Publications
Explore the publications from our RecSys laboratory.
21.
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.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
22.
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.},
keywords = {},
pubstate = {published},
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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.
23.
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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pubstate = {published},
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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.
24.
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.},
keywords = {},
pubstate = {published},
tppubtype = {article}
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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.
25.
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 = {},
pubstate = {published},
tppubtype = {inproceedings}
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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.
26.
Wagne, Ahmadou
COVID-19 and populism in Austrian news user comments – A machine learning approach Masters Thesis
Technische Universität Wien, Wien, 2023.
@mastersthesis{ 20.500.12708_176675,
title = {COVID-19 and populism in Austrian news user comments - A machine learning approach},
author = {Ahmadou Wagne},
doi = {10.34726/hss.2023.105940},
year = {2023},
date = {2023-01-01},
address = {Wien},
school = {Technische Universität Wien},
abstract = {The COVID-19 pandemic and the resulting government measures have triggered a wave of protests and demonstrations in Austria. We saw some protestors resorting to populist rhetoric to express their dissatisfaction, which in some cases, led to anti-democratic tendencies. Populist talking points have not been confined to the public sphere but rather emerged from social media and other online platforms, including news comments. Thus, there is a need to develop automated methods to detect populist statements in those texts. While previous research on populism has focused on politicians, recent studies have emphasized the role of citizens as populist actors. However, most of the current methods to detect populism in text rely on manual coding or dictionary-based approaches. Only a few scholars have attempted to employ machine learning for this task, resulting in a shortage of annotated data, particularly for the German language. To address this gap, this thesis adopts a minimalistic ideational definition of populism and performs various experiments using BERT-based transformer models to enhance the detection of populist user-generated content. Additionally, the thesis presents the first annotated dataset for populist news user comments in the German language by conducting an annotation study. The proposed model outperforms the state-of-the-art in this area and is applied in a case study analyzing the correlation between COVID-19 and populism in Austrian news user comments. A large-scale analysis of comments in the news forum of the Austrian daily newspaper Der Standard is conducted and the study reveals that the topic of COVID-19 in news articles attracted more populist comments than other topics during the pandemic. From these findings, implications can be drawn for future crisis management and communication.},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
The COVID-19 pandemic and the resulting government measures have triggered a wave of protests and demonstrations in Austria. We saw some protestors resorting to populist rhetoric to express their dissatisfaction, which in some cases, led to anti-democratic tendencies. Populist talking points have not been confined to the public sphere but rather emerged from social media and other online platforms, including news comments. Thus, there is a need to develop automated methods to detect populist statements in those texts. While previous research on populism has focused on politicians, recent studies have emphasized the role of citizens as populist actors. However, most of the current methods to detect populism in text rely on manual coding or dictionary-based approaches. Only a few scholars have attempted to employ machine learning for this task, resulting in a shortage of annotated data, particularly for the German language. To address this gap, this thesis adopts a minimalistic ideational definition of populism and performs various experiments using BERT-based transformer models to enhance the detection of populist user-generated content. Additionally, the thesis presents the first annotated dataset for populist news user comments in the German language by conducting an annotation study. The proposed model outperforms the state-of-the-art in this area and is applied in a case study analyzing the correlation between COVID-19 and populism in Austrian news user comments. A large-scale analysis of comments in the news forum of the Austrian daily newspaper Der Standard is conducted and the study reveals that the topic of COVID-19 in news articles attracted more populist comments than other topics during the pandemic. From these findings, implications can be drawn for future crisis management and communication.
27.
28.
Nalis-Neuner, Irina
Sentiment Analysis – psychological perspective Miscellaneous
2023.
@misc{ 20.500.12708_193816,
title = {Sentiment Analysis - psychological perspective},
author = {Irina Nalis-Neuner},
year = {2023},
date = {2023-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
29.
