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
2020
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/},
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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},
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Pesonen, Juho; Neidhardt, Julia
Special issue: perspectives on eTourism Journal Article
In: Information Technology and Tourism, vol. 22, iss. 1, pp. 1–3, 2020, ISSN: 1098-3058.
@article{ 20.500.12708_141676,
title = {Special issue: perspectives on eTourism},
author = {Juho Pesonen and Julia Neidhardt},
doi = {10.1007/s40558-020-00171-5},
issn = {1098-3058},
year = {2020},
date = {2020-01-01},
journal = {Information Technology and Tourism},
volume = {22},
issue = {1},
pages = {1–3},
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Neidhardt, Julia; Wörndl, Wolfgang; Paris, Cody Morris
Technology: ENTER2020 Special Issue of eRTR Journal Article
In: e-Review of Tourism Research (eRTR), vol. 17, iss. 5, pp. 1–2, 2020, ISSN: 1941-5842.
@article{ 20.500.12708_141677,
title = {Technology: ENTER2020 Special Issue of eRTR},
author = {Julia Neidhardt and Wolfgang Wörndl and Cody Morris Paris},
issn = {1941-5842},
year = {2020},
date = {2020-01-01},
journal = {e-Review of Tourism Research (eRTR)},
volume = {17},
issue = {5},
pages = {1–2},
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https://ertr.tamu.edu/volume-17-issue-5-enter-special-issue/
Baumann, Andreas; Hofmann, Klaus; Marakasova, Anna; Neidhardt, Julia; Wissik, Tanja
Semantic shifts in the Austrian public discourse: A lexical networks approach Proceedings Article
In: 42nd Annual Conference of the German Linguistic Society (DGfS 2020), 2020.
@inproceedings{ 20.500.12708_58392,
title = {Semantic shifts in the Austrian public discourse: A lexical networks approach},
author = {Andreas Baumann and Klaus Hofmann and Anna Marakasova and Julia Neidhardt and Tanja Wissik},
year = {2020},
date = {2020-01-01},
booktitle = {42nd Annual Conference of the German Linguistic Society (DGfS 2020)},
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2019
Sertkan, Mete; Neidhardt, Julia; Werthner, Hannes
What is the “Personality” of a tourism destination? Journal Article
In: Information Technology and Tourism, 2019, ISSN: 1098-3058.
@article{ 20.500.12708_274,
title = {What is the “Personality” of a tourism destination?},
author = {Mete Sertkan and Julia Neidhardt and Hannes Werthner},
doi = {10.1007/s40558-018-0135-6},
issn = {1098-3058},
year = {2019},
date = {2019-01-01},
journal = {Information Technology and Tourism},
publisher = {Springer},
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Seyfang, Leonhard; Neidhardt, Julia
A Framework for Recommender Systems Based on a Finite Multidimensional Model Space. Proceedings Article
In: Proceedings of the ACM Workshop of Recommenders in Tourism (RecTour) 2019, pp. 27–31, 2019.
@inproceedings{ 20.500.12708_57580,
title = {A Framework for Recommender Systems Based on a Finite Multidimensional Model Space.},
author = {Leonhard Seyfang and Julia Neidhardt},
year = {2019},
date = {2019-01-01},
booktitle = {Proceedings of the ACM Workshop of Recommenders in Tourism (RecTour) 2019},
pages = {27–31},
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Delic, Amra; Ricci, Francesco; Neidhardt, Julia
Preference Networks and Non-Linear Preferences in Group Recommendations Proceedings Article
In: IEEE/WIC/ACM International Conference on Web Intelligence, 2019.
@inproceedings{ 20.500.12708_58133,
title = {Preference Networks and Non-Linear Preferences in Group Recommendations},
author = {Amra Delic and Francesco Ricci and Julia Neidhardt},
doi = {10.1145/3350546.3352556},
year = {2019},
date = {2019-01-01},
booktitle = {IEEE/WIC/ACM International Conference on Web Intelligence},
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Sertkan, Mete; Neidhardt, Julia; Werthner, Hannes
From Pictures to Touristic Profiles: A Deep-Learning Based Approach Proceedings Article
In: ProceedingsDSRS-Turing´19., pp. 75–78, 2019, ISBN: 978-1-5262-0820-0.
@inproceedings{ 20.500.12708_58129,
title = {From Pictures to Touristic Profiles: A Deep-Learning Based Approach},
author = {Mete Sertkan and Julia Neidhardt and Hannes Werthner},
isbn = {978-1-5262-0820-0},
year = {2019},
date = {2019-01-01},
booktitle = {ProceedingsDSRS-Turing´19.},
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Baumann, Andreas; Neidhardt, Julia; Wissik, Tanja
DYLEN: Diachronic Dynamics of Lexical Networks. Proceedings Article
In: Poster Proceedings of 2nd Conference on Language, Data and Knowledge (LDK 2019), pp. 1–5, 2019.
@inproceedings{ 20.500.12708_55541,
title = {DYLEN: Diachronic Dynamics of Lexical Networks.},
author = {Andreas Baumann and Julia Neidhardt and Tanja Wissik},
year = {2019},
date = {2019-01-01},
booktitle = {Poster Proceedings of 2nd Conference on Language, Data and Knowledge (LDK 2019)},
pages = {1–5},
series = {CEUR Workshop Proceedings},
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Sertkan, Mete; Neidhardt, Julia; Werthner, Hannes
Documents, Topics, and Authors: Text Mining of Online News Proceedings Article
In: 2019 IEEE 21st Conference on Business Informatics (CBI), 2019.
@inproceedings{ 20.500.12708_57685,
title = {Documents, Topics, and Authors: Text Mining of Online News},
author = {Mete Sertkan and Julia Neidhardt and Hannes Werthner},
doi = {10.1109/cbi.2019.00053},
year = {2019},
date = {2019-01-01},
booktitle = {2019 IEEE 21st Conference on Business Informatics (CBI)},
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Neidhardt, Julia; Wörndl, Wolfgang; Kuflik, Tsvi; Zanker, Markus; Barbu, Catalin-Mihai
RecTour 2019 Proceedings Article
In: Proceedings of the 13th ACM Conference on Recommender Systems, ACM Digital Library, 2019.
@inproceedings{ 20.500.12708_57654,
title = {RecTour 2019},
author = {Julia Neidhardt and Wolfgang Wörndl and Tsvi Kuflik and Markus Zanker and Catalin-Mihai Barbu},
doi = {10.1145/3298689.3346969},
year = {2019},
date = {2019-01-01},
booktitle = {Proceedings of the 13th ACM Conference on Recommender Systems},
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Akhtar, Mubashra; Neidhardt, Julia; Werthner, Hannes
The Potential of Chatbots: Analysis of Chatbot Conversations Proceedings Article
In: 2019 IEEE 21st Conference on Business Informatics (CBI), 2019.
@inproceedings{ 20.500.12708_57691,
title = {The Potential of Chatbots: Analysis of Chatbot Conversations},
author = {Mubashra Akhtar and Julia Neidhardt and Hannes Werthner},
doi = {10.1109/cbi.2019.00052},
year = {2019},
date = {2019-01-01},
booktitle = {2019 IEEE 21st Conference on Business Informatics (CBI)},
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Neidhardt, Julia
Recommender Systems and Decision-making in the Tourism Domain. Miscellaneous
2019.
@misc{ 20.500.12708_87037,
title = {Recommender Systems and Decision-making in the Tourism Domain.},
author = {Julia Neidhardt},
year = {2019},
date = {2019-01-01},
keywords = {},
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Neidhardt, Julia; Wörndl, Wolfgang; Paris, Cody Morris
Destinations: ENTER2020 Special Issue of eRTR Journal Article
In: e-Review of Tourism Research (eRTR), vol. 17, iss. 2, pp. 1–2, 2019, ISSN: 1941-5842.
