, Julia; Kolb, Thomas Elmar
PopAut: An Annotated Corpus for Populism Detection in Austrian News Comments Proceedings Article
In: Calzolari, Nicoletta; Kan, Min-Yen; Hoste, Veronique; Lenci, Alessandro; Sakti, Sakriani; Xue, Nianwen (Ed.): Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pp. 12879–12892, ELRA and ICCL, Torino, Italy, 2024.
@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},
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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.
9.
Godolja, Dante; Kolb, Thomas Elmar; Neidhardt, Julia
Unlocking the Potential of Content-Based Restaurant Recommender Systems Proceedings Article
In: Berezina, Katerina; Nixon, Lyndon; Tuomi, Aarni (Ed.): Information and Communication Technologies in Tourism 2024, pp. 239–244, Springer Nature Switzerland, Cham, 2024, ISBN: 978-3-031-58839-6.
@inproceedings{10.1007/978-3-031-58839-6_26,
title = {Unlocking the Potential of Content-Based Restaurant Recommender Systems},
author = {Dante Godolja and Thomas Elmar Kolb and Julia Neidhardt},
editor = {Katerina Berezina and Lyndon Nixon and Aarni Tuomi},
isbn = {978-3-031-58839-6},
year = {2024},
date = {2024-01-01},
booktitle = {Information and Communication Technologies in Tourism 2024},
pages = {239–244},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {Content-based restaurant recommender systems use features such as cuisine type, price range, and location to suggest dining options to users. Current research explores ways to improve their effectiveness. In this work, we explore different ideas on how to build a recommender system. We explore TF-IDF as a baseline and the state-of-the-art model SBERT. These ideas are tested on a real-world data-set of a digital restaurant guide. Evaluation involves both qualitative assessment by a domain expert and quantitative analysis. The results show that, with proper preprocessing, TF-IDF can achieve similar scores to SBERT and, depending on the scenario, even better results. However, SBERT still provides more novel recommendations than TF-IDF. Depending on the scenario, both models can be used to generate meaningful restaurant recommendations. However, more implicit aspects like a restaurant's atmosphere can hardly be captured by these models.},
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Content-based restaurant recommender systems use features such as cuisine type, price range, and location to suggest dining options to users. Current research explores ways to improve their effectiveness. In this work, we explore different ideas on how to build a recommender system. We explore TF-IDF as a baseline and the state-of-the-art model SBERT. These ideas are tested on a real-world data-set of a digital restaurant guide. Evaluation involves both qualitative assessment by a domain expert and quantitative analysis. The results show that, with proper preprocessing, TF-IDF can achieve similar scores to SBERT and, depending on the scenario, even better results. However, SBERT still provides more novel recommendations than TF-IDF. Depending on the scenario, both models can be used to generate meaningful restaurant recommendations. However, more implicit aspects like a restaurant’s atmosphere can hardly be captured by these models.
10.
Scholz, Felix; Kolb, Thomas Elmar; Neidhardt, Julia
Classifying User Roles in Online News Forums: A Model for User Interaction and Behavior Analysis Proceedings Article
In: Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization, pp. 240–249, Association for Computing Machinery, Cagliari, Italy, 2024, ISBN: 9798400704666.
