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arXiv 2023年

作者： Koopmann, Tobias Pfister, Jan Markus, André Carolus, Astrid Wienrich, Carolin Hotho, Andreas University of Würzburg Department of Computer Science CAIDAS Chair for Data Science Germany University of Würzburg Institute Human-Computer-Media Psychology of Intelligent Interactive Systems Germany University of Würzburg Institute Human-Computer-Media Media Psychology Germany

Analyzing, understanding, and describing human behavior is advantageous in different settings, such as web browsing or traffic navigation. Understanding human behavior naturally helps to improve and optimize the underlying infrastructure or user interfaces. Typically, human navigation is represented by sequences of transitions between states. Previous work suggests to use hypotheses, representing different intuitions about the navigation to analyze these transitions. To mathematically grasp this setting, first-order Markov chains are used to capture the behavior, consequently allowing to apply different kinds of graph comparisons, but comes with the inherent drawback of losing information about higher-order dependencies within the sequences. To this end, we propose to analyze entire sequences using autoregressive language models, as they are traditionally used to model higher-order dependencies in sequences. We show that our approach can be easily adapted to model different settings introduced in previous work, namely HypTrails, MixedTrails and even SubTrails, while at the same time bringing unique advantages: 1. Modeling higher-order dependencies between state transitions, while 2. being able to identify short comings in proposed hypotheses, and 3. naturally introducing a unified approach to model all settings. To show the expressiveness of our approach, we evaluate our approach on different synthetic datasets and conclude with an exemplary analysis of a real-world dataset, examining the behavior of users who interact with voice assistants. © 2023, CC BY.

关键词： User interfaces

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A conceptual model for leaving the data-centric approach in machine learning

arXiv

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arXiv 2023年

作者： Scher, Sebastian Geiger, Bernhard Kopeinik, Simone Trügler, Andreas Kowald, Dominik Know-Center Sandgasse 36 Graz Austria Institute of Interactive Systems and Data Science Graz University of Technology Inffeldgasse 16C Graz Austria Department of Geography and Regional Science University of Graz Heinrichstraße 36 Graz Austria

For a long time, machine learning (ML) has been seen as the abstract problem of learning relationships from data independent of the surrounding settings. This has recently been challenged, and methods have been proposed to include external constraints in the machine learning models. These methods usually come from application-specific fields, such as de-biasing algorithms in the field of fairness in ML or physical constraints in the fields of physics and engineering. In this paper, we present and discuss a conceptual high-level model that unifies these approaches in a common language. We hope that this will enable and foster exchange between the different fields and their different methods for including external constraints into ML models, and thus leaving purely data-centric approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Value Identification in Multistakeholder Recommender systems for Humanities and Historical Research: The Case of the Digital Archive ***

arXiv

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arXiv 2024年

作者： Atzenhofer-Baumgartner, Florian Geiger, Bernhard C. Vogeler, Georg Kowald, Dominik Institute of Interactive Systems and Data Science Graz University of Technology Graz Austria Department of Digital Humanities University of Graz Graz Austria Know Center Research GmbH Graz Austria Signal Processing and Speech Communication Laboratory Graz University of Technology Graz Austria

Recommender systems remain underutilized in humanities and historical research, despite their potential to enhance the discovery of cultural records. This paper offers an initial value identification of the multiple stakeholders that might be impacted by recommendations in ***, a digital archive for historical legal documents. Specifically, we discuss the diverse values and objectives of its stakeholders, such as editors, aggregators, platform owners, researchers, publishers, and funding agencies. These in-depth insights into the potentially conflicting values of stakeholder groups allow designing and adapting recommender systems to enhance their usefulness for humanities and historical research. Additionally, our findings will support deeper engagement with additional stakeholders to refine value models and evaluation metrics for recommender systems in the given domains. Our conclusions are embedded in and applicable to other digital archives and a broader cultural heritage context. © 2024, CC BY-SA.

关键词： Embedded systems

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The data-driven business value matrix - a classification scheme for data-driven business models 32

The data-driven business value matrix - a classification sch...