@article{ 20.500.12708_144239,
title = {Destinations: ENTER2020 Special Issue of eRTR},
author = {Julia Neidhardt and Wolfgang Wörndl and Cody Morris Paris},
issn = {1941-5842},
year = {2019},
date = {2019-01-01},
journal = {e-Review of Tourism Research (eRTR)},
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https://ertr.tamu.edu/volume-17-issue-2-enter-special-issue/
Neidhardt, Julia; Wörndl, Wolfgang; Paris, Cody Morris
Innovation: ENTER2020 Special Issue of eRTR. Journal Article
In: e-Review of Tourism Research (eRTR), vol. 17, iss. 3, pp. 1–2, 2019, ISSN: 1941-5842.
@article{ 20.500.12708_144238,
title = {Innovation: ENTER2020 Special Issue of eRTR.},
author = {Julia Neidhardt and Wolfgang Wörndl and Cody Morris Paris},
issn = {1941-5842},
year = {2019},
date = {2019-01-01},
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Neidhardt, Julia; Wörndl, Wolfgang; Paris, Cody Morris
Social Media: ENTER2020 Special Issue of eRTR Journal Article
In: e-Review of Tourism Research (eRTR), vol. 17, iss. 4, pp. 1–2, 2019, ISSN: 1941-5842.
@article{ 20.500.12708_144236,
title = {Social Media: ENTER2020 Special Issue of eRTR},
author = {Julia Neidhardt and Wolfgang Wörndl and Cody Morris Paris},
issn = {1941-5842},
year = {2019},
date = {2019-01-01},
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https://ertr.tamu.edu/volume-17-issue-4-enter-special-issue/
Sertkan, Mete; Neidhardt, Julia; Werthner, Hannes
Von Reisezielmerkmalen zur Reisezielpersönlichkeit Journal Article
In: Tourismuswissen – quarterly, vol. APRIL, pp. 91–98, 2019.
@article{ 20.500.12708_143937,
title = {Von Reisezielmerkmalen zur Reisezielpersönlichkeit},
author = {Mete Sertkan and Julia Neidhardt and Hannes Werthner},
year = {2019},
date = {2019-01-01},
journal = {Tourismuswissen - quarterly},
volume = {APRIL},
pages = {91–98},
keywords = {},
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2018
Neidhardt, Julia; Werthner, Hannes
IT and tourism: still a hot topic, but do not forget IT Journal Article
In: Information Technology and Tourism, 2018, ISSN: 1098-3058.
@article{ 20.500.12708_255,
title = {IT and tourism: still a hot topic, but do not forget IT},
author = {Julia Neidhardt and Hannes Werthner},
doi = {10.1007/s40558-018-0115-x},
issn = {1098-3058},
year = {2018},
date = {2018-01-01},
journal = {Information Technology and Tourism},
publisher = {Springer},
abstract = {More and more aspects of our life “move” to the Web. The Web and the Internet, as the underlying information infrastructure and “machine”, can be considered as a mirror of the “real” physical world. However, the Web is not only reflecting this world, it is obviously also transforming it, where it is increasingly hard to distinguish between the physical and the virtual. It acts both as an enabler and driver of new, technical, economic and societal developments. With recent achievements in areas such as machine learning, Internet of Things or artificial intelligence (or rather intelligent assistance—a probably more appropriate term), we see the power of computer science. In this short comment, we argue that IT and tourism is still a hot topic, also or especially from a scientific point of view. However, from this latter point of view this might change, and we claim that, in order to prevent the field from becoming insignificant, more emphasis must be put on technical and formal aspects of science. We do not know the future, we are not “futurologists” (In a marketing note of an innovation and technology conference, the organizers even wanted to provide a look beyond the future—we have no idea how this nonsense could be done!), but we take the freedom to highlight some issues, which we consider to be important—in particular since one of the authors has a background of driving the field for nearly 30 years.},
keywords = {},
pubstate = {published},
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More and more aspects of our life “move” to the Web. The Web and the Internet, as the underlying information infrastructure and “machine”, can be considered as a mirror of the “real” physical world. However, the Web is not only reflecting this world, it is obviously also transforming it, where it is increasingly hard to distinguish between the physical and the virtual. It acts both as an enabler and driver of new, technical, economic and societal developments. With recent achievements in areas such as machine learning, Internet of Things or artificial intelligence (or rather intelligent assistance—a probably more appropriate term), we see the power of computer science. In this short comment, we argue that IT and tourism is still a hot topic, also or especially from a scientific point of view. However, from this latter point of view this might change, and we claim that, in order to prevent the field from becoming insignificant, more emphasis must be put on technical and formal aspects of science. We do not know the future, we are not “futurologists” (In a marketing note of an innovation and technology conference, the organizers even wanted to provide a look beyond the future—we have no idea how this nonsense could be done!), but we take the freedom to highlight some issues, which we consider to be important—in particular since one of the authors has a background of driving the field for nearly 30 years.
Delic, Amra; Neidhardt, Julia; Nguyen, Thuy Ngoc; Ricci, Francesco
An observational user study for group recommender systems in the tourism domain Journal Article
In: Information Technology and Tourism, vol. 19, iss. 1-4, pp. 87–116, 2018, ISSN: 1098-3058.
@article{ 20.500.12708_83,
title = {An observational user study for group recommender systems in the tourism domain},
author = {Amra Delic and Julia Neidhardt and Thuy Ngoc Nguyen and Francesco Ricci},
doi = {10.1007/s40558-018-0106-y},
issn = {1098-3058},
year = {2018},
date = {2018-01-01},
journal = {Information Technology and Tourism},
volume = {19},
issue = {1-4},
pages = {87–116},
publisher = {SPRINGER HEIDELBERG},
abstract = {In this article we argue and give evidence that the research on group recommender systems must look more carefully at the dynamics of group decision-making in order to produce technologies that will be truly beneficial for groups. We illustrate the adopted research method and the results of a user study aimed at observing and measuring the evolution of user preferences and interaction in a tourism decision-making task: finding a destination to visit together as a group. We discuss the benefits and caveats of such an observational study method and we present the implications that the derived data and findings have on the design of interactive group recommender systems.},
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In this article we argue and give evidence that the research on group recommender systems must look more carefully at the dynamics of group decision-making in order to produce technologies that will be truly beneficial for groups. We illustrate the adopted research method and the results of a user study aimed at observing and measuring the evolution of user preferences and interaction in a tourism decision-making task: finding a destination to visit together as a group. We discuss the benefits and caveats of such an observational study method and we present the implications that the derived data and findings have on the design of interactive group recommender systems.
Neidhardt, Julia; Wörndl, Wolfgang; Kuflik, Tsvi; Zanker, Markus
ACM recsys workshop on recommenders in tourism (rectour 2018) Proceedings Article
In: Proceedings of the 12th ACM Conference on Recommender Systems, ACM Digital Library, New York, 2018.
@inproceedings{ 20.500.12708_57718,
title = {ACM recsys workshop on recommenders in tourism (rectour 2018)},
author = {Julia Neidhardt and Wolfgang Wörndl and Tsvi Kuflik and Markus Zanker},
doi = {10.1145/3240323.3240341},
year = {2018},
date = {2018-01-01},
booktitle = {Proceedings of the 12th ACM Conference on Recommender Systems},
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address = {New York},
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Neidhardt, Julia
Social Influence and Opinion Forming in Online Social Networks. Miscellaneous
2018.