@inproceedings{10.1145/3631700.3665187,
title = {Classifying User Roles in Online News Forums: A Model for User Interaction and Behavior Analysis},
author = {Felix Scholz and Thomas Elmar Kolb and Julia Neidhardt},
url = {https://doi.org/10.1145/3631700.3665187},
doi = {10.1145/3631700.3665187},
isbn = {9798400704666},
year = {2024},
date = {2024-01-01},
booktitle = {Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization},
pages = {240–249},
publisher = {Association for Computing Machinery},
address = {Cagliari, Italy},
series = {UMAP Adjunct '24},
abstract = {The growing exchange of opinions in online news forums brings together a diverse cross-section of users with varying opinions and motivations. Understanding these behaviors is crucial for unraveling the composition of these large user bases. This study proposes an explainable model aimed at classifying users based on their activity and interaction patterns in online news forums. The model leverages exploratory and statistical data analysis to reveal recurring behaviors and provides a tool to analyze the evolution of large user communities, offering an overview of their composition. The model identifies six active roles: Taciturn, Silent Voter, Regular, Conversationalist, Power User, and Celebrity, and one inactive role, Lurker. The model was evaluated for its predictive power, achieving a macro F1 score of 0.8632, demonstrating its robustness. By applying the model to a long-term dataset from the online news forum derStandard.at, an analysis of role distribution over time was conducted. The results indicated a gradual increase in user activity within the forum. Moreover, the study assessed the co-occurrence of roles in users’ long-term behavior and measured the frequency of role changes. This analysis aimed to determine whether users have consistent roles or exhibit various roles, which may depend on time or context.},
keywords = {},
pubstate = {published},
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The growing exchange of opinions in online news forums brings together a diverse cross-section of users with varying opinions and motivations. Understanding these behaviors is crucial for unraveling the composition of these large user bases. This study proposes an explainable model aimed at classifying users based on their activity and interaction patterns in online news forums. The model leverages exploratory and statistical data analysis to reveal recurring behaviors and provides a tool to analyze the evolution of large user communities, offering an overview of their composition. The model identifies six active roles: Taciturn, Silent Voter, Regular, Conversationalist, Power User, and Celebrity, and one inactive role, Lurker. The model was evaluated for its predictive power, achieving a macro F1 score of 0.8632, demonstrating its robustness. By applying the model to a long-term dataset from the online news forum derStandard.at, an analysis of role distribution over time was conducted. The results indicated a gradual increase in user activity within the forum. Moreover, the study assessed the co-occurrence of roles in users’ long-term behavior and measured the frequency of role changes. This analysis aimed to determine whether users have consistent roles or exhibit various roles, which may depend on time or context.
11.
Nalis, Irina; Sippl, Tobias; Kolb, Thomas Elmar; Neidhardt, Julia
Navigating Serendipity – An Experimental User Study On The Interplay of Trust and Serendipity In Recommender Systems Proceedings Article
In: Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization, pp. 386–393, Association for Computing Machinery, Cagliari, Italy, 2024, ISBN: 9798400704666.
@inproceedings{10.1145/3631700.3664901,
title = {Navigating Serendipity - An Experimental User Study On The Interplay of Trust and Serendipity In Recommender Systems},
author = {Irina Nalis and Tobias Sippl and Thomas Elmar Kolb and Julia Neidhardt},
url = {https://doi.org/10.1145/3631700.3664901},
doi = {10.1145/3631700.3664901},
isbn = {9798400704666},
year = {2024},
date = {2024-01-01},
booktitle = {Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization},
pages = {386–393},
publisher = {Association for Computing Machinery},
address = {Cagliari, Italy},
series = {UMAP Adjunct '24},
abstract = {Recommender systems play a crucial role in our daily lives, constantly evolving to meet the diverse needs of users. As the pursuit of improved user experiences continues, metrics such as serendipity have emerged within the realm of beyond-accuracy paradigms. However, integrating serendipitous recommendations presents complex challenges, necessitating a delicate balance between novelty, relevance, and user engagement. In this interdisciplinary experimental user study, we address these challenges within the context of a book recommender system. By investigating the impact of interface design changes on user trust, a key determinant of satisfaction with serendipitous recommendations, we measured trust levels for both individual recommended items and the recommender system as a whole. Our findings indicate that while interface enhancements did not yield significant increases in trust, they did notably elevate serendipity ratings for previously unknown books. These results highlight the intricate interplay between technical and psychological factors in the design of recommender systems, emphasizing the importance of human-centered approaches in the creation of more responsible AI applications. This research contributes to ongoing discussions surrounding user-centric recommendation systems and aligns with broader themes of digital humanism and responsible AI.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Recommender systems play a crucial role in our daily lives, constantly evolving to meet the diverse needs of users. As the pursuit of improved user experiences continues, metrics such as serendipity have emerged within the realm of beyond-accuracy paradigms. However, integrating serendipitous recommendations presents complex challenges, necessitating a delicate balance between novelty, relevance, and user engagement. In this interdisciplinary experimental user study, we address these challenges within the context of a book recommender system. By investigating the impact of interface design changes on user trust, a key determinant of satisfaction with serendipitous recommendations, we measured trust levels for both individual recommended items and the recommender system as a whole. Our findings indicate that while interface enhancements did not yield significant increases in trust, they did notably elevate serendipity ratings for previously unknown books. These results highlight the intricate interplay between technical and psychological factors in the design of recommender systems, emphasizing the importance of human-centered approaches in the creation of more responsible AI applications. This research contributes to ongoing discussions surrounding user-centric recommendation systems and aligns with broader themes of digital humanism and responsible AI.