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32nd Bled eConference: Humanizing Technology for a Sustainable Society, BLED 2019

作者： Breitfuss, Gert Fruhwirth, Michael Pammer-Schindler, Viktoria Stern, Hermann Dennerlein, Sebastian Know-Center Graz Austria University of Technology Institute for Interactive Systems and Data Science Graz Austria

ISBN: (纸本)9789612862800

Increasing digitization is generating more and more data in all areas of business. Modern analytical methods open up these large amounts of data for business value creation. Expected business value ranges from process optimization such as reduction of maintenance work and strategic decision support to business model innovation. In the development of a data-driven business model, it is useful to conceptualise elements of data-driven business models in order to differentiate and compare between examples of a data-driven business model and to think of opportunities for using data to innovate an existing or design a new business model. The goal of this paper is to identify a conceptual tool that supports data-driven business model innovation in a similar manner: We applied three existing classification schemes to differentiate between data-driven business models based on 30 examples for data-driven business model innovations. Subsequently, we present the strength and weaknesses of every scheme to identify possible blind spots for gaining business value out of data-driven activities. Following this discussion, we outline a new classification scheme. The newly developed scheme combines all positive aspects from the three analysed classification models and resolves the identified weaknesses. Copyright © 32nd Bled eConference Humanizing Technology for a Sustainable Society, BLED 2019 - Conference Proceedings. All rights reserved.

关键词： Classification (of information)

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Insights in Learners' Behaviour and Early Dropout Detection based on Coursera MOOCs 19

Insights in Learners' Behaviour and Early Dropout Detection ...

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19th International Conference on Information Technology Based Higher Education and Training, ITHET 2021

作者： Frank, Sarah Gutl, Christian Perez-Sanagustin, Mar Hilliger, Isabel Institute Of Interactive Systems And Data Science Graz University Of Technology Graz Austria Institute Recherche En Informatique De Toulouse Université Paul Sabatier Toulouse France School Of Engineering Pontificia Universidad Católica De Chile Santiago Chile

ISBN: (纸本)9781728188836

Increased utilization of distance learning makes the creation of effective learning tools ever more important. However, while Massive Open Online Courses (MOOCs) are a popular online learning tool, they often suffer from high user attrition. This paper investigated this effect for 6 different MOOCs with more than 35 thousand users, using AdaBoosted decision trees to create a model which was then used for the prediction of dropouts, as well as the calculation of feature importance scores. The resulting model generally scored high accuracy scores which were plotted for each course, and feature importance scores were especially high for the features encompassing the number of user requests, the user's total active time and the average time between clicks. Furthermore, the paper explored the results of the inclusion of forum data in this setting. While the forum features did not lead to a major increase in model accuracy, there was a statistical difference between the number of forum interactions for those who completed the MOOCs and those who did not, which opens up possibilities for future research into utilization of forum interaction data, and forums in MOOCs in general. © 2021 IEEE.

关键词： Decision trees

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Constraint 2021: Machine learning models for COVID-19 fake news detection shared task

arXiv

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arXiv 2021年

作者： Felber, Thomas Institute of Interactive Systems and Data Science Graz University of Technology Inffeldgasse 13 Graz8010 Austria

In this system paper we present our contribution to the Constraint 2021 COVID-19 Fake News Detection Shared Task, which poses the challenge of classifying COVID-19 related social media posts as either fake or real. In our system, we address this challenge by applying classical machine learning algorithms together with several linguistic features, such as n-grams, readability, emotional tone and punctuation. In terms of pre-processing, we experiment with various steps like stop word removal, stemming/lemmatization, link removal and more. We find our best performing system to be based on a linear SVM, which obtains a weighted average F1 score of 95.19% on test data, which lands a place in the middle of the leaderboard (place 80 of 167). © 2021, CC BY.

关键词： Classification (of information)

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The impact of algorithm awareness training on competent interaction with intelligent voice assistants

引用

Discover Education 2025年第1期4卷 1-22页

作者： Markus, André Baumann, Maximilian Pfister, Jan Hotho, Andreas Carolus, Astrid Wienrich, Carolin Psychology of Intelligent Interactive Systems Institute Human-Computer-Media Julius-Maximilians-University Emil-Fischer-Straße 50 Würzburg 97074 Germany Media Psychology Institute Human-Computer-Media Julius-Maximilians-University Oswald-Külpe-Weg 82 Würzburg 97074 Germany Chair of Data Science (Informatic X) Julius-Maximilians-University Emil-Fischer-Straße 50 Würzburg 97074 Germany

Intelligent Voice Assistants (IVAs) have become integral to many users' daily lives, using advanced algorithms to automate various tasks. Nevertheless, many users do not understand the underlying algorithms and how they work, posing potential risks to the competent and self-determined use of IVAs. This work develops three online training modules to promote algorithm awareness, providing (1) basic knowledge of algorithms, (2) risks posed by algorithms in IVAs, and (3) scientific evidence on algorithm aversion. A total of 110 participants were studied to analyze the training effects on various perception levels relevant to IVAs, including usage perception (attitude, exploration), privacy aspects (trustworthiness, privacy control), persuasion aspects (persuasion knowledge, anthropomorphic perception), and self-determined interaction variables (reflection, indulgence). The results show that the modules increase awareness of IVA algorithms, influence user perceptions (e.g., higher exploration intentions), and promote critical engagement with IVAs (e.g., lower trustworthiness). Moreover, the modules contribute to a higher sense of privacy control, reduce persuasive perceptions of IVAs (e.g., anthropomorphic perception), and promote self-determined interaction (e.g., higher indulgent use). The modules offer a new approach to promoting the competent use of IVAs in society and provide a starting point for further research and educational institutions to increase algorithm awareness for IVAs and other AI-based systems. © The Author(s) 2025.