@misc{ 20.500.12708_86851,
title = {Social Influence and Opinion Forming in Online Social Networks.},
author = {Julia Neidhardt},
year = {2018},
date = {2018-01-01},
abstract = {Web-based phenomena such as fake news, hate speech or echo chambers have been gaining increasing attention in the public discourse. However, the extent and actual impact of these phenomena are still unknown. In order to address these issues in more depth, it is important to understand how opinions emerge, change and get exchanged. In our work, we therefore intend to develop data-driven approaches to examine social influence mechanisms and opinion forming at a large scale.
We aim, in particular, to introduce a complex user model, which characterizes each person with respect to three levels: 1) the individual level capturing the characteristics of a user; 2) the network level capturing the social relationships of a user; and 3) the group level describing sets of similar users with certain dominant opinions and high-level ideologies. We introduce the concepts based on a rich and unique dataset of an online news forum that contains the complete posting history of all users, their detailed behaviors and interactions as well as all news articles and the respective meta data for almost two decades.},
keywords = {},
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Web-based phenomena such as fake news, hate speech or echo chambers have been gaining increasing attention in the public discourse. However, the extent and actual impact of these phenomena are still unknown. In order to address these issues in more depth, it is important to understand how opinions emerge, change and get exchanged. In our work, we therefore intend to develop data-driven approaches to examine social influence mechanisms and opinion forming at a large scale.
We aim, in particular, to introduce a complex user model, which characterizes each person with respect to three levels: 1) the individual level capturing the characteristics of a user; 2) the network level capturing the social relationships of a user; and 3) the group level describing sets of similar users with certain dominant opinions and high-level ideologies. We introduce the concepts based on a rich and unique dataset of an online news forum that contains the complete posting history of all users, their detailed behaviors and interactions as well as all news articles and the respective meta data for almost two decades.
We aim, in particular, to introduce a complex user model, which characterizes each person with respect to three levels: 1) the individual level capturing the characteristics of a user; 2) the network level capturing the social relationships of a user; and 3) the group level describing sets of similar users with certain dominant opinions and high-level ideologies. We introduce the concepts based on a rich and unique dataset of an online news forum that contains the complete posting history of all users, their detailed behaviors and interactions as well as all news articles and the respective meta data for almost two decades.
Sertkan, Mete; Neidhardt, Julia; Werthner, Hannes
Mapping of Tourism Destinations to Travel Behavioural Patterns Proceedings Article
In: Information and Communication Technologies in Tourism 2018, pp. 422–434, Springer, Cham, 2018, ISBN: 9783319729237.
@inproceedings{ 20.500.12708_57174,
title = {Mapping of Tourism Destinations to Travel Behavioural Patterns},
author = {Mete Sertkan and Julia Neidhardt and Hannes Werthner},
doi = {10.1007/978-3-319-72923-7_32},
isbn = {9783319729237},
year = {2018},
date = {2018-01-01},
booktitle = {Information and Communication Technologies in Tourism 2018},
pages = {422–434},
publisher = {Springer, Cham},
abstract = {Tourism is an information intensive domain, where recommender systems have become an essential tool to guide customers to the right products. However, they are facing major challenges, since tourism products are considered as complex and emotional. It has been shown that the seven-factor model is a legitimate way to counter some of these challenges. However, in order to recommend an item, it has also to be described in terms of this model. This work´s aim is to find a scalable way to map tourism destinations, defined by their attributes, to the seven-factor model. Through statistical analysis and learning methods it is shown that there is a significant relationship between particular destination features and the seven-factors and that destinations can be grouped in a meaningful way using their attributes.},
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Tourism is an information intensive domain, where recommender systems have become an essential tool to guide customers to the right products. However, they are facing major challenges, since tourism products are considered as complex and emotional. It has been shown that the seven-factor model is a legitimate way to counter some of these challenges. However, in order to recommend an item, it has also to be described in terms of this model. This work´s aim is to find a scalable way to map tourism destinations, defined by their attributes, to the seven-factor model. Through statistical analysis and learning methods it is shown that there is a significant relationship between particular destination features and the seven-factors and that destinations can be grouped in a meaningful way using their attributes.
Delic, Amra; Neidhardt, Julia; Werthner, Hannes
Group Decision Making and Group Recommendations in Tourism Domain Proceedings Article
In: CBI 2018 – Proceedings of the 20th IEEE International Conference on Business Informatics, IEEE, 2018.
@inproceedings{ 20.500.12708_57392,
title = {Group Decision Making and Group Recommendations in Tourism Domain},
author = {Amra Delic and Julia Neidhardt and Hannes Werthner},
year = {2018},
date = {2018-01-01},
booktitle = {CBI 2018 - Proceedings of the 20th IEEE International Conference on Business Informatics},
publisher = {IEEE},
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Delic, Amra; Masthoff, Judith; Neidhardt, Julia; Werthner, Hannes
How to Use Social Relationships in Group Recommenders Proceedings Article
In: Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization, ACM New York, 2018.
@inproceedings{ 20.500.12708_57391,
title = {How to Use Social Relationships in Group Recommenders},
author = {Amra Delic and Judith Masthoff and Julia Neidhardt and Hannes Werthner},
doi = {10.1145/3209219.3209226},
year = {2018},
date = {2018-01-01},
booktitle = {Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization},
publisher = {ACM New York},
abstract = {In this paper we present the results of a user study focusing on social relationships within small groups. The goal is to better understand how to incorporate the information about social relationships in group recommendation models. Our analysis, conducted on a data set of 150 participants in 41 groups deciding on a travel destination to visit together, brings out some intriguing outcomes. We demonstrate that social centrality is hardly an indicator of the social influence in the decision-making process of "equality matching" types of groups. However, socially central group members and socially close groups are significantly happier with group decisions than those who are loosely related. Moreover, in this paper we show that social relationships are indicators of other concepts relevant in group settings, therefore in group recommender systems as well.},
keywords = {},
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In this paper we present the results of a user study focusing on social relationships within small groups. The goal is to better understand how to incorporate the information about social relationships in group recommendation models. Our analysis, conducted on a data set of 150 participants in 41 groups deciding on a travel destination to visit together, brings out some intriguing outcomes. We demonstrate that social centrality is hardly an indicator of the social influence in the decision-making process of "equality matching" types of groups. However, socially central group members and socially close groups are significantly happier with group decisions than those who are loosely related. Moreover, in this paper we show that social relationships are indicators of other concepts relevant in group settings, therefore in group recommender systems as well.
Mukherjee, Satyam; Huang, Yun; Neidhardt, Julia; Uzzi, Brian; Contractor, Noshir
Prior shared success predicts victory in team competitions Journal Article
In: Nature Human Behaviour, vol. 3, iss. 1, pp. 74–81, 2018, ISSN: 2397-3374.
@article{ 20.500.12708_145946,
title = {Prior shared success predicts victory in team competitions},
author = {Satyam Mukherjee and Yun Huang and Julia Neidhardt and Brian Uzzi and Noshir Contractor},
doi = {10.1038/s41562-018-0460-y},
issn = {2397-3374},
year = {2018},
date = {2018-01-01},
journal = {Nature Human Behaviour},
volume = {3},
issue = {1},
pages = {74–81},
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Neidhardt, Julia; Kuflik, Tsvi; Wörndl, Wolfgang
Special Section on Recommender Systems in Tourism Journal Article
In: Information Technology and Tourism, vol. 19, iss. 1-4, pp. 83–85, 2018, ISSN: 1098-3058.
