Publications
Explore the publications from our RecSys laboratory.
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.},
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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.},
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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.},
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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}
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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.},
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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.},
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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}
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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.},
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Krathu, Worarat; Pichler, Christian; Xiao, Guohui; Werthner, Hannes; Neidhardt, Julia; Zapletal, Marco; Huemer, Christian
Inter-organizational success factors: a cause and effect model Journal Article
In: Information Systems and E-Business Management, vol. 13, iss. 3, pp. 553–593, 2015, ISSN: 1617-9846.
@article{20.500.12708_150395,
title = {Inter-organizational success factors: a cause and effect model},
author = {Worarat Krathu and Christian Pichler and Guohui Xiao and Hannes Werthner and Julia Neidhardt and Marco Zapletal and Christian Huemer},
doi = {10.1007/s10257-014-0258-z},
issn = {1617-9846},
year = {2015},
date = {2015-01-01},
journal = {Information Systems and E-Business Management},
volume = {13},
issue = {3},
pages = {553–593},
abstract = {Inter-organizational systems form the basis for successful business collaboration in the Internet and B2B e-commerce era. To properly design and manage such systems one needs to understand the structure and dynamics of the relationships between organizations. The evaluation of such inter-organizational relationships (IORs) is normally conducted using "success factors". These are often referred to as constructs, such as trust and information sharing. In strategic management and performance analysis, different methods are employed for evaluating business performance and strategies, such as the Balanced Scorecard (BSC) method. The BSC utilizes success factors for measuring and monitoring IORs against business strategies. For these reasons, a thorough understanding of success factors, the relationships between them, as well as their relationship to business strategies is required. In other words, understanding success factors allows strategists deriving measurements for success factors as well as aligning these success factors with business strategies. This underpins nowadays close relationship between business strategy, IORs and their realization by means of inter-organizational systems. In this paper, we present (1) a systematic literature review studying success factors and their impact on IORs as well as (2) an analysis of the results found. The review is based on 177 publications, published between 2000 and 2012, dealing with factors influencing IORs. The work presented provides an overview on success factors, influencing relationships between success factors, as well as their influence on the success of IORs. The work is somehow "meta-empirical" as it only looks at published studies and not on own cases. Consequently, it is based on the assumption that studies in scientific literature represent the real-world. The constructs and relationships found in the review are grouped based on their scope and summarized in a cause and effect model. The grouping of constructs results in five groups including Relationship Orientation, Relational Norm, Relational Capital, Atmosphere, and Others. Since the cause and effect model represents a directed graph, different network analysis methods may be applied for analyzing the model. In particular, an in- and out-degree analysis is applied on the cause and effect model for detecting the most influencing as well as the most influenced success factors.},
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Neidhardt, Julia; Seyfang, Leonhard; Schuster, Rainer; Werthner, Hannes
A picture-based approach to recommender systems Journal Article
In: Information Technology and Tourism, vol. 15, iss. 1, pp. 49–69, 2015, ISSN: 1098-3058.
@article{20.500.12708_150411,
title = {A picture-based approach to recommender systems},
author = {Julia Neidhardt and Leonhard Seyfang and Rainer Schuster and Hannes Werthner},
doi = {10.1007/s40558-014-0017-5},
issn = {1098-3058},
year = {2015},
date = {2015-01-01},
journal = {Information Technology and Tourism},
volume = {15},
issue = {1},
pages = {49–69},
abstract = {Due to their complexity, tourism products present major challenges to recommender techniques. Especially the assessment of customer preferences in order to get accurate user profiles is a non-trivial task for several reasons: (a) tourism is an "emotional" experience, which is typically hard to capture by using rational terms; (b) particularly in early phases of a travel decision process, users are not able to explicitly express their preferences; (c) and they are often lacking domain knowledge and thus have difficulties to use the right terminology. In this paper we introduce an alternative, i.e., a picture-based approach, as a new method to implicitly elicit user preferences for tourism products. We develop a model in which a user's travel profile is composed of seven basic factors. The scores of these factors are determined by asking the user to select a number of pictures that are appealing to him or her. The model as well as its implementation into a recommender system are described in detail. First evaluations show that interactions with the system are perceived as inspiring and enjoyable.},
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Werthner, Hannes; Alzua-Sorzabal, Aurkene; Cantoni, Lorenzo; Dickinger, Astrid; Gretzel, Ulrike; Jannach, Dietmar; Neidhardt, Julia; Pröll, Birgit; Ricci, Francesco; Scaglione, Miriam; Stangl, Brigitte; Stock, Oliviero; Zanker, Markus
Future research issues in IT and tourism Journal Article
In: Information Technology and Tourism, vol. 15, iss. 1, pp. 1–15, 2015, ISSN: 1098-3058.