Sertkan, Mete; Althammer, Sophia; Hofstätter, Sebastian
Ranger: A Toolkit for Effect-Size Based Multi-Task Evaluation Proceedings Article
In: Danushka, Bollegala (Ed.): pp. 581–587, Association for Computational Linguistics, 2023.
@inproceedings{ 20.500.12708_192941,
title = {Ranger: A Toolkit for Effect-Size Based Multi-Task Evaluation},
author = {Mete Sertkan and Sophia Althammer and Sebastian Hofstätter},
editor = {Bollegala Danushka},
doi = {10.18653/v1/2023.acl-demo.56},
year = {2023},
date = {2023-01-01},
volume = {Volume 3: System Demonstrations},
pages = {581–587},
publisher = {Association for Computational Linguistics},
abstract = {In this paper, we introduce Ranger - a toolkit to facilitate the easy use of effect-size-based meta-analysis for multi-task evaluation in NLP and IR. We observed that our communities often face the challenge of aggregating results over incomparable metrics and scenarios, which makes conclusions and take-away messages less reliable. With Ranger, we aim to address this issue by providing a task-agnostic toolkit that combines the effect of a treatment on multiple tasks into one statistical evaluation, allowing for comparison of metrics and computation of an overall summary effect. Our toolkit produces publication-ready forest plots that enable clear communication of evaluation results over multiple tasks. Our goal with the ready-to-use Ranger toolkit is to promote robust, effect-size-based evaluation and improve evaluation standards in the community. We provide two case studies for common IR and NLP settings to highlight Ranger’s benefits.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
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In this paper, we introduce Ranger – a toolkit to facilitate the easy use of effect-size-based meta-analysis for multi-task evaluation in NLP and IR. We observed that our communities often face the challenge of aggregating results over incomparable metrics and scenarios, which makes conclusions and take-away messages less reliable. With Ranger, we aim to address this issue by providing a task-agnostic toolkit that combines the effect of a treatment on multiple tasks into one statistical evaluation, allowing for comparison of metrics and computation of an overall summary effect. Our toolkit produces publication-ready forest plots that enable clear communication of evaluation results over multiple tasks. Our goal with the ready-to-use Ranger toolkit is to promote robust, effect-size-based evaluation and improve evaluation standards in the community. We provide two case studies for common IR and NLP settings to highlight Ranger’s benefits.
30.
Afzaal, Muhammad; Zia, Aayesha; Nouri, Jalal; Fors, Uno
Informative Feedback and Explainable AI-Based Recommendations to Support Students’ Self-regulation Journal Article
In: Technology, Knowledge and Learning, pp. 1–24, 2023.
@article{afzaal2023informative,
title = {Informative Feedback and Explainable AI-Based Recommendations to Support Students’ Self-regulation},
author = {Muhammad Afzaal and Aayesha Zia and Jalal Nouri and Uno Fors},
year = {2023},
date = {2023-01-01},
journal = {Technology, Knowledge and Learning},
pages = {1–24},
publisher = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
31.
Afzaal, Muhammad; Nouri, Jalal; Aayesha, Aayesha
A Transformer-Based Approach for the Automatic Generation of Concept-Wise Exercises to Provide Personalized Learning Support to Students Proceedings Article
In: European Conference on Technology Enhanced Learning, pp. 16–31, Springer 2023.
@inproceedings{afzaal2023transformer,
title = {A Transformer-Based Approach for the Automatic Generation of Concept-Wise Exercises to Provide Personalized Learning Support to Students},
author = {Muhammad Afzaal and Jalal Nouri and Aayesha Aayesha},
year = {2023},
date = {2023-01-01},
booktitle = {European Conference on Technology Enhanced Learning},
pages = {16–31},
organization = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
32.
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.},
keywords = {},
pubstate = {published},
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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.
33.
34.
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.
35.
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.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
36.
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},
tppubtype = {inproceedings}
}
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.
37.
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.
38.
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.
39.
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 = {},
pubstate = {published},
tppubtype = {article}
}
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.
40.
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.