@article{ 20.500.12708_145947,
title = {Special Section on Recommender Systems in Tourism},
author = {Julia Neidhardt and Tsvi Kuflik and Wolfgang Wörndl},
doi = {10.1007/s40558-018-0111-1},
issn = {1098-3058},
year = {2018},
date = {2018-01-01},
journal = {Information Technology and Tourism},
volume = {19},
issue = {1-4},
pages = {83–85},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Glatzer, Lisa; Neidhardt, Julia; Werthner, Hannes
Automated Assignment of Hotel Descriptions to Travel Behavioural Patterns Proceedings Article
In: Information and Communication Technologies in Tourism 2018, pp. 409–421, Springer, 2018, ISBN: 9783319729237.
@inproceedings{ 20.500.12708_57173,
title = {Automated Assignment of Hotel Descriptions to Travel Behavioural Patterns},
author = {Lisa Glatzer and Julia Neidhardt and Hannes Werthner},
doi = {10.1007/978-3-319-72923-7_31},
isbn = {9783319729237},
year = {2018},
date = {2018-01-01},
booktitle = {Information and Communication Technologies in Tourism 2018},
pages = {409–421},
publisher = {Springer},
abstract = {The amount of people using online platforms to book a travel accommodation has grown tremendously. Hence, tour operators implement recommender systems to offer most suitable hotels to their customers. In this paper, a method of using hotel descriptions for recommendation is introduced. Different natural language processing methods were applied to pre-process a corpus of hotel descriptions. Further, three machine learning approaches for the allocation of hotel descriptions to travel behavioural patterns were implemented: clustering, classification and a dictionary-based approach. The main results show that clustering cannot be used in this context since the algorithm mostly relies on the operator-dependent structure of the descriptions. Supervised classification achieves the highest precision for six travel patterns, whereas the dictionary approach works best for one pattern. In general, the results for the different travel patterns vary due to the unequally distributed data sets as well as various characteristics of the patterns.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The amount of people using online platforms to book a travel accommodation has grown tremendously. Hence, tour operators implement recommender systems to offer most suitable hotels to their customers. In this paper, a method of using hotel descriptions for recommendation is introduced. Different natural language processing methods were applied to pre-process a corpus of hotel descriptions. Further, three machine learning approaches for the allocation of hotel descriptions to travel behavioural patterns were implemented: clustering, classification and a dictionary-based approach. The main results show that clustering cannot be used in this context since the algorithm mostly relies on the operator-dependent structure of the descriptions. Supervised classification achieves the highest precision for six travel patterns, whereas the dictionary approach works best for one pattern. In general, the results for the different travel patterns vary due to the unequally distributed data sets as well as various characteristics of the patterns.
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.): Personalized Human-Computer Interaction., pp. 1–5, DeGruyter, Oldenbourg, 2018.
@incollection{ 20.500.12708_29973,
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},
year = {2018},
date = {2018-01-01},
booktitle = {Personalized Human-Computer Interaction.},
pages = {1–5},
publisher = {DeGruyter},
address = {Oldenbourg},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
2017
Delic, Amra; Neidhardt, Julia
A Comprehensive Approach to Group Recommendations in the Travel and Tourism Domain Proceedings Article
In: Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization, ACM, New York, 2017.
@inproceedings{ 20.500.12708_57166,
title = {A Comprehensive Approach to Group Recommendations in the Travel and Tourism Domain},
author = {Amra Delic and Julia Neidhardt},
doi = {10.1145/3099023.3099076},
year = {2017},
date = {2017-01-01},
booktitle = {Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization},
publisher = {ACM},
address = {New York},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Neidhardt, Julia; Fesenmaier, Daniel R.; Kuflik, Tsvi; Wörndl, Wolfgang
RecTour 2017 Proceedings Article
In: Proceedings of the Eleventh ACM Conference on Recommender Systems, ACM, New York, NY, USA, 2017.
@inproceedings{ 20.500.12708_57172,
title = {RecTour 2017},
author = {Julia Neidhardt and Daniel R. Fesenmaier and Tsvi Kuflik and Wolfgang Wörndl},
doi = {10.1145/3109859.3109962},
year = {2017},
date = {2017-01-01},
booktitle = {Proceedings of the Eleventh ACM Conference on Recommender Systems},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Grün, Christoph; Neidhardt, Julia; Werthner, Hannes
Ontology-based Matchmaking to Provide Personalized Recommendations for Tourists Proceedings Article
In: ENTER Conference, Information and Communication Technologies in Tourism 2017, pp. 3–14, Springer, 2017, ISBN: 978-3-319-51167-2.
@inproceedings{ 20.500.12708_56750,
title = {Ontology-based Matchmaking to Provide Personalized Recommendations for Tourists},
author = {Christoph Grün and Julia Neidhardt and Hannes Werthner},
isbn = {978-3-319-51167-2},
year = {2017},
date = {2017-01-01},
booktitle = {ENTER Conference, Information and Communication Technologies in Tourism 2017},
pages = {3–14},
publisher = {Springer},
abstract = {This paper addresses the challenges to support tourists in their decision-making during the pre-trip phase and to facilitate the process of identifying those tourism objects that best fit the tourists´ preferences. The latter directly depends on the quality of the matchmaking process, i.e. finding those tourism objects that are most attractive to a particular tourist. To achieve this goal, an innovative approach is introduced that matches tourist profiles with the characteristics of tourism objects in order to obtain a ranked list of appropriate objects for a particular tourist. The matchmaking process leverages tourist factors as a shortcut to propose a first user profile and related to this, a first set of tourism objects. User feedback is then used to dynamically adapt the tourist profile and thus refine the set of recommended objects. Our approach is tested through a prototypical recommender system that suggests tourists in Vienna attractions that are tailored to their personal needs. Furthermore, a user study is conducted by asking people to interact with the system and fill in a questionnaire afterwards.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper addresses the challenges to support tourists in their decision-making during the pre-trip phase and to facilitate the process of identifying those tourism objects that best fit the tourists´ preferences. The latter directly depends on the quality of the matchmaking process, i.e. finding those tourism objects that are most attractive to a particular tourist. To achieve this goal, an innovative approach is introduced that matches tourist profiles with the characteristics of tourism objects in order to obtain a ranked list of appropriate objects for a particular tourist. The matchmaking process leverages tourist factors as a shortcut to propose a first user profile and related to this, a first set of tourism objects. User feedback is then used to dynamically adapt the tourist profile and thus refine the set of recommended objects. Our approach is tested through a prototypical recommender system that suggests tourists in Vienna attractions that are tailored to their personal needs. Furthermore, a user study is conducted by asking people to interact with the system and fill in a questionnaire afterwards.
Neidhardt, Julia; Werthner, Hannes
Travellers and Their Joint Characteristics Within the Seven-Factor Model Proceedings Article
In: ENTER Conference, Information and Communication Technologies in Tourism 2017, pp. 503–515, Springer, 2017, ISBN: 978-3-319-51167-2.
@inproceedings{ 20.500.12708_56751,
title = {Travellers and Their Joint Characteristics Within the Seven-Factor Model},
author = {Julia Neidhardt and Hannes Werthner},
isbn = {978-3-319-51167-2},
year = {2017},
date = {2017-01-01},
booktitle = {ENTER Conference, Information and Communication Technologies in Tourism 2017},
pages = {503–515},
publisher = {Springer},
abstract = {Recommender systems face specific challenges in the travel domain, as the tourism product is typically very complex. In addition, travelling can be seen as an emotional experience. Thus travel decisions are usually not only based on rational criteria but are rather implicitly given. Therefore sophisticated user models are required. In this paper it is analysed in detail whether the seven-factor model is capable of differentiating between different groups of users in an accurate way. Within this model each user is described with respect to seven travel behavioural patterns that account for both tourist roles and personality traits of a user. To identify groups of travellers, individual attributes are used and also a cluster analysis is conducted. With the help of statistical analyses clear evidence is provided that the seven-factor model is capable of distinguishing between different groups of users in a meaningful and effective way.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Recommender systems face specific challenges in the travel domain, as the tourism product is typically very complex. In addition, travelling can be seen as an emotional experience. Thus travel decisions are usually not only based on rational criteria but are rather implicitly given. Therefore sophisticated user models are required. In this paper it is analysed in detail whether the seven-factor model is capable of differentiating between different groups of users in an accurate way. Within this model each user is described with respect to seven travel behavioural patterns that account for both tourist roles and personality traits of a user. To identify groups of travellers, individual attributes are used and also a cluster analysis is conducted. With the help of statistical analyses clear evidence is provided that the seven-factor model is capable of distinguishing between different groups of users in a meaningful and effective way.