@article{20.500.12708_150597,
title = {Future research issues in IT and tourism},
author = {Hannes Werthner and Aurkene Alzua-Sorzabal and Lorenzo Cantoni and Astrid Dickinger and Ulrike Gretzel and Dietmar Jannach and Julia Neidhardt and Birgit Pröll and Francesco Ricci and Miriam Scaglione and Brigitte Stangl and Oliviero Stock and Markus Zanker},
doi = {10.1007/s40558-014-0021-9},
issn = {1098-3058},
year = {2015},
date = {2015-01-01},
journal = {Information Technology and Tourism},
volume = {15},
issue = {1},
pages = {1–15},
abstract = {The objective of this manifesto (as a result of the JITT workshop in June 2014) is to identify a list of pivotal research topics and issues in e-tourism. E-tourism can be seen as everything that happens electronically in the travel and tourism industry/experience; more formally it is defined as the design, implementation and application of IT and ecommerce solutions in the travel and tourism industry as well as the analysis of the impact of the respective technical and economic processes and market structures on all the involved actors and especially on the traveller's experience. In tourism as an "information business", Information Technology has always played an important role since the 1960s with the computerized reservation systems/global distribution systems; these were one of the first world-wide electronic networks. And since the beginning of the Web in the early 1990s, travel and tourism was and is a major application domain for Web-based services. As such, the domain is also a major driver of technological innovation. This manifesto provides guidelines on strategic research issues for the research community, but as such it is also conceived as a basis document for industry and policy makers.},
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Neidhardt, Julia; Huang, Yun; Contractor, Noshir
Assembly factors influencing the victor in head to head short duration team competitions in a multiplayer online battle arena game Miscellaneous
2014.
@misc{20.500.12708_86039,
title = {Assembly factors influencing the victor in head to head short duration team competitions in a multiplayer online battle arena game},
author = {Julia Neidhardt and Yun Huang and Noshir Contractor},
year = {2014},
date = {2014-01-01},
abstract = {While network approaches are used to study how the assembly of teams impacts their performance, there has been little attention to assess the impact of assembly on the relative performance of two teams in a head to head contest. In this study, we focus specifically on assembly factors that influence performance of short-duration contests in mid-sized teams playing a multiplayer online game, Dota2, where two teams consisting of five players each compete with each other. We determine the attributes and relational factors that help a team to defeat the opponent. Among attributes we consider the players' skills as well as the diversity of their skills and their roles. Relational factors include the friendship relations within a team, previous co-playing experiences of team members as well as the embeddedness of the team, i.e., whether team members belong to a community that often plays together. We use game log data for short matches (under 30 minutes) within Dota2 to empirically test this model. We find that teams with players who have more diverse roles are more likely to win. We also find some evidence that teams consisting of players who have focused on training their fighting skills rather than on non-fighting skills have an advantage. However, when relational factors are included in the model, some skill factors are no longer significant. Instead, friendship ties between team members as well as the embeddedness of the team within the community have a positive impact on the likelihood of a team to win.},
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Huang, Yun; Neidhardt, Julia; Contractor, Noshir
Why Players Leave: Exploring Major Factors of Quitting EverQuest II Miscellaneous
2014.
@misc{20.500.12708_86040,
title = {Why Players Leave: Exploring Major Factors of Quitting EverQuest II},
author = {Yun Huang and Julia Neidhardt and Noshir Contractor},
year = {2014},
date = {2014-01-01},
abstract = {In online games as well as many online social sites, the success and attractiveness of a community is strongly determined by the active participation of its members. Analyzing why people leave and predicting churn behavior becomes a critical challenge, especially with the mutual influence among users. The purpose of this paper is to explore important factors that influence quitting decisions in games from three dimensions: achievement, commitment, and social network effects. In EverQuest II, a massively multiplayer online role-playing game, we identify different player attributes such as their progress and rewards in game, time committed on different game activities, and team and organization relations and examine their impacts on the likelihood of unsubscribing the game. In particular, we focus on social influence in players' teaming network and characterize the contagious effect of quitting behavior. We use a generalization of Exponential Random Graph Models (ERGM/p*) to study the social influence among users who played together. Our results show that players with higher achievement, such as higher levels, more award items, and more kills, are less likely to quit the game. The commitment on game activities has a mixed effect: players spend more time on building items are more likely to quit but players spend more time on trading items are less likely to quit. For social effects, we find that players who have more teammates and join a guild are less likely to quit. However, the likelihood of quitting increases significantly with the number of quitting teammates.},
keywords = {},
pubstate = {published},
tppubtype = {misc}
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Neidhardt, Julia
A picture-based approach for travel recommendations Miscellaneous
2014.