12.
Huebner, Blake; Kolb, Thomas Elmar; Neidhardt, Julia
Evaluating Group Fairness in News Recommendations: A Comparative Study of Algorithms and Metrics Proceedings Article
In: Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization, pp. 337–346, Association for Computing Machinery, Cagliari, Italy, 2024, ISBN: 9798400704666.
@inproceedings{10.1145/3631700.3664897,
title = {Evaluating Group Fairness in News Recommendations: A Comparative Study of Algorithms and Metrics},
author = {Blake Huebner and Thomas Elmar Kolb and Julia Neidhardt},
url = {https://doi.org/10.1145/3631700.3664897},
doi = {10.1145/3631700.3664897},
isbn = {9798400704666},
year = {2024},
date = {2024-01-01},
booktitle = {Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization},
pages = {337–346},
publisher = {Association for Computing Machinery},
address = {Cagliari, Italy},
series = {UMAP Adjunct '24},
abstract = {Beyond accuracy metrics, such as fairness and diversity, have become widely studied topics in recommender systems. Improving these metrics is important not only from an ethical and legal perspective, but can also improve overall user satisfaction. Although these metrics are widely discussed, very little empirical research has been done, especially comparing multiple algorithms across different metrics. This work explores the role of fairness and diversity in news recommender systems, specifically in the context of the Austrian media landscape. This study aims to identify the most effective approaches for generating fair and diverse news recommendations, while addressing the potential negative consequences of biased recommendations and filter bubbles, such as societal polarization and the suppression of information. This includes an extensive literature review of relevant group unfairness metrics and state-of-the-art fairness-aware algorithms. A dataset of articles from an Austrian newspaper was used for empirical research, with analysis performed on fairness, and diversity of recommendations. The key message of the study is that accuracy and fairness can be achieved simultaneously with the right modeling approach, while diversity can be held constant using these modeling techniques. The study recommends the use of Personalized Fairness based on Causal Notion models for accuracy and reducing certain unfairness metrics, and finds Fairness Objectives for Collaborative Filtering models more effective at reducing other types of unfairness. The findings contribute to the field by demonstrating the importance of incorporating these metrics into the design and evaluation of recommender systems.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Beyond accuracy metrics, such as fairness and diversity, have become widely studied topics in recommender systems. Improving these metrics is important not only from an ethical and legal perspective, but can also improve overall user satisfaction. Although these metrics are widely discussed, very little empirical research has been done, especially comparing multiple algorithms across different metrics. This work explores the role of fairness and diversity in news recommender systems, specifically in the context of the Austrian media landscape. This study aims to identify the most effective approaches for generating fair and diverse news recommendations, while addressing the potential negative consequences of biased recommendations and filter bubbles, such as societal polarization and the suppression of information. This includes an extensive literature review of relevant group unfairness metrics and state-of-the-art fairness-aware algorithms. A dataset of articles from an Austrian newspaper was used for empirical research, with analysis performed on fairness, and diversity of recommendations. The key message of the study is that accuracy and fairness can be achieved simultaneously with the right modeling approach, while diversity can be held constant using these modeling techniques. The study recommends the use of Personalized Fairness based on Causal Notion models for accuracy and reducing certain unfairness metrics, and finds Fairness Objectives for Collaborative Filtering models more effective at reducing other types of unfairness. The findings contribute to the field by demonstrating the importance of incorporating these metrics into the design and evaluation of recommender systems.
13.
Kolb, Thomas Elmar
Enhancing Cross-Domain Recommender Systems with LLMs: Evaluating Bias and Beyond-Accuracy Measures Proceedings Article
In: Proceedings of the 18th ACM Conference on Recommender Systems, pp. 1388–1394, Association for Computing Machinery, Bari, Italy, 2024, ISBN: 9798400705052.