Delic, Amra; Neidhardt, Julia; Rook, Laurens; Werthner, Hannes; Zanker, Markus
Researching Individual Satisfaction with Group Decisions in Tourism: Experimental Evidence Proceedings Article
In: ENTER Conference, Information and Communication Technologies in Tourism 2017, pp. 73–84, Springer, 2017, ISBN: 978-3-319-51167-2.
@inproceedings{ 20.500.12708_56749b,
title = {Researching Individual Satisfaction with Group Decisions in Tourism: Experimental Evidence},
author = {Amra Delic and Julia Neidhardt and Laurens Rook and Hannes Werthner and Markus Zanker},
isbn = {978-3-319-51167-2},
year = {2017},
date = {2017-01-01},
booktitle = {ENTER Conference, Information and Communication Technologies in Tourism 2017},
pages = {73–84},
publisher = {Springer},
abstract = {The goal of the present study was to investigate how satisfied individuals are with the final outcome of a group decision-making process on a joint travel destination. Using an experimental paradigm (N_total= 200},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The goal of the present study was to investigate how satisfied individuals are with the final outcome of a group decision-making process on a joint travel destination. Using an experimental paradigm (N_total= 200
Neidhardt, Julia; Rümmele, Nataliia; Werthner, Hannes
Predicting happiness: user interactions and sentiment analysis in an online travel forum Journal Article
In: Information Technology & Tourism, vol. 17, iss. 1, pp. 101–119, 2017, ISSN: 1098-3058.
@article{ 20.500.12708_50b,
title = {Predicting happiness: user interactions and sentiment analysis in an online travel forum},
author = {Julia Neidhardt and Nataliia Rümmele and Hannes Werthner},
doi = {10.1007/s40558-017-0079-2},
issn = {1098-3058},
year = {2017},
date = {2017-01-01},
journal = {Information Technology & Tourism},
volume = {17},
issue = {1},
pages = {101–119},
publisher = {SPRINGER HEIDELBERG},
abstract = {Web sources of tourism services provide valuable resources of knowledge not only for the travellers but also for the companies. Tourism operators are increasingly aware that user related data should be regarded as an important asset. Furthermore, as data is permanently generated and always available, the landscape of empirical research is changing. In this paper, user activities and interactions in the tourism domain are analysed. In particular, the emotions of the users regarding their forthcoming trips are studied with the objective to characterize interdependencies between them. Social network analysis is applied to examine interactions between the users. To capture their emotions, text mining techniques and sentiment analysis are applied to construct a measure, which is based on free-text comments in a travel forum. The experimental outcome provides some evidence that the network has an effect on the sentiment of the users.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Web sources of tourism services provide valuable resources of knowledge not only for the travellers but also for the companies. Tourism operators are increasingly aware that user related data should be regarded as an important asset. Furthermore, as data is permanently generated and always available, the landscape of empirical research is changing. In this paper, user activities and interactions in the tourism domain are analysed. In particular, the emotions of the users regarding their forthcoming trips are studied with the objective to characterize interdependencies between them. Social network analysis is applied to examine interactions between the users. To capture their emotions, text mining techniques and sentiment analysis are applied to construct a measure, which is based on free-text comments in a travel forum. The experimental outcome provides some evidence that the network has an effect on the sentiment of the users.
Grill, Gabriel; Neidhardt, Julia; Werthner, Hannes
Network Analysis on the Austrian Media Corpus Proceedings Article
In: VSS 2017 – Vienna young Scientists Symposium, pp. 128–129, Book-of-Abstracts.com, Heinz A. Krebs, Gumpoldskirchen, Austria, 2017, ISBN: 978-3-9504017-2-1.
@inproceedings{ 20.500.12708_57171b,
title = {Network Analysis on the Austrian Media Corpus},
author = {Gabriel Grill and Julia Neidhardt and Hannes Werthner},
isbn = {978-3-9504017-2-1},
year = {2017},
date = {2017-01-01},
booktitle = {VSS 2017 - Vienna young Scientists Symposium},
pages = {128–129},
publisher = {Book-of-Abstracts.com},
address = {Heinz A. Krebs, Gumpoldskirchen, Austria},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2016
Neidhardt, Julia; Rümmele, Nataliia; Werthner, Hannes
Can We Predict Your Sentiments by Listening to Your Peers? Proceedings Article
In: Information and Communication Technologies in Tourism 2016, pp. 593–603, Springer International Publishing, Part V, 2016, ISBN: 9783319282312.
@inproceedings{ 20.500.12708_56265,
title = {Can We Predict Your Sentiments by Listening to Your Peers?},
author = {Julia Neidhardt and Nataliia Rümmele and Hannes Werthner},
doi = {10.1007/978-3-319-28231-2_43},
isbn = {9783319282312},
year = {2016},
date = {2016-01-01},
booktitle = {Information and Communication Technologies in Tourism 2016},
pages = {593–603},
publisher = {Springer International Publishing},
address = {Part V},
abstract = {Web sources of tourism services provide valuable resources of knowledge not only for the travellers but also for the companies. Tourism operators are increasingly aware that user related data should be regarded as an important asset. In this paper, user activities and interactions in the tourism domain are analysed. In particular, the emotions of the users regarding their forthcoming trips are studied with the objective to characterize interdependencies between them. Social network analysis is applied to characterize the interactions between the users. To capture their emotions, text mining techniques and sentiment analysis are applied to construct a measure, which is based on free-text comments in a travel forum. The experimental outcome provides some evidence that social influence between the users in the network exists.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Web sources of tourism services provide valuable resources of knowledge not only for the travellers but also for the companies. Tourism operators are increasingly aware that user related data should be regarded as an important asset. In this paper, user activities and interactions in the tourism domain are analysed. In particular, the emotions of the users regarding their forthcoming trips are studied with the objective to characterize interdependencies between them. Social network analysis is applied to characterize the interactions between the users. To capture their emotions, text mining techniques and sentiment analysis are applied to construct a measure, which is based on free-text comments in a travel forum. The experimental outcome provides some evidence that social influence between the users in the network exists.
Delic, Amra; Neidhardt, Julia; Werthner, Hannes
Are Sun Lovers nervous? Proceedings Article
In: ENTER 2016: Volume 7, e-Review of Tourism Research, 2016.
