Team
Julia Neidhardt
Researcher & Lab Director
Julia Neidhardt is a researcher at the Research Unit E-Commerce at TU Wien informatics with a background in mathematics and computer science. Previously she was a guest researcher at the Austrian Academy of Sciences as well as visiting scholar at Northwestern University, USA, and at the University of Geneva, Switzerland. Her research focuses on user modeling and recommender systems in tourism and in the news domain, developing approaches to capture online opinion-forming and online behavior, and digital humanities. Her research is published in highly renowned conference proceedings and journals including Nature Human Behaviour. She regularly is invited to give talks on topics related to her research, among others at the Oxford Women in Computer Science – Distinguished Speaker Series at the University of Oxford. Julia Neidhardt is a senior program committee member of the ACM Conference on Recommender Systems (RecSys) and is an associate editor of the Journal of Information Technology &Tourism as well as a distinguished reviewer of the newly established journal ACM Transactions on Recommender Systems (TORS). She was research track co-chair of ENTER 2020 and ENTER 2019 and a co-organizer of a number of workshops and conferences. Julia Neidhardt is part of the Digital Humanism Initiative at TU Wien and board member of Center for Artificial Intelligence and Machine Learning (CAIML). She will lead the CD Lab for Recommender Systems, applying her expertise in these various research areas focusing on recommender systems.
Publications
2024
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},
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.},
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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.
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.
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.},
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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.
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.},
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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.
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.},
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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.
2023
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.},
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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.
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.},
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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.
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.},
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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.
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.},
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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.
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.},
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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.
Nalis, Irina; Neidhardt, Julia
Not Facial Expression, nor Fingerprint – Acknowledging Complexity and Context in Emotion Research for Human-Centered Personalization and Adaptation Proceedings Article
In: Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization, pp. 325–330, Association for Computing Machinery, New York, 2023.
@inproceedings{ 20.500.12708_192197,
title = {Not Facial Expression, nor Fingerprint – Acknowledging Complexity and Context in Emotion Research for Human-Centered Personalization and Adaptation},
author = {Irina Nalis and Julia Neidhardt},
doi = {10.1145/3563359.3596990},
year = {2023},
date = {2023-01-01},
booktitle = {Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization},
pages = {325–330},
publisher = {Association for Computing Machinery},
address = {New York},
abstract = {While research on emotion has emerged as a crucial area in studying this relationship, the use of classical psychological concepts in human emotion detection and sentiment analysis has been challenged by the cognitive sciences and psychology. This paper argues that the uncritical adoption of concepts that overlook the complexity and context of emotions may hinder progress in this field. To overcome this limitation, the theory of constructed emotion is reviewed, which suggests that emotions are not distinct categories but rather dimensions that require dynamic, rather than static, contextualized models. By prioritizing digital wellbeing in emotion studies and acknowledging complexity and context, future research can develop more effective models for emotion detection and sentiment analysis. The aim is to provide valuable insights for researchers seeking to advance our understanding of the relationship between technology and wellbeing for human centered-adaptation and personalization.},
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While research on emotion has emerged as a crucial area in studying this relationship, the use of classical psychological concepts in human emotion detection and sentiment analysis has been challenged by the cognitive sciences and psychology. This paper argues that the uncritical adoption of concepts that overlook the complexity and context of emotions may hinder progress in this field. To overcome this limitation, the theory of constructed emotion is reviewed, which suggests that emotions are not distinct categories but rather dimensions that require dynamic, rather than static, contextualized models. By prioritizing digital wellbeing in emotion studies and acknowledging complexity and context, future research can develop more effective models for emotion detection and sentiment analysis. The aim is to provide valuable insights for researchers seeking to advance our understanding of the relationship between technology and wellbeing for human centered-adaptation and personalization.
Knees, Peter; Neidhardt, Julia; Nalis-Neuner, Irina
Recommender Systems: Techniques, Effects, and Measures Toward Pluralism and Fairness Book Section
In: Werthner, Hannes; Ghezzi, Carlo; Kramer, Jeff (Ed.): pp. 417–434, Springer, Cham, 2023.
@incollection{ 20.500.12708_191188,
title = {Recommender Systems: Techniques, Effects, and Measures Toward Pluralism and Fairness},
author = {Peter Knees and Julia Neidhardt and Irina Nalis-Neuner},
editor = {Hannes Werthner and Carlo Ghezzi and Jeff Kramer},
doi = {10.1007/978-3-031-45304-5_27},
year = {2023},
date = {2023-01-01},
pages = {417–434},
publisher = {Springer},
address = {Cham},
abstract = {Recommender systems are widely used in various applications, such as online shopping, social media, and news personalization. They can help systems by delivering only the most relevant and promising information to their users and help people by mitigating information overload. At the same time, algorithmic recommender systems are a new form of gatekeeper that preselects and controls the information being presented and actively shapes users’ choices and behavior. This becomes a crucial aspect, as, if unaddressed and not safeguarded, these systems are susceptible to perpetuate and even amplify existing biases, including unwanted societal biases, leading to unfair and discriminatory outcomes. In this chapter, we briefly introduce recommender systems, their basic mechanisms, and their importance in various applications. We show how their outcomes and performance are assessed and discuss approaches to addressing pluralism and fairness in recommender systems. Finally, we highlight recently emerging directions within recommender systems research, pointing out opportunities for digital humanism to contribute interdisciplinary expertise.},
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Recommender systems are widely used in various applications, such as online shopping, social media, and news personalization. They can help systems by delivering only the most relevant and promising information to their users and help people by mitigating information overload. At the same time, algorithmic recommender systems are a new form of gatekeeper that preselects and controls the information being presented and actively shapes users’ choices and behavior. This becomes a crucial aspect, as, if unaddressed and not safeguarded, these systems are susceptible to perpetuate and even amplify existing biases, including unwanted societal biases, leading to unfair and discriminatory outcomes. In this chapter, we briefly introduce recommender systems, their basic mechanisms, and their importance in various applications. We show how their outcomes and performance are assessed and discuss approaches to addressing pluralism and fairness in recommender systems. Finally, we highlight recently emerging directions within recommender systems research, pointing out opportunities for digital humanism to contribute interdisciplinary expertise.
Grossmann, Wilfried; Sertkan, Mete; Neidhardt, Julia; Werthner, Hannes
Pictures as a tool for matching tourist preferences with destinations Book Section
In: Augstein, Mirjam; Herder, Eelco; Wörndl, Wolfgang (Ed.): pp. 337–354, De Gruyter Oldenbourg, Berlin ; Boston, 2023.