@misc{20.500.12708_85978,
title = {A picture-based approach for travel recommendations},
author = {Julia Neidhardt},
year = {2014},
date = {2014-01-01},
abstract = {Personalized recommendation strongly relies on an accurate model to capture user preferences; eliciting this information is, in general, a hard problem. In the field of tourism this initial profiling becomes even more challenging. It has been shown that particularly in the beginning of the travel decision making process, users themselves are often not conscious of their needs and are not able to express them. Aiming at revealing implicitly given user preferences, this work introduces an approach that utilizes a set of travel related pictures to discover users' travel behavior and in turn, to deliver recommendations. Users first select three to seven pictures they like when thinking about their future travels. Then the selected pictures are mapped onto seven preference factors that reflect different travel behavioral aspects. The scores of the factors are used to recommend touristic point of interests. The pilot study shows that users are more motivated and satisfied using this non-verbal way of interaction. This talk discusses a stream of studies to quantify intangible user preferences and provide an easy and playful method to generate inputs/data for recommendation systems.},
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Neidhardt, Julia; Schuster, Rainer; Seyfang, Leonhard; Werthner, Hannes
Eliciting the users’ unknown preferences Proceedings Article
In: Proceedings of the 8th ACM Conference on Recommender systems – RecSys ’14, ACM, New York, NY, USA, 2014.
@inproceedings{20.500.12708_55147,
title = {Eliciting the users' unknown preferences},
author = {Julia Neidhardt and Rainer Schuster and Leonhard Seyfang and Hannes Werthner},
doi = {10.1145/2645710.2645767},
year = {2014},
date = {2014-01-01},
booktitle = {Proceedings of the 8th ACM Conference on Recommender systems - RecSys '14},
publisher = {ACM},
address = {New York, NY, USA},
abstract = {Personalized recommendation strongly relies on an accurate model to capture user preferences; eliciting this information is, in general, a hard problem. In the field of tourism this initial profiling becomes even more challenging. It has been shown that particularly in the beginning of the travel decision making process, users themselves are often not conscious of their needs and are not able to express them. In this paper, the basics of a picture-based approach are introduced that aims at revealing implicitly given user preferences. Based on a set of travel related pictures selected by a user, an individual travel profile is deduced. This is accomplished by mapping those pictures onto seven basic factors that reflect different travel behavioral aspects. Also tourism products can be represented by this seven factor model. Thus, this model constitutes the basis of our recommendation algorithm. First tests show that this non-verbal way of interaction is experienced as exiting and inspiring.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
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Neidhardt, Julia
Social Influence Analysis in Online Travel Communities Miscellaneous
2013.
@misc{20.500.12708_84842,
title = {Social Influence Analysis in Online Travel Communities},
author = {Julia Neidhardt},
doi = {10.1007/978-3-642-36309-2},
year = {2013},
date = {2013-01-01},
abstract = {We propose a framework that integrates network analysis techniques as well as methods for social influence analysis of both static and dynamic networks. In the context of Web-based travel communities, this framework could lead to deeper insights on how members of such a community are influenced by others. First, we discuss the important role of the Web and of social media in today's tourism landscape. To understand Web-related phenomena a theoretical framework is needed that is able to capture those dynamics. Based on the state-of-the-art, we then introduce our conceptual and methodological approach. Expected results are on the one hand concrete statements about online travel communities and on the other hand a classification of methods related to network and social influence analysis.},
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tppubtype = {misc}
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Pobiedina, Nataliia; Neidhardt, Julia; Moreno, Maria Carmen Calatrava; Werthner, Hannes
Ranking Factors of Team Success Proceedings Article
In: WWW 2013 Companion, pp. 1185–1193, Companion Publication of the IW3C2 WWW 2013 Conference, 2013, ISBN: 978-1-4503-2038-2.