@inproceedings{10.1145/3640457.3688027,
title = {Enhancing Cross-Domain Recommender Systems with LLMs: Evaluating Bias and Beyond-Accuracy Measures},
author = {Thomas Elmar Kolb},
url = {https://doi.org/10.1145/3640457.3688027},
doi = {10.1145/3640457.3688027},
isbn = {9798400705052},
year = {2024},
date = {2024-01-01},
booktitle = {Proceedings of the 18th ACM Conference on Recommender Systems},
pages = {1388–1394},
publisher = {Association for Computing Machinery},
address = {Bari, Italy},
series = {RecSys '24},
abstract = {The research domain of recommender systems is rapidly evolving. Initially, optimization efforts focused primarily on accuracy. However, recent research has highlighted the importance of addressing bias and beyond-accuracy measures such as novelty, diversity, and serendipity. With the rise of multi-domain recommender systems, the need to re-examine bias and beyond-accuracy measures in cross-domain settings has become crucial. Traditional methods face challenges such as cold-start problems, which can potentially be mitigated by leveraging LLMs. This proposed work investigates how LLM-based recommendation methods can enhance cross-domain recommender systems, focusing on identifying, measuring, and mitigating bias while evaluating the impact of beyond-accuracy measures. We aim to provide new insights by comparing traditional and LLM-based systems within a real-world environment encompassing the domains of news, books, and various lifestyle areas. Our research seeks to address the outlined gaps and develop effective evaluation strategies for the unique challenges posed by LLMs in cross-domain recommender systems.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The research domain of recommender systems is rapidly evolving. Initially, optimization efforts focused primarily on accuracy. However, recent research has highlighted the importance of addressing bias and beyond-accuracy measures such as novelty, diversity, and serendipity. With the rise of multi-domain recommender systems, the need to re-examine bias and beyond-accuracy measures in cross-domain settings has become crucial. Traditional methods face challenges such as cold-start problems, which can potentially be mitigated by leveraging LLMs. This proposed work investigates how LLM-based recommendation methods can enhance cross-domain recommender systems, focusing on identifying, measuring, and mitigating bias while evaluating the impact of beyond-accuracy measures. We aim to provide new insights by comparing traditional and LLM-based systems within a real-world environment encompassing the domains of news, books, and various lifestyle areas. Our research seeks to address the outlined gaps and develop effective evaluation strategies for the unique challenges posed by LLMs in cross-domain recommender systems.
14.
Steindl, Bernhard
Evaluating the fairness of news recommender algorithms within detected user communities Masters Thesis
Technische Universität Wien, Vienna, Austria, 2024, (Advisors: Julia Neidhardt, Thomas Elmar Kolb).
@mastersthesis{steindl2024fairness,
title = {Evaluating the fairness of news recommender algorithms within detected user communities},
author = {Bernhard Steindl},
url = {https://doi.org/10.34726/hss.2024.115142},
doi = {10.34726/hss.2024.115142},
year = {2024},
date = {2024-01-01},
pages = {116},
address = {Vienna, Austria},
school = {Technische Universität Wien},
abstract = {This work addresses the problem of unfair treatment of different user groups in the recommendations they receive from recommendation algorithms. Recommender systems (RS) are algorithms that suggest items to a user that are most likely to be of interest, based on the user's interaction history with items. Recommendation algorithms are used in a variety of domains, such as recommending music on music platforms or news in online portals.},
note = {Advisors: Julia Neidhardt, Thomas Elmar Kolb},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
This work addresses the problem of unfair treatment of different user groups in the recommendations they receive from recommendation algorithms. Recommender systems (RS) are algorithms that suggest items to a user that are most likely to be of interest, based on the user’s interaction history with items. Recommendation algorithms are used in a variety of domains, such as recommending music on music platforms or news in online portals.
15.
Kolb, Thomas Elmar; Nalis-Neuner, Irina; Neidhardt, Julia
Like a Skilled DJ – an Expert Study on News Recommendations Beyond Accuracy Proceedings Article
In: Kille, Benjamin (Ed.): CEUR-WS.org, 2023.