@incollection{ 20.500.12708_191182,
title = {Pictures as a tool for matching tourist preferences with destinations},
author = {Wilfried Grossmann and Mete Sertkan and Julia Neidhardt and Hannes Werthner},
editor = {Mirjam Augstein and Eelco Herder and Wolfgang Wörndl},
doi = {10.1515/9783110988567-013},
year = {2023},
date = {2023-01-01},
pages = {337–354},
publisher = {De Gruyter Oldenbourg},
address = {Berlin ; Boston},
series = {De Gruyter Textbook},
abstract = {Usually descriptions of touristic products comprise information about accommodation, tourist attractions or leisure activities. Tourist decisions for a product are based on personal characteristics, planned vacation activities and specificities of potential touristic products. The decision should guarantee a high level of emotional and physical well-being, considering also some hard constraints like temporal and monetary resources, or travel distance. The starting point for the design of the described recommender system is a unified description of the preferences of the tourist and the opportunities offered by touristic products using the so-called seven-factor model. For the assignment of the values in the seven-factor model a predefined set of pictures is the pivotal instrument. These pictures represent various aspects of the personality and preferences of the tourist as well as general categories for the description of destinations, i. e., certain tourist attractions like landscape, cultural facilities, different leisure activities or emotional aspects associated with tourism. Based on the picture selection of a customer a so-called factor algorithm calculates values for each factor of the seven-factor model. This is a rather fast and intuitive method for acquisition of information about personality and preferences. The evaluation of the factors of the products is obtained by mapping descriptive attributes of touristic products onto the predefined pictures and afterwards applying the factor algorithm to the pictures characterizing the product. Based on this unified description of tourists and touristic products a recommendation can be defined by measuring the similarity between the user attributes and the product attributes. The approach is evaluated using data from a travel agency. Furthermore, other possible applications are discussed.},
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Usually descriptions of touristic products comprise information about accommodation, tourist attractions or leisure activities. Tourist decisions for a product are based on personal characteristics, planned vacation activities and specificities of potential touristic products. The decision should guarantee a high level of emotional and physical well-being, considering also some hard constraints like temporal and monetary resources, or travel distance. The starting point for the design of the described recommender system is a unified description of the preferences of the tourist and the opportunities offered by touristic products using the so-called seven-factor model. For the assignment of the values in the seven-factor model a predefined set of pictures is the pivotal instrument. These pictures represent various aspects of the personality and preferences of the tourist as well as general categories for the description of destinations, i. e., certain tourist attractions like landscape, cultural facilities, different leisure activities or emotional aspects associated with tourism. Based on the picture selection of a customer a so-called factor algorithm calculates values for each factor of the seven-factor model. This is a rather fast and intuitive method for acquisition of information about personality and preferences. The evaluation of the factors of the products is obtained by mapping descriptive attributes of touristic products onto the predefined pictures and afterwards applying the factor algorithm to the pictures characterizing the product. Based on this unified description of tourists and touristic products a recommendation can be defined by measuring the similarity between the user attributes and the product attributes. The approach is evaluated using data from a travel agency. Furthermore, other possible applications are discussed.
Baumann, Andreas; Hofmann, Klaus; Marakasova, Anna; Neidhardt, Julia; Wissik, Tanja
Semantic micro-dynamics as a reflex of occurrence frequency: a semantic networks approach Journal Article
In: Cognitive Linguistics, vol. 34, iss. 3-4, pp. 533–568, 2023, ISSN: 0936-5907.
@article{ 20.500.12708_191531,
title = {Semantic micro-dynamics as a reflex of occurrence frequency: a semantic networks approach},
author = {Andreas Baumann and Klaus Hofmann and Anna Marakasova and Julia Neidhardt and Tanja Wissik},
url = {https://api.elsevier.com/content/abstract/scopus_id/85175416448},
doi = {10.1515/cog-2022-0008},
issn = {0936-5907},
year = {2023},
date = {2023-01-01},
journal = {Cognitive Linguistics},
volume = {34},
issue = {3-4},
pages = {533–568},
publisher = {De Gruyter},
abstract = {This article correlates fine-grained semantic variability and change with measures of occurrence frequency to investigate whether a word's degree of semantic change is sensitive to how often it is used. We show that this sensitivity can be detected within a short time span (i.e., 20 years), basing our analysis on a large corpus of German allowing for a high temporal resolution (i.e., per month). We measure semantic variability and change with the help of local semantic networks, combining elements of deep learning methodology and graph theory. Our micro-scale analysis complements previous macro-scale studies from the field of natural language processing, corroborating the finding that high token frequency has a negative effect on the degree of semantic change in a lexical item. We relate this relationship to the role of exemplars for establishing form-function pairings between words and their habitual usage contexts.},
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This article correlates fine-grained semantic variability and change with measures of occurrence frequency to investigate whether a word’s degree of semantic change is sensitive to how often it is used. We show that this sensitivity can be detected within a short time span (i.e., 20 years), basing our analysis on a large corpus of German allowing for a high temporal resolution (i.e., per month). We measure semantic variability and change with the help of local semantic networks, combining elements of deep learning methodology and graph theory. Our micro-scale analysis complements previous macro-scale studies from the field of natural language processing, corroborating the finding that high token frequency has a negative effect on the degree of semantic change in a lexical item. We relate this relationship to the role of exemplars for establishing form-function pairings between words and their habitual usage contexts.
Pachinger, Pia; Hanbury, Allan; Neidhardt, Julia; Planitzer, Anna Maria
Toward Disambiguating the Definitions of Abusive, Offensive, Toxic, and Uncivil Comments Proceedings Article
In: pp. 107–113, 2023.
@inproceedings{ 20.500.12708_191532,
title = {Toward Disambiguating the Definitions of Abusive, Offensive, Toxic, and Uncivil Comments},
author = {Pia Pachinger and Allan Hanbury and Julia Neidhardt and Anna Maria Planitzer},
doi = {10.18653/v1/2023.c3nlp-1.11},
year = {2023},
date = {2023-01-01},
pages = {107–113},
abstract = {The definitions of abusive, offensive, toxic and uncivil comments used for annotating corpora for automated content moderation are highly intersected and researchers call for their disambiguation. We summarize the definitions of these terms as they appear in 23 papers across different fields. We compare examples given for uncivil, offensive, and toxic comments, attempting to foster more unified scientific resources. Additionally, we stress that the term incivility that frequently appears in social science literature has hardly been mentioned in the literature we analyzed that focuses on computational linguistics and natural language processing.},
keywords = {},
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The definitions of abusive, offensive, toxic and uncivil comments used for annotating corpora for automated content moderation are highly intersected and researchers call for their disambiguation. We summarize the definitions of these terms as they appear in 23 papers across different fields. We compare examples given for uncivil, offensive, and toxic comments, attempting to foster more unified scientific resources. Additionally, we stress that the term incivility that frequently appears in social science literature has hardly been mentioned in the literature we analyzed that focuses on computational linguistics and natural language processing.
2022
Hussak, Melanie; Neidhardt, Julia; Stilz, Melanie
Bildung für den Frieden in einer digitalisierten Welt Miscellaneous
2022.
@misc{ 20.500.12708_153931,
title = {Bildung für den Frieden in einer digitalisierten Welt},
author = {Melanie Hussak and Julia Neidhardt and Melanie Stilz},
year = {2022},
date = {2022-01-01},
abstract = {In Artikel 4 der Erklärung der UN-Generalversammlung über eine Kultur des Friedens steht:
Bildung auf allen Ebenen ist eines der wichtigsten Instrumente zum Aufbau einer Kultur des Friedens. Dabei kommt der Menschenrechtserziehung eine besondere Bedeutung zu.