@inproceedings{ 20.500.12708_56264,
title = {Are Sun Lovers nervous?},
author = {Amra Delic and Julia Neidhardt and Hannes Werthner},
year = {2016},
date = {2016-01-01},
booktitle = {ENTER 2016: Volume 7},
publisher = {e-Review of Tourism Research},
abstract = {In the travel and tourism industry, understanding travellers´ behaviours, needs and interests is one of the major issues to be tackled in order to provide excellent services. During the last decades, enormous efforts have been made in numerous studies with regard to the topic. The goal of this paper is to contribute to this knowledge base, trying to find relationships between tourist roles and long term personality traits. The paper is based on the 17 tourist roles defined by Gibson and Yiannakis (2002) and the Big Five Factors describing personality traits. Data collection was done using a 50 items questionnaire capturing basic demographic data, travel preferences and personality traits of nearly 1.000 respondents. The analysis shows interesting results, especially when taking different age ranges into consideration.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In the travel and tourism industry, understanding travellers´ behaviours, needs and interests is one of the major issues to be tackled in order to provide excellent services. During the last decades, enormous efforts have been made in numerous studies with regard to the topic. The goal of this paper is to contribute to this knowledge base, trying to find relationships between tourist roles and long term personality traits. The paper is based on the 17 tourist roles defined by Gibson and Yiannakis (2002) and the Big Five Factors describing personality traits. Data collection was done using a 50 items questionnaire capturing basic demographic data, travel preferences and personality traits of nearly 1.000 respondents. The analysis shows interesting results, especially when taking different age ranges into consideration.
Fesenmaier, Daniel R.; Kuflik, Tsvi; Neidhardt, Julia
RecTour 2016 Proceedings Article
In: Proceedings of the 10th ACM Conference on Recommender Systems, ACM, 2016.
@inproceedings{ 20.500.12708_56513,
title = {RecTour 2016},
author = {Daniel R. Fesenmaier and Tsvi Kuflik and Julia Neidhardt},
doi = {10.1145/2959100.2959205},
year = {2016},
date = {2016-01-01},
booktitle = {Proceedings of the 10th ACM Conference on Recommender Systems},
publisher = {ACM},
abstract = {In this paper, we summarize RecTour 2016 – a workshop on recommenders in tourism co-located with RecSys 2016. There was a great variety of submissions, i.e., research papers, demo papers and position papers, addressing fundamental challenges of recommender systems in the tourism domain. The main topics included group recommendations, context-aware recommenders, choice-based recommenders and event recommendations.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we summarize RecTour 2016 – a workshop on recommenders in tourism co-located with RecSys 2016. There was a great variety of submissions, i.e., research papers, demo papers and position papers, addressing fundamental challenges of recommender systems in the tourism domain. The main topics included group recommendations, context-aware recommenders, choice-based recommenders and event recommendations.
Delic, Amra; Neidhardt, Julia; Nguyen, Thuy Ngoc; Ricci, Francesco; Rook, Laurens; Werthner, Hannes; Zanker, Markus
Observing Group Decision Making Processes Proceedings Article
In: Proceedings of the 10th ACM Conference on Recommender Systems, ACM, 2016.
@inproceedings{ 20.500.12708_56514,
title = {Observing Group Decision Making Processes},
author = {Amra Delic and Julia Neidhardt and Thuy Ngoc Nguyen and Francesco Ricci and Laurens Rook and Hannes Werthner and Markus Zanker},
doi = {10.1145/2959100.2959168},
year = {2016},
date = {2016-01-01},
booktitle = {Proceedings of the 10th ACM Conference on Recommender Systems},
publisher = {ACM},
abstract = {Most research on group recommender systems relies on the assumption that individuals have conflicting preferences; in order to generate group recommendations the system should identify a fair way of aggregating these preferences. Both empirical studies and theoretical frameworks have tried to identify the most effective preference aggregation techniques without coming to definite conclusions. In this paper, we propose to approach group recommendation from the group dynamics perspective and analyze the group decision making process for a particular task (in the travel domain). We observe several individual and group properties and correlate them to choice satisfaction. Supported by these initial results we therefore advocate for the development of new group recommendation techniques that consider group dynamics and support the full group decision making process.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Most research on group recommender systems relies on the assumption that individuals have conflicting preferences; in order to generate group recommendations the system should identify a fair way of aggregating these preferences. Both empirical studies and theoretical frameworks have tried to identify the most effective preference aggregation techniques without coming to definite conclusions. In this paper, we propose to approach group recommendation from the group dynamics perspective and analyze the group decision making process for a particular task (in the travel domain). We observe several individual and group properties and correlate them to choice satisfaction. Supported by these initial results we therefore advocate for the development of new group recommendation techniques that consider group dynamics and support the full group decision making process.
Delic, Amra; Neidhardt, Julia; Nguyen, Thuy Ngoc; Ricci, Francesco
Research Methods for Group Recommender Systems Proceedings Article
In: Proceedings of the Workshop on Recommenders in Tourism (RecTour 2016), pp. 30–37, CEUR-WS.org, Boston, MA, USA, 2016.
@inproceedings{ 20.500.12708_56515,
title = {Research Methods for Group Recommender Systems},
author = {Amra Delic and Julia Neidhardt and Thuy Ngoc Nguyen and Francesco Ricci},
year = {2016},
date = {2016-01-01},
booktitle = {Proceedings of the Workshop on Recommenders in Tourism (RecTour 2016)},
pages = {30–37},
publisher = {CEUR-WS.org},
address = {Boston, MA, USA},
series = {CEUR Workshop Proceedings},
abstract = {In this article we argue that the research on group recom- mender systems must look more carefully at group dynamics in decision making in order to produce technologies that will be truly beneficial for users. Hence, we illustrate a user study method aimed at observing and measuring the evolution of user preferences and actions in a tourism decision making task: finding a destination to visit. We discuss the benefits and caveats of such an observational study method and we present the implications that the derived data and findings may have on the design of interactive group recommender systems.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In this article we argue that the research on group recom- mender systems must look more carefully at group dynamics in decision making in order to produce technologies that will be truly beneficial for users. Hence, we illustrate a user study method aimed at observing and measuring the evolution of user preferences and actions in a tourism decision making task: finding a destination to visit. We discuss the benefits and caveats of such an observational study method and we present the implications that the derived data and findings may have on the design of interactive group recommender systems.
Neidhardt, Julia
Modeling and understanding social influence in groups and networks PhD Thesis
Technische Universität Wien, 2016.
@phdthesis{ 20.500.12708_2430,
title = {Modeling and understanding social influence in groups and networks},
author = {Julia Neidhardt},
doi = {10.34726/hss.2016.37208},
year = {2016},
date = {2016-01-01},
address = {Wien},
school = {Technische Universität Wien},
abstract = {Social influence occurs when a person changes her behavior according to the behavior of other people in the social system. Today, these complex mechanisms can be studied by making use of vast amounts of detailed data on human behavior and social interactions, coming from the World Wide Web and other data sources. The main objective of this work is to capture social influence processes in computational models on a large scale. In the presented analysis, three levels of information are distinguished (i.e., individual, group and network level). To illustrate each level in detail and to show their differences, conventional methods and their shortcomings are discussed and empirical studies are conducted. At the individual level, regression models are applied; at the group level, approaches based on geometric data analysis; and at the network level, social network analysis. At the network level, conditional random field models are introduced as an alternative way to capture social influence processes. Finally, it is discussed, how all three levels can be integrated into one model. The empirical analyses are related to travel recommender systems, churn behavior, sentiments in online forums and team-vs-team competitions. The results of this belong to two categories: 1) methodological advances; 2) concrete statements in different domains of application. It is shown that the introduced models are able to capture social context in an accurate way. Most of them, moreover, scale well. Furthermore, integrating different levels of information allows comparing them and their associations with the studied social influence processes directly. Thus, a more comprehensive picture of the respective domain of application is obtained.},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
Social influence occurs when a person changes her behavior according to the behavior of other people in the social system. Today, these complex mechanisms can be studied by making use of vast amounts of detailed data on human behavior and social interactions, coming from the World Wide Web and other data sources. The main objective of this work is to capture social influence processes in computational models on a large scale. In the presented analysis, three levels of information are distinguished (i.e., individual, group and network level). To illustrate each level in detail and to show their differences, conventional methods and their shortcomings are discussed and empirical studies are conducted. At the individual level, regression models are applied; at the group level, approaches based on geometric data analysis; and at the network level, social network analysis. At the network level, conditional random field models are introduced as an alternative way to capture social influence processes. Finally, it is discussed, how all three levels can be integrated into one model. The empirical analyses are related to travel recommender systems, churn behavior, sentiments in online forums and team-vs-team competitions. The results of this belong to two categories: 1) methodological advances; 2) concrete statements in different domains of application. It is shown that the introduced models are able to capture social context in an accurate way. Most of them, moreover, scale well. Furthermore, integrating different levels of information allows comparing them and their associations with the studied social influence processes directly. Thus, a more comprehensive picture of the respective domain of application is obtained.