关键词： Algorithm awareness Digital interaction literacy Human–computer interaction Intelligent voice assistant Privacy Smart speaker

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Modelling the long-term fairness dynamics of data-driven targeted help on job seekers

arXiv

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arXiv 2022年

作者： Scher, Sebastian Kopeinik, Simone Trügler, Andreas Kowald, Dominik Know-Center GmbH Graz8010 Austria Institute of Interactive Systems and Data Science Graz University of Technology Graz8010 Austria Department of Geography and Regional Science University of Graz Graz8010 Austria

The use of data‑driven decision support by public agencies is becoming more widespread and already influences the allocation of public resources. This raises ethical concerns, as it has adversely affected minorities and historically discriminated groups. In this paper, we use an approach that combines statistics and data‑driven approaches with dynamical modeling to assess long‑term fairness effects of labor market interventions. Specifically, we develop and use a model to investigate the impact of decisions caused by a public employment authority that selectively supports job‑seekers through targeted help. The selection of who receives what help is based on a data‑driven intervention model that estimates an individual’s chances of finding a job in a timely manner and rests upon data that describes a population in which skills relevant to the labor market are unevenly distributed between two groups (e.g., males and females). The intervention model has incomplete access to the individual’s actual skills and can augment this with knowledge of the individual’s group affiliation, thus using a protected attribute to increase predictive accuracy. We assess this intervention model’s dynamics—especially fairness‑related issues and trade‑offs between different fairness goals‑ over time and compare it to an intervention model that does not use group affiliation as a predictive feature. We conclude that in order to quantify the trade‑off correctly and to assess the long‑term fairness effects of such a system in the real‑world, careful modeling of the surrounding labor market is indispensable. © 2022, CC BY.

关键词： Employment

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CHALLENGES IN IMPLEMENTING A RECOMMENDER SYSTEM FOR HISTORICAL RESEARCH IN THE HUMANITIES

arXiv

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arXiv 2024年

作者： Atzenhofer-Baumgartner, Florian Geiger, Bernhard C. Trattner, Christoph Vogeler, Georg Kowald, Dominik Institute of Interactive Systems and Data Science Graz University of Technology Graz Austria Department of Digital Humanities University of Graz Graz Austria Know Center Research GmbH Graz Austria Signal Processing and Speech Communication Laboratory Graz University of Technology Graz Austria University of Bergen & Media Futures Bergen Norway

This extended abstract describes the challenges in implementing recommender systems for digital archives in the humanities, focusing on ***, a platform for historical legal documents. We discuss three key aspects: (i) the unique characteristics of so-called charters as items for recommendation, (ii) the complex multi-stakeholder environment, and (iii) the distinct information-seeking behavior of scholars in the humanities. By examining these factors, we aim to contribute to the development of more effective and tailored recommender systems for (digital) humanities research. © 2024, CC BY-SA.

关键词： Recommender systems

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Limiting tags fosters efficiency

arXiv

引用

arXiv 2021年

作者： Santos, Tiago Burghardt, Keith Lerman, Kristina Helic, Denis Institute of Interactive Systems and Data Science Graz University of Technology Austria Information Sciences Institute University of Southern California United States

Tagging facilitates information retrieval in social media and other online communities by allowing users to organize and describe online content. Researchers found that the efficiency of tagging systems steadily decreases over time, because tags become less precise in identifying specific documents, i.e., they lose their descriptiveness. However, previous works did not answer how or even whether community managers can improve the efficiency of tags. In this work, we use information-theoretic measures to track the descriptive and retrieval efficiency of tags on Stack Overflow, a question-answering system that strictly limits the number of tags users can specify per question. We observe that tagging efficiency stabilizes over time, while tag content and descriptiveness both increase. To explain this observation, we hypothesize that limiting the number of tags fosters novelty and diversity in tag usage, two properties which are both beneficial for tagging efficiency. To provide qualitative evidence supporting our hypothesis, we present a statistical model of tagging that demonstrates how novelty and diversity lead to greater tag efficiency in the long run. Our work offers insights into policies to improve information organization and retrieval in online communities. © 2021, CC BY.

关键词： Social networking (online)

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