Frieden ist ein nie abgeschlossenes Projekt, das auf die stetige Abnahme von Gewalt und die gleichzeitige Zunahme von Gerechtigkeit zielt. Die Friedenspädagogik als inter- und transdisziplinäre Wissenschaft besitzt den weiten Blick, das Potential der Informatik für den “Ewigen Frieden” (Kant) in einer Paneldiskussion anschaulich und konkret darzustellen.},
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In Artikel 4 der Erklärung der UN-Generalversammlung über eine Kultur des Friedens steht:
Bildung auf allen Ebenen ist eines der wichtigsten Instrumente zum Aufbau einer Kultur des Friedens. Dabei kommt der Menschenrechtserziehung eine besondere Bedeutung zu.
Frieden ist ein nie abgeschlossenes Projekt, das auf die stetige Abnahme von Gewalt und die gleichzeitige Zunahme von Gerechtigkeit zielt. Die Friedenspädagogik als inter- und transdisziplinäre Wissenschaft besitzt den weiten Blick, das Potential der Informatik für den “Ewigen Frieden” (Kant) in einer Paneldiskussion anschaulich und konkret darzustellen.
Bildung auf allen Ebenen ist eines der wichtigsten Instrumente zum Aufbau einer Kultur des Friedens. Dabei kommt der Menschenrechtserziehung eine besondere Bedeutung zu.
Frieden ist ein nie abgeschlossenes Projekt, das auf die stetige Abnahme von Gewalt und die gleichzeitige Zunahme von Gerechtigkeit zielt. Die Friedenspädagogik als inter- und transdisziplinäre Wissenschaft besitzt den weiten Blick, das Potential der Informatik für den “Ewigen Frieden” (Kant) in einer Paneldiskussion anschaulich und konkret darzustellen.
Sertkan, Mete; Althammer, Sophia; Hofstätter, Sebastian; Neidhardt, Julia
Diversifying Sentiments in News Recommendation Proceedings Article
In: 2022.
@inproceedings{ 20.500.12708_175977,
title = {Diversifying Sentiments in News Recommendation},
author = {Mete Sertkan and Sophia Althammer and Sebastian Hofstätter and Julia Neidhardt},
doi = {10.34726/3903},
year = {2022},
date = {2022-01-01},
volume = {3228},
series = {CEUR Workshop Proceedings},
abstract = {Personalized news recommender systems are widely deployed to filter the information overload caused by the sheer amount of news produced daily. Recommended news articles usually have a sentiment similar to the sentiment orientation of the previously consumed news, creating a self-reinforcing cycle of sentiment chambers around people. Wu et al. introduced SentiRec – a sentiment diversity-aware neural news recommendation model to counter this lack of diversity.
In this work, we reproduce SentiRec without access to the original source code and data sample. We re-implement SentiRec from scratch and use the Microsoft MIND dataset (same source but different subset as in the original work) for our experiments. We evaluate and discuss our reproduction from different perspectives. While the original paper mainly has a user-centric perspective on sentiment diversity by comparing the recommendation list to the user’s interaction history, we also analyze the intra-list sentiment diversity of the recommendation list. Additionally, we study the effect of sentiment diversification on topical diversity. Our results suggest that SentiRec does not generalize well to other data since the compared baselines already perform well, opposing the original work’s findings. While the original SentiRec utilizes a rule-based sentiment analyzer, we also study a pre-trained neural sentiment analyzer. However, we observe no improvements in effectiveness nor in sentiment diversity. To foster reproducibility, we make our source code publicly available.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Personalized news recommender systems are widely deployed to filter the information overload caused by the sheer amount of news produced daily. Recommended news articles usually have a sentiment similar to the sentiment orientation of the previously consumed news, creating a self-reinforcing cycle of sentiment chambers around people. Wu et al. introduced SentiRec – a sentiment diversity-aware neural news recommendation model to counter this lack of diversity.
In this work, we reproduce SentiRec without access to the original source code and data sample. We re-implement SentiRec from scratch and use the Microsoft MIND dataset (same source but different subset as in the original work) for our experiments. We evaluate and discuss our reproduction from different perspectives. While the original paper mainly has a user-centric perspective on sentiment diversity by comparing the recommendation list to the user’s interaction history, we also analyze the intra-list sentiment diversity of the recommendation list. Additionally, we study the effect of sentiment diversification on topical diversity. Our results suggest that SentiRec does not generalize well to other data since the compared baselines already perform well, opposing the original work’s findings. While the original SentiRec utilizes a rule-based sentiment analyzer, we also study a pre-trained neural sentiment analyzer. However, we observe no improvements in effectiveness nor in sentiment diversity. To foster reproducibility, we make our source code publicly available.
In this work, we reproduce SentiRec without access to the original source code and data sample. We re-implement SentiRec from scratch and use the Microsoft MIND dataset (same source but different subset as in the original work) for our experiments. We evaluate and discuss our reproduction from different perspectives. While the original paper mainly has a user-centric perspective on sentiment diversity by comparing the recommendation list to the user’s interaction history, we also analyze the intra-list sentiment diversity of the recommendation list. Additionally, we study the effect of sentiment diversification on topical diversity. Our results suggest that SentiRec does not generalize well to other data since the compared baselines already perform well, opposing the original work’s findings. While the original SentiRec utilizes a rule-based sentiment analyzer, we also study a pre-trained neural sentiment analyzer. However, we observe no improvements in effectiveness nor in sentiment diversity. To foster reproducibility, we make our source code publicly available.
Kolb, Thomas Elmar; Nalis, Irina; Sertkan, Mete; Neidhardt, Julia
The Role of Bias in News Recommendation in the Perception of the Covid-19 Pandemic Proceedings Article
In: Thomas, Kolb (Ed.): 2022.
@inproceedings{ 20.500.12708_150340,
title = {The Role of Bias in News Recommendation in the Perception of the Covid-19 Pandemic},
author = {Thomas Elmar Kolb and Irina Nalis and Mete Sertkan and Julia Neidhardt},
editor = {Kolb Thomas},
doi = {10.48550/ARXIV.2209.07608},
year = {2022},
date = {2022-01-01},
abstract = {News recommender systems (NRs) have been shown to shape public discourse and to enforce behaviors that have a critical, oftentimes detrimental effect on democracies. Earlier research on the impact of media bias has revealed their strong impact on opinions and preferences. Responsible NRs are supposed to have depolarizing capacities, once they go beyond accuracy measures. We performed sequence prediction by using the BERT4Rec algorithm to investigate the interplay of news of coverage and user behavior. Based on live data and training of a large data set from one news outlet "event bursts", "rally around the flag" effect and "filter bubbles" were investigated in our interdisciplinary approach between data science and psychology. Potentials for fair NRs that go beyond accuracy measures are outlined via training of the models with a large data set of articles, keywords, and user behavior. The development of the news coverage and user behavior of the COVID-19 pandemic from primarily medical to broader political content and debates was traced. Our study provides first insights for future development of responsible news recommendation that acknowledges user preferences while stimulating diversity and accountability instead of accuracy, only.},
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}
News recommender systems (NRs) have been shown to shape public discourse and to enforce behaviors that have a critical, oftentimes detrimental effect on democracies. Earlier research on the impact of media bias has revealed their strong impact on opinions and preferences. Responsible NRs are supposed to have depolarizing capacities, once they go beyond accuracy measures. We performed sequence prediction by using the BERT4Rec algorithm to investigate the interplay of news of coverage and user behavior. Based on live data and training of a large data set from one news outlet “event bursts”, “rally around the flag” effect and “filter bubbles” were investigated in our interdisciplinary approach between data science and psychology. Potentials for fair NRs that go beyond accuracy measures are outlined via training of the models with a large data set of articles, keywords, and user behavior. The development of the news coverage and user behavior of the COVID-19 pandemic from primarily medical to broader political content and debates was traced. Our study provides first insights for future development of responsible news recommendation that acknowledges user preferences while stimulating diversity and accountability instead of accuracy, only.