@inproceedings{ 20.500.12708_191170,
title = {Like a Skilled DJ - an Expert Study on News Recommendations Beyond Accuracy},
author = {Thomas Elmar Kolb and Irina Nalis-Neuner and Julia Neidhardt},
editor = {Benjamin Kille},
doi = {10.34726/5332},
year = {2023},
date = {2023-01-01},
volume = {3561},
publisher = {CEUR-WS.org},
series = {CEUR Workshop Proceedings},
abstract = {In the past, recommender systems were primarily focused on optimizing accuracy. However, in recent years, there has been an increasing awareness that considerations beyond accuracy are necessary. The definition of what constitutes a good recommendation is a crucial issue. The most precise prediction may not always be the recommendation that satisfies the user best. This study offers a comprehensive investigation into the present advancements within the realm of beyond-accuracy measurements, especially the metrics diversity, serendipity, and novelty. Collaborative efforts between algorithmic models and domain experts can enrich recommendation quality, particularly in labeling and categorizing content. To address this, we present a study conducted by experts in the news domain. This study provides new insights into the multifaceted nature of this challenge. Employing an interdisciplinary approach, we underscore the significance of constructing a system that revolves around the user. Recent discussions about algorithmic content filtering and its societal implications underscore the importance of maintaining human involvement in the decision-making loop.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In the past, recommender systems were primarily focused on optimizing accuracy. However, in recent years, there has been an increasing awareness that considerations beyond accuracy are necessary. The definition of what constitutes a good recommendation is a crucial issue. The most precise prediction may not always be the recommendation that satisfies the user best. This study offers a comprehensive investigation into the present advancements within the realm of beyond-accuracy measurements, especially the metrics diversity, serendipity, and novelty. Collaborative efforts between algorithmic models and domain experts can enrich recommendation quality, particularly in labeling and categorizing content. To address this, we present a study conducted by experts in the news domain. This study provides new insights into the multifaceted nature of this challenge. Employing an interdisciplinary approach, we underscore the significance of constructing a system that revolves around the user. Recent discussions about algorithmic content filtering and its societal implications underscore the importance of maintaining human involvement in the decision-making loop.
16.
Kolb, Thomas Elmar; Wagne, Ahmadou; Sertkan, Mete; Neidhardt, Julia
Potentials of Combining Local Knowledge and LLMs for Recommender Systems Proceedings Article
In: Anelli, Vito Walter; Basile, Pierpaolo; Melo, Gerard De; Donini, Francesco; Ferrara, Antonio; Musto, Cataldo; Narducci, Fedelucio; Ragone, Azzurra; Zanker, Markus (Ed.): pp. 61–64, CEUR-WS.org, 2023.
@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}
}
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.
17.
18.
Sertkan, Mete; Althammer, Sophia; Hofstätter, Sebastian; Knees, Peter; Neidhardt, Julia
Exploring Effect-Size-Based Meta-Analysis for Multi-Dataset Evaluation Proceedings Article
In: CEUR-WS.org, 2023.
@inproceedings{ 20.500.12708_191697,
title = {Exploring Effect-Size-Based Meta-Analysis for Multi-Dataset Evaluation},
author = {Mete Sertkan and Sophia Althammer and Sebastian Hofstätter and Peter Knees and Julia Neidhardt},
doi = {10.34726/5352},
year = {2023},
date = {2023-01-01},
volume = {3476},
publisher = {CEUR-WS.org},
series = {CEUR Workshop Proceedings},
abstract = {In this paper, we address the essential yet complex task of evaluating Recommender Systems (RecSys) across multiple datasets. This is critical for gauging their overall performance and applicability in various contexts. Owing to the unique characteristics of each dataset and the variability in algorithm performance, we propose the adoption of effect-size-based meta-analysis, a proven tool in comparative research. This approach enables us to compare a “treatment model” and a “control model” across multiple datasets, offering a comprehensive evaluation of their performance. Through two case studies, we highlight the flexibility and effectiveness of this method in multi-dataset evaluations, irrespective of the metric utilized. The power of forest plots in providing an intuitive and concise summarization of our analysis is also demonstrated, which significantly aids in the communication of research findings. Our work provides valuable insights into leveraging these methodologies to draw more reliable and validated conclusions on the generalizability and robustness of RecSys models.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we address the essential yet complex task of evaluating Recommender Systems (RecSys) across multiple datasets. This is critical for gauging their overall performance and applicability in various contexts. Owing to the unique characteristics of each dataset and the variability in algorithm performance, we propose the adoption of effect-size-based meta-analysis, a proven tool in comparative research. This approach enables us to compare a “treatment model” and a “control model” across multiple datasets, offering a comprehensive evaluation of their performance. Through two case studies, we highlight the flexibility and effectiveness of this method in multi-dataset evaluations, irrespective of the metric utilized. The power of forest plots in providing an intuitive and concise summarization of our analysis is also demonstrated, which significantly aids in the communication of research findings. Our work provides valuable insights into leveraging these methodologies to draw more reliable and validated conclusions on the generalizability and robustness of RecSys models.