2015
Neidhardt, Julia; Huang, Yun; Werthner, Hannes; Contractor, Noshir
Conditional Random Field Models as a Way to Capture Peer Influence in Social Networks Miscellaneous
2015.
@misc{ 20.500.12708_86041,
title = {Conditional Random Field Models as a Way to Capture Peer Influence in Social Networks},
author = {Julia Neidhardt and Yun Huang and Hannes Werthner and Noshir Contractor},
year = {2015},
date = {2015-01-01},
abstract = {Peer influence occurs when individuals adapt their behavior according to the behavior of their friends. When studying human interactions and the spreading of beliefs, feelings or behaviors, influence mechanisms typically play a decisive role. If there are multiple observations of network and behavior, temporal models such as SIENA can identify social influence based on behavioral change at different time points. However, in cross-sectional cases, where only one observation of the network is available, studying and predicting individual behavior while controlling for social influence is very challenging both statistically and computationally. In this work, we propose using Conditional Random Field (CRF) logistic regression for modeling peer influence in cross-sectional settings and compare it with existing methods such as Autologistic Actor Attribute Models (ALAAM).
We use data about teenage smoking behavior from previous social influence studies to evaluate our approach. The results of CRF models are consistent with ALAAM. CRF produces accurate coefficient estimations compared to the over-estimation in ordinary logistic regression models. For example, after controlling for contagion effects, gender has no significant impact on smoking; but it has in the ordinary logistics regression. Similarly, drinking alcohol, smoking siblings, and being in a romantic relationship have smaller effect sizes when social influence effects are taken into account. This study shows that CRF models are capable of modeling individual behavior with peer influence and are both computationally efficient and scalable for large networks. Moreover, the extension of the CRF models can characterize different types of peer influence such as contagion and similarity and can model dynamic behavior in a network.},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Peer influence occurs when individuals adapt their behavior according to the behavior of their friends. When studying human interactions and the spreading of beliefs, feelings or behaviors, influence mechanisms typically play a decisive role. If there are multiple observations of network and behavior, temporal models such as SIENA can identify social influence based on behavioral change at different time points. However, in cross-sectional cases, where only one observation of the network is available, studying and predicting individual behavior while controlling for social influence is very challenging both statistically and computationally. In this work, we propose using Conditional Random Field (CRF) logistic regression for modeling peer influence in cross-sectional settings and compare it with existing methods such as Autologistic Actor Attribute Models (ALAAM).
We use data about teenage smoking behavior from previous social influence studies to evaluate our approach. The results of CRF models are consistent with ALAAM. CRF produces accurate coefficient estimations compared to the over-estimation in ordinary logistic regression models. For example, after controlling for contagion effects, gender has no significant impact on smoking; but it has in the ordinary logistics regression. Similarly, drinking alcohol, smoking siblings, and being in a romantic relationship have smaller effect sizes when social influence effects are taken into account. This study shows that CRF models are capable of modeling individual behavior with peer influence and are both computationally efficient and scalable for large networks. Moreover, the extension of the CRF models can characterize different types of peer influence such as contagion and similarity and can model dynamic behavior in a network.
We use data about teenage smoking behavior from previous social influence studies to evaluate our approach. The results of CRF models are consistent with ALAAM. CRF produces accurate coefficient estimations compared to the over-estimation in ordinary logistic regression models. For example, after controlling for contagion effects, gender has no significant impact on smoking; but it has in the ordinary logistics regression. Similarly, drinking alcohol, smoking siblings, and being in a romantic relationship have smaller effect sizes when social influence effects are taken into account. This study shows that CRF models are capable of modeling individual behavior with peer influence and are both computationally efficient and scalable for large networks. Moreover, the extension of the CRF models can characterize different types of peer influence such as contagion and similarity and can model dynamic behavior in a network.
Neidhardt, Julia
Computational Analyses of Network Data Miscellaneous
2015.
@misc{ 20.500.12708_86175,
title = {Computational Analyses of Network Data},
author = {Julia Neidhardt},
year = {2015},
date = {2015-01-01},
abstract = {In this sessions we will use software tools to analyze network data, where we will give equal emphasis to conducting the analysis and interpreting the results.},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
In this sessions we will use software tools to analyze network data, where we will give equal emphasis to conducting the analysis and interpreting the results.
Huang, Yun; Neidhardt, Julia
From Networks to Space: Constructing Metric Spaces for Social Interactions Miscellaneous
2015.
@misc{ 20.500.12708_86174,
title = {From Networks to Space: Constructing Metric Spaces for Social Interactions},
author = {Yun Huang and Julia Neidhardt},
year = {2015},
date = {2015-01-01},
abstract = {Bourdieu's field theory provides a relational framework to bridge objective ties and subjective relations in complex social systems. The important concepts, field and habitus, provide essential building blocks for constructing social space. Although field theory has been tested in various social settings, many of them only focused on a particular domain of social structures and avoid the nature of multi-dimension and overlapping social structures. On the other hand, the advancement of online social communities such as Facebook and online games has generated enormous network data reflecting the underlying social interactions. This provides opportunities and challenges to study intertwisted social groups and illustrate the structure of a much larger social space.
Despite of his advocacy of relational thinking, Bourdieu argued against social network analysis based on individual exchange and interactions and preferred correspondence analysis, which concentrates on objective relations, e.g. differential levels of capitals. Nooy (2003) compared correspondence analysis and social network analysis and argued that objective relations and individual interactions have a mutual influence and both should be used for identifying people's positions in the social space. The recent progress of Exponential Random Graph Models (ERGM/p*) provides advanced techniques to extract underlying formation mechanisms (part of habitus) from the structures of observed individual interactions. However, due to the computational complexity, this method is only feasible for small and homogenous networks.
In this paper, we propose hyperbolic embedding as a nexus to integrate field theory and social network analysis. As type of geometric data analysis, hyperbolic embedding supports more flexible dimensional reduction and eliminates the 2D restriction of correspondence analysis; meanwhile, as Krioukov et al. (2010) have shown, hyperbolic embedding also characterizes the link probability in exponential random graphs. Therefore, we obtain a unified metric space (through hyperbolic embedding) that represents an ensemble of capitals and interactions and provides positions of individuals and their distances in social space. To demonstrate the method, we use large networks from online communities such as Second Life to construct a metric space of hundreds of thousands people and illustrate the shapes of local structures.},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Bourdieu’s field theory provides a relational framework to bridge objective ties and subjective relations in complex social systems. The important concepts, field and habitus, provide essential building blocks for constructing social space. Although field theory has been tested in various social settings, many of them only focused on a particular domain of social structures and avoid the nature of multi-dimension and overlapping social structures. On the other hand, the advancement of online social communities such as Facebook and online games has generated enormous network data reflecting the underlying social interactions. This provides opportunities and challenges to study intertwisted social groups and illustrate the structure of a much larger social space.