Neidhardt, Julia; Wörndl, Wolfgang; Kuflik, Tsvi; Goldenberg, Dmitri; Zanker, Markus
Workshop on Recommenders in Tourism (RecTour) Proceedings Article
In: Golbeck, Jennifer; Harper, F. Maxwell; Murdock, Vanessa (Ed.): pp. 678–679, Association for Computing Machinery, New York, 2022.
@inproceedings{ 20.500.12708_191201,
title = {Workshop on Recommenders in Tourism (RecTour)},
author = {Julia Neidhardt and Wolfgang Wörndl and Tsvi Kuflik and Dmitri Goldenberg and Markus Zanker},
editor = {Jennifer Golbeck and F. Maxwell Harper and Vanessa Murdock},
doi = {10.1145/3523227.3547416},
year = {2022},
date = {2022-01-01},
pages = {678–679},
publisher = {Association for Computing Machinery},
address = {New York},
abstract = {The Workshop on Recommenders in Tourism (RecTour) 2022, which is held in conjunction with the 16th ACM Conference on Recommender Systems (RecSys), 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},
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The Workshop on Recommenders in Tourism (RecTour) 2022, which is held in conjunction with the 16th ACM Conference on Recommender Systems (RecSys), 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.
Sertkan, Mete; Neidhardt, Julia
Exploring Expressed Emotions for Neural News Recommendation Proceedings Article
In: pp. 22–28, Association for Computing Machinery, New York, NY, United States, 2022.
@inproceedings{ 20.500.12708_153156,
title = {Exploring Expressed Emotions for Neural News Recommendation},
author = {Mete Sertkan and Julia Neidhardt},
doi = {10.1145/3511047.3536414},
year = {2022},
date = {2022-01-01},
pages = {22–28},
publisher = {Association for Computing Machinery},
address = {New York, NY, United States},
abstract = {Due to domain-specific challenges such as short item lifetimes and continuous cold-start issues, news recommender systems rely more on content-based methods to deduce reliable user models and make personalized recommendations. Research has shown that alongside the content of an item, the way it is presented to the users also plays a critical role. In this work, we focus on the effect of incorporating expressed emotions within news articles on recommendation performance. We propose a neural news recommendation model that disentangles semantic and emotional modeling of news articles and users. While we exploit the textual content for the semantic representation, we extract and combine emotions of different information levels for the emotional representation. Offline experiments on a real-world dataset show that our approach outperforms non-emotion-aware solutions significantly. Finally, we provide a future outline, where we plan to investigate a) the online performance and b) the explainability/explorability of our approach.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Due to domain-specific challenges such as short item lifetimes and continuous cold-start issues, news recommender systems rely more on content-based methods to deduce reliable user models and make personalized recommendations. Research has shown that alongside the content of an item, the way it is presented to the users also plays a critical role. In this work, we focus on the effect of incorporating expressed emotions within news articles on recommendation performance. We propose a neural news recommendation model that disentangles semantic and emotional modeling of news articles and users. While we exploit the textual content for the semantic representation, we extract and combine emotions of different information levels for the emotional representation. Offline experiments on a real-world dataset show that our approach outperforms non-emotion-aware solutions significantly. Finally, we provide a future outline, where we plan to investigate a) the online performance and b) the explainability/explorability of our approach.
Neidhardt, Julia; Sertkan, Mete
Towards an Approach for Analyzing Dynamic Aspects of Bias and Beyond-Accuracy Measures Proceedings Article
In: Boratto, Ludovico; Faralli, Stefano; Marras, Mirko; stilo, (Ed.): pp. 35–42, Springer Cham, 2022.
@inproceedings{ 20.500.12708_153166,
title = {Towards an Approach for Analyzing Dynamic Aspects of Bias and Beyond-Accuracy Measures},
author = {Julia Neidhardt and Mete Sertkan},
editor = {Ludovico Boratto and Stefano Faralli and Mirko Marras and stilo},
doi = {10.1007/978-3-031-09316-6_4},
year = {2022},
date = {2022-01-01},
volume = {1610},
pages = {35–42},
publisher = {Springer Cham},
abstract = {The quality of recommender systems has traditionally only been assessed using accuracy measures. Research has shown that accuracy is only one side of the medallion and that we should also consider quality features that go beyond accuracy. Recently, also fairness-related aspects and bias have increasingly been considered as outcome dimensions in this context. While beyond-accuracy measures including diversity, novelty and serendipity and bias in recommendation have been subject to the research discourse, their interrelation and temporal and group dynamics are clearly under-explored. In this position paper, we propose an approach that groups users based on their behaviors and preferences and that addresses beyond-accuracy needs of those groups while controlling for bias. Further, we consider the analysis of long-term dynamics of different interrelated beyond-accuracy measures and bias as crucial research direction since it helps to advance the field and to address societal issues related to recommender systems and personalization.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The quality of recommender systems has traditionally only been assessed using accuracy measures. Research has shown that accuracy is only one side of the medallion and that we should also consider quality features that go beyond accuracy. Recently, also fairness-related aspects and bias have increasingly been considered as outcome dimensions in this context. While beyond-accuracy measures including diversity, novelty and serendipity and bias in recommendation have been subject to the research discourse, their interrelation and temporal and group dynamics are clearly under-explored. In this position paper, we propose an approach that groups users based on their behaviors and preferences and that addresses beyond-accuracy needs of those groups while controlling for bias. Further, we consider the analysis of long-term dynamics of different interrelated beyond-accuracy measures and bias as crucial research direction since it helps to advance the field and to address societal issues related to recommender systems and personalization.
Dietz, Linus W.; Sertkan, Mete; Myftija, Saadi; Palage, Sameera Thimbiri; Neidhardt, Julia; Wörndl, Wolfgang
A Comparative Study of Data-Driven Models for Travel Destination Characterization Journal Article
In: Frontiers in Big Data, vol. 5, 2022.