@inproceedings{20.500.12708_54595,
title = {Ranking Factors of Team Success},
author = {Nataliia Pobiedina and Julia Neidhardt and Maria Carmen Calatrava Moreno and Hannes Werthner},
isbn = {978-1-4503-2038-2},
year = {2013},
date = {2013-01-01},
booktitle = {WWW 2013 Companion},
pages = {1185–1193},
publisher = {Companion Publication of the IW3C2 WWW 2013 Conference},
abstract = {As an increasing number of human activities are moving to the Web, more and more teams are predominantly virtual. Therefore, formation and success of virtual teams is an important issue in a wide range of fields. In this paper we model social behavior patterns of team work using data from virtual communities. In particular, we use data about the Web community of the multiplayer online game Dota~2 to study cooperation within teams. By applying statistical analysis we investigate how and to which extent different factors of the team in the game, such as role distribution, experience, number of friends and national diversity, have an influence on the team's success. In order to complete the picture we also rank the factors according to their influence. The results of our study imply that cooperation within the team is better than competition.},
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Pobiedina, Nataliia; Neidhardt, Julia; Moreno, Maria Carmen Calatrava; Grad-Gyenge, Laszlo; Werthner, Hannes
On Successful Team Formation: Statistical Analysis of a Multiplayer Online Game Proceedings Article
In: 2013 IEEE 15th Conference on Business Informatics, IEEE, 2013.
@inproceedings{20.500.12708_54639,
title = {On Successful Team Formation: Statistical Analysis of a Multiplayer Online Game},
author = {Nataliia Pobiedina and Julia Neidhardt and Maria Carmen Calatrava Moreno and Laszlo Grad-Gyenge and Hannes Werthner},
doi = {10.1109/cbi.2013.17},
year = {2013},
date = {2013-01-01},
booktitle = {2013 IEEE 15th Conference on Business Informatics},
publisher = {IEEE},
abstract = {Teamwork plays an important role in many areas of today´s society, such as business activities. Thus, the question of how to form an effective team is of increasing interest. In this
paper we use the team-oriented multiplayer online game Dota 2 to study cooperation within teams and the success of teams. Making use of game log data, we choose a statistical approach to identify factors that increase the chance of a team to win. The factors
that we analyze are related to the roles that players can take within the game, the experiences of the players and friendship ties within a team. Our results show that such data can be used
to infer social behavior patterns.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
paper we use the team-oriented multiplayer online game Dota 2 to study cooperation within teams and the success of teams. Making use of game log data, we choose a statistical approach to identify factors that increase the chance of a team to win. The factors
that we analyze are related to the roles that players can take within the game, the experiences of the players and friendship ties within a team. Our results show that such data can be used
to infer social behavior patterns.
Neidhardt, Julia
Social Influence Analysis Networks and Teams Technical Report
2013.
@techreport{20.500.12708_38664,
title = {Social Influence Analysis Networks and Teams},
author = {Julia Neidhardt},
year = {2013},
date = {2013-01-01},
abstract = {This report summarizes the research conducted during my time as a visiting scholar at the SONIC lab at Northwestern University, i.e., from August 2013 until November 2013. The work was done in close cooperation with Yun Huang and Prof. Noshir Contractor from SONIC lab. The research stay was funded by the Austrian Marshall Plan Foundation.},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
Fensel, Anna; Neidhardt, Julia; Pobiedina, Nataliia; Fensel, Dieter; Werthner, Hannes
Towards an Intelligent Framework to Understand and Feed the Web Proceedings Article
In: Abramowicz, Witold; Domingue, John; Węcel, Krzysztof (Ed.): Business Information Systems Workshops, pp. 255–266, Springer, Berlin, Heidelberg, 2012, ISBN: 9783642342271.
@inproceedings{20.500.12708_54208,
title = {Towards an Intelligent Framework to Understand and Feed the Web},
author = {Anna Fensel and Julia Neidhardt and Nataliia Pobiedina and Dieter Fensel and Hannes Werthner},
editor = {Witold Abramowicz and John Domingue and Krzysztof Węcel},
doi = {10.1007/978-3-642-34228-8_24},
isbn = {9783642342271},
year = {2012},
date = {2012-01-01},
booktitle = {Business Information Systems Workshops},
pages = {255–266},
publisher = {Springer},
address = {Berlin, Heidelberg},
abstract = {The Web is becoming a mirror of the "real" physical world. More and more aspects of our life move to the Web, thus also transforming this world. And the diversity of ways to communicate over the Internet has enormously grown. In this context communicating the right thing at the right time in the right way to the right person has become a remarkable challenge. In this conceptual paper we propose a framework to apply semantic technologies in combination with statistical and learning methods on Web and social media data to build a decision support framework. This framework should help professionals as well as normal users to optimize the spread of their information and the potential impact of this information on the Web.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}