19.
Basso, Linda; Nalis-Neuner, Irina; Neidhardt, Julia
News Diversity and Well-Being – An Experimental Exploration Of Diversity-Aware Recommender Systems Proceedings Article
In: 2023.
@inproceedings{ 20.500.12708_193212,
title = {News Diversity and Well-Being – An Experimental Exploration Of Diversity-Aware Recommender Systems},
author = {Linda Basso and Irina Nalis-Neuner and Julia Neidhardt},
year = {2023},
date = {2023-01-01},
abstract = {The demand for socially responsible designs for news recommender systems is currently of the utmost relevance. This paper presents a novel and interdisciplinary approach, bringing together psychol- ogists and computer scientists, to examine the impact of diverse news recommendations on individual users. In this experimental study, participants were divided into two groups, interacting with either a diverse news recommender system (experimental group) or a non-diversified system (control group). Subjective well-being and personal evaluations of the recommender system were measured. Although the study did not find a significant positive impact on participants’ subjective well-being after consuming more diverse news, this preliminary investigation opens avenues for further re- search. This study sets the stage for future investigations, providing valuable insights and highlighting the complexities of promoting diverse news consumption through recommender systems. Further research is warranted to explore potential enhancements and refine the understanding of the relationship between diversified news recommendations and user well-being. This contribution lays a groundstone for further research on responsibilities and how to implement basic human values, which are important to sustain and advance the democratic society we live in.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The demand for socially responsible designs for news recommender systems is currently of the utmost relevance. This paper presents a novel and interdisciplinary approach, bringing together psychol- ogists and computer scientists, to examine the impact of diverse news recommendations on individual users. In this experimental study, participants were divided into two groups, interacting with either a diverse news recommender system (experimental group) or a non-diversified system (control group). Subjective well-being and personal evaluations of the recommender system were measured. Although the study did not find a significant positive impact on participants’ subjective well-being after consuming more diverse news, this preliminary investigation opens avenues for further re- search. This study sets the stage for future investigations, providing valuable insights and highlighting the complexities of promoting diverse news consumption through recommender systems. Further research is warranted to explore potential enhancements and refine the understanding of the relationship between diversified news recommendations and user well-being. This contribution lays a groundstone for further research on responsibilities and how to implement basic human values, which are important to sustain and advance the democratic society we live in.
20.
Neidhardt, Julia; Wörndl, Wolfgang; Kuflik, Tsvi; Goldenberg, Dmitri; Zanker, Markus
Workshop on Recommenders in Tourism (RecTour) 2023 Proceedings Article
In: Zhang, Jie; Chen, Li; Berkovsky, Shlomo (Ed.): pp. 1274–1275, Association for Computing Machinery, New York, 2023.
@inproceedings{ 20.500.12708_191202,
title = {Workshop on Recommenders in Tourism (RecTour) 2023},
author = {Julia Neidhardt and Wolfgang Wörndl and Tsvi Kuflik and Dmitri Goldenberg and Markus Zanker},
editor = {Jie Zhang and Li Chen and Shlomo Berkovsky},
doi = {10.1145/3604915.3608764},
year = {2023},
date = {2023-01-01},
pages = {1274–1275},
publisher = {Association for Computing Machinery},
address = {New York},
abstract = {The Workshop on Recommenders in Tourism (RecTour) 2023, which is held in conjunction with the 17th issue of the ACM Conference on Recommender Systems (RecSys) in Singapore, addresses specific challenges for recommender systems in the tourism domain. In this overview paper, we summarize our motivations to organize the RecTour workshop and present the main topic areas of RecTour submissions. These include context-aware recommendations, group recommender systems, recommending composite items, decision making and user interaction issues, different information sources and various application scenarios.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The Workshop on Recommenders in Tourism (RecTour) 2023, which is held in conjunction with the 17th issue of the ACM Conference on Recommender Systems (RecSys) in Singapore, addresses specific challenges for recommender systems in the tourism domain. In this overview paper, we summarize our motivations to organize the RecTour workshop and present the main topic areas of RecTour submissions. These include context-aware recommendations, group recommender systems, recommending composite items, decision making and user interaction issues, different information sources and various application scenarios.