@article{ 20.500.12708_139725,
title = {A Comparative Study of Data-Driven Models for Travel Destination Characterization},
author = {Linus W. Dietz and Mete Sertkan and Saadi Myftija and Sameera Thimbiri Palage and Julia Neidhardt and Wolfgang Wörndl},
url = {https://api.elsevier.com/content/abstract/scopus_id/85128638005},
doi = {10.3389/fdata.2022.829939},
year = {2022},
date = {2022-01-01},
journal = {Frontiers in Big Data},
volume = {5},
publisher = {Frontiers Media},
abstract = {Characterizing items for content-based recommender systems is a challenging task in complex domains such as travel and tourism. In the case of destination recommendation, no feature set can be readily used as a similarity ground truth, which makes it hard to evaluate the quality of destination characterization approaches. Furthermore, the process should scale well for many items, be cost-efficient, and most importantly correct. To evaluate which data sources are most suitable, we investigate 18 characterization methods that fall into three categories: venue data, textual data, and factual data. We make these data models comparable using rank agreement metrics and reveal which data sources capture similar underlying concepts. To support choosing more suitable data models, we capture a desired concept using an expert survey and evaluate our characterization methods toward it. We find that the textual models to characterize cities perform best overall, with data models based on factual and venue data being less competitive. However, we show that data models with explicit features can be optimized by learning weights for their features.},
keywords = {},
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Characterizing items for content-based recommender systems is a challenging task in complex domains such as travel and tourism. In the case of destination recommendation, no feature set can be readily used as a similarity ground truth, which makes it hard to evaluate the quality of destination characterization approaches. Furthermore, the process should scale well for many items, be cost-efficient, and most importantly correct. To evaluate which data sources are most suitable, we investigate 18 characterization methods that fall into three categories: venue data, textual data, and factual data. We make these data models comparable using rank agreement metrics and reveal which data sources capture similar underlying concepts. To support choosing more suitable data models, we capture a desired concept using an expert survey and evaluate our characterization methods toward it. We find that the textual models to characterize cities perform best overall, with data models based on factual and venue data being less competitive. However, we show that data models with explicit features can be optimized by learning weights for their features.
Yim, Seung-Bin; Wünsche, Katharina; Cetin, Asil; Neidhardt, Julia; Baumann, Andreas; Wissik, Tanja
Visualizing Parliamentary Speeches as Networks: the DYLEN Tool Proceedings Article
In: Fišer, Darja; Eskevich, Maria; Lenardič, Jakob; Jong, Franciska (Ed.): pp. 56–60, European Language Resources Association (ELRA), 2022.
@inproceedings{ 20.500.12708_177100,
title = {Visualizing Parliamentary Speeches as Networks: the DYLEN Tool},
author = {Seung-Bin Yim and Katharina Wünsche and Asil Cetin and Julia Neidhardt and Andreas Baumann and Tanja Wissik},
editor = {Darja Fišer and Maria Eskevich and Jakob Lenardič and Franciska Jong},
doi = {10.34726/4105},
year = {2022},
date = {2022-01-01},
pages = {56–60},
publisher = {European Language Resources Association (ELRA)},
abstract = {In this paper, we present a web based interactive visualization tool for lexical networks based on the utterances of Austrian Members of Parliament. The tool is designed to compare two networks in parallel and is composed of graph visualization, node-metrics comparison and time-series comparison components that are interconnected with each other.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we present a web based interactive visualization tool for lexical networks based on the utterances of Austrian Members of Parliament. The tool is designed to compare two networks in parallel and is composed of graph visualization, node-metrics comparison and time-series comparison components that are interconnected with each other.
Kolb, Thomas; Sekanina, Katharina; Kern, Bettina Manuela Johanna; Neidhardt, Julia; Wissik, Tanja; Baumann, Andreas
The ALPIN Sentiment Dictionary: Austrian Language Polarity in Newspapers Proceedings Article
In: pp. 4708–4716, European Language Resources Association, Marseille, France, 2022.
@inproceedings{ 20.500.12708_177094,
title = {The ALPIN Sentiment Dictionary: Austrian Language Polarity in Newspapers},
author = {Thomas Kolb and Katharina Sekanina and Bettina Manuela Johanna Kern and Julia Neidhardt and Tanja Wissik and Andreas Baumann},
doi = {10.34726/4101},
year = {2022},
date = {2022-01-01},
pages = {4708–4716},
publisher = {European Language Resources Association},
address = {Marseille, France},
abstract = {This paper introduces the Austrian German sentiment dictionary ALPIN to account for the lack of resources for dictionary-based sentiment analysis in this specific variety of German, which is characterized by lexical idiosyncrasies that also affect word sentiment. The proposed language resource is based on Austrian news media in the field of politics, an austriacism list based on different resources and a posting data set based on a popular Austrian news media. Different resources are used to increase the diversity of the resulting language resource. Extensive crowd-sourcing is performed followed by evaluation and automatic conversion into sentiment scores. We show that crowd-sourcing enables the creation of a sentiment dictionary for the Austrian German domain. Additionally, the different parts of the sentiment dictionary are evaluated to show their impact on the resulting resource. Furthermore, the proposed dictionary is utilized in a web application and available for future research and free to use for anyone.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper introduces the Austrian German sentiment dictionary ALPIN to account for the lack of resources for dictionary-based sentiment analysis in this specific variety of German, which is characterized by lexical idiosyncrasies that also affect word sentiment. The proposed language resource is based on Austrian news media in the field of politics, an austriacism list based on different resources and a posting data set based on a popular Austrian news media. Different resources are used to increase the diversity of the resulting language resource. Extensive crowd-sourcing is performed followed by evaluation and automatic conversion into sentiment scores. We show that crowd-sourcing enables the creation of a sentiment dictionary for the Austrian German domain. Additionally, the different parts of the sentiment dictionary are evaluated to show their impact on the resulting resource. Furthermore, the proposed dictionary is utilized in a web application and available for future research and free to use for anyone.
Neidhardt, Julia; Werthner, Hannes; Woltran, Stefan
It Is Simple, It Is Complicated. Perspectives on Digital Humanism. Book Section
In: Werthner, Hannes; Prem, Erich; Lee, Edward A.; Ghezzi, Carlo (Ed.): Perspectives on Digital Humanism, pp. 335–342, Springer, 2022.
@incollection{ 20.500.12708_30705,
title = {It Is Simple, It Is Complicated. Perspectives on Digital Humanism.},
author = {Julia Neidhardt and Hannes Werthner and Stefan Woltran},
editor = {Hannes Werthner and Erich Prem and Edward A. Lee and Carlo Ghezzi},
year = {2022},
date = {2022-01-01},
booktitle = {Perspectives on Digital Humanism},
pages = {335–342},
publisher = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
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2021
Neidhardt, Julia
Network Science and e-Tourism Book Section
In: Xiang, Zheng; Fuchs, Matthias; Gretzel, Ulrike; Höpken, Wolfram (Ed.): Springer Cham, 2021.
@incollection{ 20.500.12708_152206,
title = {Network Science and e-Tourism},
author = {Julia Neidhardt},
editor = {Zheng Xiang and Matthias Fuchs and Ulrike Gretzel and Wolfram Höpken},
doi = {10.1007/978-3-030-05324-6_33-1},
year = {2021},
date = {2021-01-01},
publisher = {Springer Cham},
abstract = {This chapter provides an introduction to network science and its applications within e-tourism research. In the first part, an overview of network science as a continuously growing scientific field is given. Network science provides various concepts and methods for the analysis of the structure and dynamics of all kinds of networks such as social networks, information networks, and economic networks. Afterward, popular software and tools to model, analyze, and visualize network data are briefly discussed. In the third part, an overview of research in e-tourism that utilized network science methods is provided. In existing studies, different types of networks were constructed and analyzed, in particular networks of travelers, networks of tourism websites, networks capturing behavioral patterns of travelers, or text networks of travel-related posts. Furthermore, it is briefly discussed, which data sources are typically used in the literature. Finally, the main points are summarized and conclusions are drawn.},
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This chapter provides an introduction to network science and its applications within e-tourism research. In the first part, an overview of network science as a continuously growing scientific field is given. Network science provides various concepts and methods for the analysis of the structure and dynamics of all kinds of networks such as social networks, information networks, and economic networks. Afterward, popular software and tools to model, analyze, and visualize network data are briefly discussed. In the third part, an overview of research in e-tourism that utilized network science methods is provided. In existing studies, different types of networks were constructed and analyzed, in particular networks of travelers, networks of tourism websites, networks capturing behavioral patterns of travelers, or text networks of travel-related posts. Furthermore, it is briefly discussed, which data sources are typically used in the literature. Finally, the main points are summarized and conclusions are drawn.