Despite of his advocacy of relational thinking, Bourdieu argued against social network analysis based on individual exchange and interactions and preferred correspondence analysis, which concentrates on objective relations, e.g. differential levels of capitals. Nooy (2003) compared correspondence analysis and social network analysis and argued that objective relations and individual interactions have a mutual influence and both should be used for identifying people’s positions in the social space. The recent progress of Exponential Random Graph Models (ERGM/p*) provides advanced techniques to extract underlying formation mechanisms (part of habitus) from the structures of observed individual interactions. However, due to the computational complexity, this method is only feasible for small and homogenous networks.
In this paper, we propose hyperbolic embedding as a nexus to integrate field theory and social network analysis. As type of geometric data analysis, hyperbolic embedding supports more flexible dimensional reduction and eliminates the 2D restriction of correspondence analysis; meanwhile, as Krioukov et al. (2010) have shown, hyperbolic embedding also characterizes the link probability in exponential random graphs. Therefore, we obtain a unified metric space (through hyperbolic embedding) that represents an ensemble of capitals and interactions and provides positions of individuals and their distances in social space. To demonstrate the method, we use large networks from online communities such as Second Life to construct a metric space of hundreds of thousands people and illustrate the shapes of local structures.
Despite of his advocacy of relational thinking, Bourdieu argued against social network analysis based on individual exchange and interactions and preferred correspondence analysis, which concentrates on objective relations, e.g. differential levels of capitals. Nooy (2003) compared correspondence analysis and social network analysis and argued that objective relations and individual interactions have a mutual influence and both should be used for identifying people’s positions in the social space. The recent progress of Exponential Random Graph Models (ERGM/p*) provides advanced techniques to extract underlying formation mechanisms (part of habitus) from the structures of observed individual interactions. However, due to the computational complexity, this method is only feasible for small and homogenous networks.
In this paper, we propose hyperbolic embedding as a nexus to integrate field theory and social network analysis. As type of geometric data analysis, hyperbolic embedding supports more flexible dimensional reduction and eliminates the 2D restriction of correspondence analysis; meanwhile, as Krioukov et al. (2010) have shown, hyperbolic embedding also characterizes the link probability in exponential random graphs. Therefore, we obtain a unified metric space (through hyperbolic embedding) that represents an ensemble of capitals and interactions and provides positions of individuals and their distances in social space. To demonstrate the method, we use large networks from online communities such as Second Life to construct a metric space of hundreds of thousands people and illustrate the shapes of local structures.
Neidhardt, Julia; Huang, Yun; Contractor, Noshir
Team vs. Team: Success Factors in a Multiplayer Online Battle Arena Game Proceedings Article
In: Academy of Management Proceedings, 2015.
@inproceedings{ 20.500.12708_56043,
title = {Team vs. Team: Success Factors in a Multiplayer Online Battle Arena Game},
author = {Julia Neidhardt and Yun Huang and Noshir Contractor},
doi = {10.5465/ambpp.2015.18725abstract},
year = {2015},
date = {2015-01-01},
booktitle = {Academy of Management Proceedings},
series = {Academy of Management Proceedings},
abstract = {There is a small but growing body of research investigating how teams form and how that affects how they perform. Much of that research focuses on teams that seek to accomplish certain tasks such as writing an article or perform a Broadway musical. There has been much less investigation of the relative performance of teams that form to directly compete against another team. In this study, we report on team-vs-team competitions in the multiplayer online game Dota 2. Here, the teams´ overall goal is to beat the opponent. We use this unique setting to observe multi-level factors influence the relative performance of the teams. Those factors include compositional factors or attributes of the individuals comprising a team, relational factors or prior relations among individuals within a team and ecosystem factors or overlapping prior membership of team members with others within the ecosystem of teams. We also study how these multilevel factors affect the duration of a match. Our results show that advantages at the compositional, relational and ecosystem levels predict which team will succeed in short or medium duration matches. Relational and ecosystem factors are particularly helpful in predicting the winner in short duration matches, whereas compositional factors are more important predicting winners in medium duration matches. However, the two types of relations have opposite effects on the duration of winning. None of the three multilevel factors help explain which team will win in long matches.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
There is a small but growing body of research investigating how teams form and how that affects how they perform. Much of that research focuses on teams that seek to accomplish certain tasks such as writing an article or perform a Broadway musical. There has been much less investigation of the relative performance of teams that form to directly compete against another team. In this study, we report on team-vs-team competitions in the multiplayer online game Dota 2. Here, the teams´ overall goal is to beat the opponent. We use this unique setting to observe multi-level factors influence the relative performance of the teams. Those factors include compositional factors or attributes of the individuals comprising a team, relational factors or prior relations among individuals within a team and ecosystem factors or overlapping prior membership of team members with others within the ecosystem of teams. We also study how these multilevel factors affect the duration of a match. Our results show that advantages at the compositional, relational and ecosystem levels predict which team will succeed in short or medium duration matches. Relational and ecosystem factors are particularly helpful in predicting the winner in short duration matches, whereas compositional factors are more important predicting winners in medium duration matches. However, the two types of relations have opposite effects on the duration of winning. None of the three multilevel factors help explain which team will win in long matches.
Sacharidis, Dimitrios; Delic, Amra; Neidhardt, Julia
Learning the Role and Behavior of Users in Group Decision Making Proceedings Article
In: Mouzhi, Ge; Ricci, Francesco (Ed.): Proceedings of the 2nd International Workshop on Decision Making and Recommender Systems, pp. 25–28, CEUR-WS.org, 2015.
@inproceedings{ 20.500.12708_56269,
title = {Learning the Role and Behavior of Users in Group Decision Making},
author = {Dimitrios Sacharidis and Amra Delic and Julia Neidhardt},
editor = {Ge Mouzhi and Francesco Ricci},
year = {2015},
date = {2015-01-01},
booktitle = {Proceedings of the 2nd International Workshop on Decision Making and Recommender Systems},
pages = {25–28},
publisher = {CEUR-WS.org},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Neidhardt, Julia; Pobiedina, Nataliia; Werthner, Hannes
What Can We Learn From Review Data? Proceedings Article
In: ENTER 2015: Volume 6, e-Review of Tourism Research, 2015.
@inproceedings{ 20.500.12708_55856,
title = {What Can We Learn From Review Data?},
author = {Julia Neidhardt and Nataliia Pobiedina and Hannes Werthner},
year = {2015},
date = {2015-01-01},
booktitle = {ENTER 2015: Volume 6},
publisher = {e-Review of Tourism Research},
abstract = {Online reviews of tourism services provide valuable resources of knowledge not only for travelers but also for companies. Tourism operators are more and more aware that user related data should be seen as an important asset. This work-in-progress analyzes free text reviews as well as numerical ratings of group tours with the aim to characterize their relations. This is done with the help of statistical models. On the one hand, these models comprise textual attributes and sentiment scores of the reviews, based on text mining techniques and sentiment analysis respectively. On the other hand, non-textual attributes such as meta data about the tours and user related factors are included. First results imply a very moderate relationship between sentiment scores and ratings; the non-textual attributes appear to have a higher impact.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Online reviews of tourism services provide valuable resources of knowledge not only for travelers but also for companies. Tourism operators are more and more aware that user related data should be seen as an important asset. This work-in-progress analyzes free text reviews as well as numerical ratings of group tours with the aim to characterize their relations. This is done with the help of statistical models. On the one hand, these models comprise textual attributes and sentiment scores of the reviews, based on text mining techniques and sentiment analysis respectively. On the other hand, non-textual attributes such as meta data about the tours and user related factors are included. First results imply a very moderate relationship between sentiment scores and ratings; the non-textual attributes appear to have a higher impact.