Kolb, Thomas Elmar; Sekanina, Katharina; Kern, Bettina Manuela Johanna; Neidhardt, Julia; Baumann, Andreas; Wissik, Tanja
Creating an Austrian language polarity dictionary with the crowd Miscellaneous
2021.
@misc{ 20.500.12708_153831,
title = {Creating an Austrian language polarity dictionary with the crowd},
author = {Thomas Elmar Kolb and Katharina Sekanina and Bettina Manuela Johanna Kern and Julia Neidhardt and Andreas Baumann and Tanja Wissik},
year = {2021},
date = {2021-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Neidhardt, Julia; Wörndl, Wolfgang; Kuflik, Tsvi; Zanker, Markus
Workshop on Recommenders in Tourism (RecTour) Proceedings Article
In: Fifteenth ACM Conference on Recommender Systems, ACM, 2021.
@inproceedings{ 20.500.12708_58556,
title = {Workshop on Recommenders in Tourism (RecTour)},
author = {Julia Neidhardt and Wolfgang Wörndl and Tsvi Kuflik and Markus Zanker},
doi = {10.1145/3460231.3470930},
year = {2021},
date = {2021-01-01},
booktitle = {Fifteenth ACM Conference on Recommender Systems},
publisher = {ACM},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Kuflik, Tsvi; Barbu, Catalin Mihai; Delic, Amra; Goldenberg, Dmitri; Neidhardt, Julia; Coba, Ludovik; Zanker, Markus
WebTour 2021 Workshop on Web Tourism Proceedings Article
In: Proceedings of the 14th ACM International Conference on Web Search and Data Mining, ACM, 2021.
@inproceedings{ 20.500.12708_58574,
title = {WebTour 2021 Workshop on Web Tourism},
author = {Tsvi Kuflik and Catalin Mihai Barbu and Amra Delic and Dmitri Goldenberg and Julia Neidhardt and Ludovik Coba and Markus Zanker},
doi = {10.1145/3437963.3441836},
year = {2021},
date = {2021-01-01},
booktitle = {Proceedings of the 14th ACM International Conference on Web Search and Data Mining},
publisher = {ACM},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Neidhardt, Julia
Digital Humanism Ringvorlesung “Introduction to Digital Humanities” Miscellaneous
2021.
@misc{ 20.500.12708_87290,
title = {Digital Humanism Ringvorlesung "Introduction to Digital Humanities"},
author = {Julia Neidhardt},
year = {2021},
date = {2021-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Neidhardt, Julia
Digital Humanism. Digital Humanism – AI and Ethics Miscellaneous
2021.
@misc{ 20.500.12708_87289,
title = {Digital Humanism. Digital Humanism - AI and Ethics},
author = {Julia Neidhardt},
year = {2021},
date = {2021-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Neidhardt, Julia
ÖTSI and the polar(ity) landscape in Austria. Miscellaneous
2021.
@misc{ 20.500.12708_87284,
title = {ÖTSI and the polar(ity) landscape in Austria.},
author = {Julia Neidhardt},
year = {2021},
date = {2021-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Neidhardt, Julia
Imagine 21- Create your Future – Mission Impossible? Miscellaneous
2021.
@misc{ 20.500.12708_87287,
title = {Imagine 21- Create your Future - Mission Impossible?},
author = {Julia Neidhardt},
year = {2021},
date = {2021-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Hofmann, Klaus; Baumann, Andreas; Marakasova, Anna; Neidhardt, Julia; Wissik, Tanja
Semantische Netzwerke als Hilfsmittel für die Untersuchung des Begriffskomplexes Arbeit-Lohn-Vermögen im öffentlichen Diskurs Österreichs Proceedings Article
In: Momentum 2021 Kongress, 2021.
@inproceedings{ 20.500.12708_58724,
title = {Semantische Netzwerke als Hilfsmittel für die Untersuchung des Begriffskomplexes Arbeit-Lohn-Vermögen im öffentlichen Diskurs Österreichs},
author = {Klaus Hofmann and Andreas Baumann and Anna Marakasova and Julia Neidhardt and Tanja Wissik},
year = {2021},
date = {2021-01-01},
booktitle = {Momentum 2021 Kongress},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Baumann, Andreas; Hofmann, Klaus; Kern, Bettina; Marakasova, Anna; Neidhardt, Julia; Wissik, Tanja
Exploring Causal Relationships Among Emotional and Topical Trajectories in Political Text Data Proceedings Article
In: 3rd Conference on Language, Data and Knowledge (LDK 2021), pp. 1–8, OASICS, 93, 2021, ISBN: 978-3-95977-199-3.
@inproceedings{ 20.500.12708_55667,
title = {Exploring Causal Relationships Among Emotional and Topical Trajectories in Political Text Data},
author = {Andreas Baumann and Klaus Hofmann and Bettina Kern and Anna Marakasova and Julia Neidhardt and Tanja Wissik},
doi = {10.4230/OASIcs.LDK.2021.38},
isbn = {978-3-95977-199-3},
year = {2021},
date = {2021-01-01},
booktitle = {3rd Conference on Language, Data and Knowledge (LDK 2021)},
pages = {1–8},
publisher = {OASICS},
address = {93},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Kern, Bettina; Baumann, Andreas; Kolb, Thomas; Sekanina, Katharina; Hofmann, Klaus; Wissik, Tanja; Neidhardt, Julia
A review and cluster analysis of German polarity resources for sentiment analysis Proceedings Article
In: 3rd Conference on Language, Data and Knowledge (LDK 2021), pp. 1–17, OASICS, 93, 2021, ISBN: 978-3-95977-199-3.
@inproceedings{ 20.500.12708_55666,
title = {A review and cluster analysis of German polarity resources for sentiment analysis},
author = {Bettina Kern and Andreas Baumann and Thomas Kolb and Katharina Sekanina and Klaus Hofmann and Tanja Wissik and Julia Neidhardt},
doi = {10.4230/OASIcs.LDK.2021.37},
isbn = {978-3-95977-199-3},
year = {2021},
date = {2021-01-01},
booktitle = {3rd Conference on Language, Data and Knowledge (LDK 2021)},
pages = {1–17},
publisher = {OASICS},
address = {93},
keywords = {},
pubstate = {published},
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}
2020
Hofmann, Klaus; Marakasova, Anna; Baumann, Andreas; Neidhardt, Julia; Wissik, Tanja
Comparing Lexical Usage in Political Discourse across Diachronic Corpora. Proceedings Article
In: Proceedings of the Second ParlaCLARIN Workshop. Language Resources and Evaluation Conference (LREC 2020), Marseille, 11-16 May 2020., pp. 58–65, ACL Anthology, 2020.
@inproceedings{ 20.500.12708_58393,
title = {Comparing Lexical Usage in Political Discourse across Diachronic Corpora.},
author = {Klaus Hofmann and Anna Marakasova and Andreas Baumann and Julia Neidhardt and Tanja Wissik},
year = {2020},
date = {2020-01-01},
booktitle = {Proceedings of the Second ParlaCLARIN Workshop. Language Resources and Evaluation Conference (LREC 2020), Marseille, 11-16 May 2020.},
pages = {58–65},
publisher = {ACL Anthology},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Sertkan, Mete; Neidhardt, Julia; Werthner, Hannes
Eliciting Touristic Profiles: A User Study on Picture Collections Proceedings Article
In: Kuflik, Tsvi; Torre, Ilaria; Burke, Robin; Gena, Cristina (Ed.): Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization, Association for Computing Machinery, New York, NY, USA, 2020.
@inproceedings{ 20.500.12708_58382,
title = {Eliciting Touristic Profiles: A User Study on Picture Collections},
author = {Mete Sertkan and Julia Neidhardt and Hannes Werthner},
editor = {Tsvi Kuflik and Ilaria Torre and Robin Burke and Cristina Gena},
doi = {10.1145/3340631.3394868},
year = {2020},
date = {2020-01-01},
booktitle = {Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization},
publisher = {Association for Computing Machinery, New York, NY, USA},
abstract = {Eliciting the preferences and needs of tourists is challenging, since people often have difficulties to explicitly express them, especially in the initial phase of travel planning. Recommender systems employed at the early stage of planning can therefore be very beneficial to the general satisfaction of a user. Previous studies have explored pictures as a tool of communication and as a way to implicitly deduce a traveller's preferences and needs. In this paper, we conduct a user study to verify previous claims and conceptual work on the feasibility of modelling travel interests from a selection of a user's pictures. We utilize fine-tuned convolutional neural networks to compute a vector representation of a picture, where each dimension corresponds to a travel behavioural pattern from the traditional Seven-Factor model. In our study, we followed strict privacy principles and did not save uploaded pictures after computing their vector representation. We aggregate the representations of the pictures of a user into a single user representation, ie, touristic profile, using different strategies. In our user study with 81 participants, we let users adjust the predicted touristic profile and confirm the usefulness of our approach. Our results show that given a collection of pictures the touristic profile of a user can be determined.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Eliciting the preferences and needs of tourists is challenging, since people often have difficulties to explicitly express them, especially in the initial phase of travel planning. Recommender systems employed at the early stage of planning can therefore be very beneficial to the general satisfaction of a user. Previous studies have explored pictures as a tool of communication and as a way to implicitly deduce a traveller’s preferences and needs. In this paper, we conduct a user study to verify previous claims and conceptual work on the feasibility of modelling travel interests from a selection of a user’s pictures. We utilize fine-tuned convolutional neural networks to compute a vector representation of a picture, where each dimension corresponds to a travel behavioural pattern from the traditional Seven-Factor model. In our study, we followed strict privacy principles and did not save uploaded pictures after computing their vector representation. We aggregate the representations of the pictures of a user into a single user representation, ie, touristic profile, using different strategies. In our user study with 81 participants, we let users adjust the predicted touristic profile and confirm the usefulness of our approach. Our results show that given a collection of pictures the touristic profile of a user can be determined.
Sertkan, Mete; Neidhardt, Julia; Werthner, Hannes
From Pictures to Travel Characteristics: Deep Learning-Based Profiling of Tourists and Tourism Destinations Proceedings Article
In: Neidhardt, Julia; Wörndl, Wolfgang (Ed.): Information and Communication Technologies in Tourism 2020, pp. 142–153, Springer, 2020, ISBN: 9783030367374.
@inproceedings{ 20.500.12708_58381,
title = {From Pictures to Travel Characteristics: Deep Learning-Based Profiling of Tourists and Tourism Destinations},
author = {Mete Sertkan and Julia Neidhardt and Hannes Werthner},
editor = {Julia Neidhardt and Wolfgang Wörndl},
doi = {10.1007/978-3-030-36737-4_12},
isbn = {9783030367374},
year = {2020},
date = {2020-01-01},
booktitle = {Information and Communication Technologies in Tourism 2020},
pages = {142–153},
publisher = {Springer},
abstract = {Tourism products are complex and strongly tied to emotions. Thus, it is not easy for consumers to explicitly communicate their travel preferences, needs, and interest, especially in the early phase of travel decision making. In the spirit of the idiom "A picture is worth a thousand words" we utilize pictures to characterize tourists as well as tourism destinations in order to build the foundations of a recommender system (RS). In this work all entities (i.e., users and items) are characterized using the Seven-Factor Model. Pre-labelled pictures are used in order to train convolutional neural networks (CNN) in a transfer learning manner with the goal to extract the Seven-Factors of a given picture. We demonstrate that touristic characteristics can be extracted out of pictures. Furthermore, we show that those characteristics can be aggregated for a collection of pictures, such that a representation of a user or a destination can be determined respectively.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Tourism products are complex and strongly tied to emotions. Thus, it is not easy for consumers to explicitly communicate their travel preferences, needs, and interest, especially in the early phase of travel decision making. In the spirit of the idiom "A picture is worth a thousand words" we utilize pictures to characterize tourists as well as tourism destinations in order to build the foundations of a recommender system (RS). In this work all entities (i.e., users and items) are characterized using the Seven-Factor Model. Pre-labelled pictures are used in order to train convolutional neural networks (CNN) in a transfer learning manner with the goal to extract the Seven-Factors of a given picture. We demonstrate that touristic characteristics can be extracted out of pictures. Furthermore, we show that those characteristics can be aggregated for a collection of pictures, such that a representation of a user or a destination can be determined respectively.
Sertkan, Mete; Neidhardt, Julia; Werthner, Hannes
PicTouRe – A Picture-Based Tourism Recommender Proceedings Article
In: Fourteenth ACM Conference on Recommender Systems, Association for Computing Machinery, New York, United States, 2020.
@inproceedings{ 20.500.12708_58385,
title = {PicTouRe - A Picture-Based Tourism Recommender},
author = {Mete Sertkan and Julia Neidhardt and Hannes Werthner},
doi = {10.1145/3383313.3411526},
year = {2020},
date = {2020-01-01},
booktitle = {Fourteenth ACM Conference on Recommender Systems},
publisher = {Association for Computing Machinery, New York, United States},
abstract = {We present PicTouRe-a picture-based tourism recommender. PicTouRe aims to mitigate people´s difficulties in explicitly expressing their touristic preferences, which is even more challenging in the initial phase of travel decision making. Addressing this issue, with PicTouRe we follow the idiom "a picture is worth a thousand words" and use pictures as a tool to implicitly elicit peoples´ touristic preferences. We describe the core concept of PicTouRe-the Generic Profiler, which in essence determines an explainable vector representation, ie, touristic profile, given any picture collection as input. We showcase a user´s journey through PicTouRe and describe the steps behind. Finally, we present results of a first user study supporting our approach. PicTouRe is available under https://pictoprof. ec. tuwien. ac. at and a demo video under https://youtu. be/xZnXLPcenEs.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We present PicTouRe-a picture-based tourism recommender. PicTouRe aims to mitigate people´s difficulties in explicitly expressing their touristic preferences, which is even more challenging in the initial phase of travel decision making. Addressing this issue, with PicTouRe we follow the idiom "a picture is worth a thousand words" and use pictures as a tool to implicitly elicit peoples´ touristic preferences. We describe the core concept of PicTouRe-the Generic Profiler, which in essence determines an explainable vector representation, ie, touristic profile, given any picture collection as input. We showcase a user´s journey through PicTouRe and describe the steps behind. Finally, we present results of a first user study supporting our approach. PicTouRe is available under https://pictoprof. ec. tuwien. ac. at and a demo video under https://youtu. be/xZnXLPcenEs.
Marakasova, Anna; Neidhardt, Julia
Short-term Semantic Shifts and their Relation to Frequency Change Proceedings Article
In: Proceedings of the Probability and Meaning Conference (PaM 2020), pp. 146–153, 2020.
@inproceedings{ 20.500.12708_55595,
title = {Short-term Semantic Shifts and their Relation to Frequency Change},
author = {Anna Marakasova and Julia Neidhardt},
year = {2020},
date = {2020-01-01},
booktitle = {Proceedings of the Probability and Meaning Conference (PaM 2020)},
pages = {146–153},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Neidhardt, Julia
Digitaler Humanismus Miscellaneous
2020.
@misc{ 20.500.12708_87124,
title = {Digitaler Humanismus},
author = {Julia Neidhardt},
year = {2020},
date = {2020-01-01},
abstract = {Die Digitalisierung eröffnet einerseits beispiellose Möglichkeiten, wirft jedoch andererseits auch schwerwiegende Bedenken auf - wie die Monopolisierung des Webs, die Bildung von Filterblasen und Echokammern als Inseln entkoppelter Wahrheiten, ethische Fragen im Zusammenhang mit Künstlicher Intelligenz oder die Verbreitung der digitalen Überwachung. Der Vortrag befasst sich mit diesen Fragen und fordert einen Digitalen Humanismus, der das komplexe Zusammenspiel von Technologie und Menschheit beschreibt, analysiert und vor allem für eine bessere Gesellschaft beeinflusst.
https://arltsymposium.fhstp.ac.at/keynotes-2020/},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Die Digitalisierung eröffnet einerseits beispiellose Möglichkeiten, wirft jedoch andererseits auch schwerwiegende Bedenken auf – wie die Monopolisierung des Webs, die Bildung von Filterblasen und Echokammern als Inseln entkoppelter Wahrheiten, ethische Fragen im Zusammenhang mit Künstlicher Intelligenz oder die Verbreitung der digitalen Überwachung. Der Vortrag befasst sich mit diesen Fragen und fordert einen Digitalen Humanismus, der das komplexe Zusammenspiel von Technologie und Menschheit beschreibt, analysiert und vor allem für eine bessere Gesellschaft beeinflusst.
https://arltsymposium.fhstp.ac.at/keynotes-2020/
https://arltsymposium.fhstp.ac.at/keynotes-2020/
Neidhardt, Julia
Digital Humanism Miscellaneous
2020.
@misc{ 20.500.12708_87123,
title = {Digital Humanism},
author = {Julia Neidhardt},
year = {2020},
date = {2020-01-01},
abstract = {https://www.linkedin.com/events/1stwidsviennaconference/},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
https://www.linkedin.com/events/1stwidsviennaconference/
Neidhardt, Julia
A Picture-based Approach to Travel Recommender Systems Miscellaneous
2020.
@misc{ 20.500.12708_87126,
title = {A Picture-based Approach to Travel Recommender Systems},
author = {Julia Neidhardt},
year = {2020},
date = {2020-01-01},
abstract = {http://www.cs.ox.ac.uk/seminars/2331.html
Personalized recommendations strongly rely on an accurate model to capture user preferences. Eliciting this information is, in general, a hard problem. In the field of tourism, this initial profiling becomes even more challenging. It has been shown that particularly in the beginning of the travel decision making process, users themselves are often not conscious of their needs and are not able to express them. Aiming at revealing implicitly given user preferences, this work introduces an approach that utilizes a set of travel related pictures to discover users' travel behavior and in turn, to deliver recommendations. Next, a more general approach based on convolutional neural networks is introduced, where any set of pictures can be used to characterize both travelers and tourism destinations. This talk discusses a stream of studies to quantify intangible user preferences and to provide easy and playful methods to generate inputs/data for recommendation systems.},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
http://www.cs.ox.ac.uk/seminars/2331.html
Personalized recommendations strongly rely on an accurate model to capture user preferences. Eliciting this information is, in general, a hard problem. In the field of tourism, this initial profiling becomes even more challenging. It has been shown that particularly in the beginning of the travel decision making process, users themselves are often not conscious of their needs and are not able to express them. Aiming at revealing implicitly given user preferences, this work introduces an approach that utilizes a set of travel related pictures to discover users’ travel behavior and in turn, to deliver recommendations. Next, a more general approach based on convolutional neural networks is introduced, where any set of pictures can be used to characterize both travelers and tourism destinations. This talk discusses a stream of studies to quantify intangible user preferences and to provide easy and playful methods to generate inputs/data for recommendation systems.
Personalized recommendations strongly rely on an accurate model to capture user preferences. Eliciting this information is, in general, a hard problem. In the field of tourism, this initial profiling becomes even more challenging. It has been shown that particularly in the beginning of the travel decision making process, users themselves are often not conscious of their needs and are not able to express them. Aiming at revealing implicitly given user preferences, this work introduces an approach that utilizes a set of travel related pictures to discover users’ travel behavior and in turn, to deliver recommendations. Next, a more general approach based on convolutional neural networks is introduced, where any set of pictures can be used to characterize both travelers and tourism destinations. This talk discusses a stream of studies to quantify intangible user preferences and to provide easy and playful methods to generate inputs/data for recommendation systems.
Neidhardt, Julia; Werthner, Hannes
Digital Humanism Miscellaneous
2020.
@misc{ 20.500.12708_87125,
title = {Digital Humanism},
author = {Julia Neidhardt and Hannes Werthner},
year = {2020},
date = {2020-01-01},
abstract = {https://www.bcsss.org/de/homo-digitalis/},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
https://www.bcsss.org/de/homo-digitalis/
Gretzel, Ulrike; Fuchs, Matthias; Baggio, Rodolfo; Hoepken, Wolfram; Law, Rob; Neidhardt, Julia; Pesonen, Juho; Zanker, Markus; Xiang, Zheng
e-Tourism beyond COVID-19: a call for transformative research. Journal Article
In: Information Technology and Tourism, vol. 22, iss. 2, pp. 187–203, 2020, ISSN: 1098-3058.
@article{ 20.500.12708_141675,
title = {e-Tourism beyond COVID-19: a call for transformative research.},
author = {Ulrike Gretzel and Matthias Fuchs and Rodolfo Baggio and Wolfram Hoepken and Rob Law and Julia Neidhardt and Juho Pesonen and Markus Zanker and Zheng Xiang},
doi = {10.1007/s40558-020-00181-3},
issn = {1098-3058},
year = {2020},
date = {2020-01-01},
journal = {Information Technology and Tourism},
volume = {22},
issue = {2},
pages = {187–203},
keywords = {},
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}