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  • 1.
    Bauer, Christine
    et al.
    Johannes Kepler University, Linz, Institute of Computational Perception & LIT AI Lab, Linz, Austria.
    Ferwerda, Bruce
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics.
    Conformity Behavior in Group Playlist Creation2020Conference paper (Refereed)
    Abstract [en]

    A strong research record on conformity has evidenced that individuals tend to conform with a group’s majority opinion. In contrast to existing literature that investigates conformity to a majority group opinion against an objectively correct answer, the originality of our study lies in that we investigate conformity in a subjective context. The emphasis of our analysis lies on the concept of “switching direction” in favor or against an item. We present first results from an online experiment where groups of five had to create a music playlist. A song was added to the playlist with an unanimous positive decision only. After seeing the other group members’ ratings, participants had the opportunity to revise their own response. Our results suggest different conformity behaviors for originally favored compared to disliked songs. For favored songs, one negative judgement by another group member was sufficient to induce participants to downvote the song. For originally disliked songs, in contrast, a majority of positive judgements was needed to induce participants to switch their vote.

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  • 2.
    Eriksson, Marcus
    et al.
    Jönköping University, School of Engineering, JTH, Industrial Product Development, Production and Design.
    Ferwerda, Bruce
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics.
    Towards a User Experience Framework for Business Intelligence2019In: Journal of Computer Information Systems, ISSN 0887-4417Article in journal (Refereed)
    Abstract [en]

    Business intelligence (BI) systems are software applications that are used to gather and process data and to deliver the processed data in understandable way to the end users. With a younger generation of users moving into key positions in organizations and enterprises higher user experience (UX) demands are placed on BI systems interfaces. Companies developing BI systems lack standardized routines for implementing UX in their BI solutions. The purpose of this study was to develop a theoretical framework based on existing research and combine it with empirical data gathered from professionals in BI systems industry in Sweden with the intention of proposing a UX framework applicable to BI systems development. The study resulted in a framework being developed using iterative build-evaluate iterations. The framework is a scalable UX framework for BI systems interfaces covering areas from planning and strategizing to implementation, maintenance, and evaluation.

  • 3.
    Ferwerda, Bruce
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics.
    Graus, Mark
    Maastricht University.
    Predicting Musical Sophistication from Music Listening Behaviors: A Preliminary Study2018Conference paper (Refereed)
    Abstract [en]

    Psychological models are increasingly being used to explain online behavioral traces. Aside from the commonly used personality traits as a general user model, more domain dependent models are gaining attention. The use of domain dependent psychological models allows for more fine-grained identification of behaviors and provide a deeper understanding behind the occurrence of those behaviors. Understanding behaviors based on psychological models can provide an advantage over data-driven approaches. For example, relying on psychological models allow for ways to personalize when data is scarce. In this preliminary work we look at the relation between users' musical sophistication and their online music listening behaviors and to what extent we can successfully predict musical sophistication. An analysis of data from a study with 61 participants shows that listening behaviors can successfully be used to infer users' musical sophistication.

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  • 4.
    Ferwerda, Bruce
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics.
    Lee, Michael
    Department of Informatics New Jersey Institute of Technology Newark, New Jersey, USA.
    Tamagotchi++: A Serious, Personalized Game to Encourage Healthy Behavior2019In: Companion Proceedings of the 24th International on Intelligent User Interfaces: 3rd Workshop on Theory-Informed User Modeling for Tailoring and Personalizing Interfaces (HUMANIZE) / [ed] Parra D.,Riche N.,Trattner C., CEUR-WS , 2019, Vol. 2327Conference paper (Refereed)
    Abstract [en]

    With life expectancy steadily increasing, healthy aging is becoming more important. Especially at a later age, the susceptibility to complications, such as morbidity, increases. Engaging in sufficient physical activities throughout a lifespan lowers the chances on such complications and contributes to an increased quality of life. However, the vast majority of the world's population does not engage enough in any form of physical activity. In this position paper we propose a serious game solution to promote physical activities based on the popular Tamagotchi from the 90's. We propose the virtual character of which the user needs to take care of to be a reflection of oneself. Thereby, any (in)activities of the user is directly reflected through the emotional and physical state of the character. Through the character, we hope to increase engagement in physical activities and facilitate long term behavioral change. Furthermore, we propose additional features and open research questions.

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  • 5.
    Ferwerda, Bruce
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics.
    Tkalcic, Marko
    Free University of Bozen-Bolzano.
    Exploring Online Music Listening Behaviors of Musically Sophisticated Users2019In: ACM UMAP 2019 Adjunct - Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization, 2019, p. 33-37Conference paper (Refereed)
    Abstract [en]

    Due to the rise of available online music, a lot of music consumption is moving from traditional offline media to online sources. Online music sources offer almost an unlimited music collection to its users. Hence, how music is consumed by users (e.g., experts) may differ from traditional offline sources. In this work we explored how musically sophisticated users (i.e. experts) consume online music in terms of diversity. To analyze this, we gathered data from two different sources: Last.fm and Spotify. As expertise is defined by the ubiquitousness of experiences, we calculated different diversity measurements to explore how ubiquitous (in terms of diversity) the listening behaviors of users are. We found that different musical sophistication levels correspond to applying diversity related to specific kind of musical characteristics (i.e., artist or genre). Our results can provide knowledge on how systems should be designed to provide better support to expert users.

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  • 6.
    Ferwerda, Bruce
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics.
    Tkalcic, Marko
    Free University of Bozen-Bolzano.
    Predicting Users' Personality from Instagram Pictures: Using Visual and/or Content Features?2018In: UMAP ’18- Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization, Association for Computing Machinery (ACM), 2018, p. 157-161Conference paper (Refereed)
    Abstract [en]

    Instagram is a popular social networking application that allows users to express themselves through the uploaded content and the different filters they can apply. In this study we look at personality prediction from Instagram picture features. We explore two different features that can be extracted from pictures: 1) visual features (e.g., hue, valence, saturation), and 2) content features (i.e., the content of the pictures). To collect data, we conducted an online survey where we asked participants to fill in a personality questionnaire and grant us access to their Instagram account through the Instagram API. We gathered 54,962 pictures of 193 Instagram users. With our results we show that visual and content features can be used to predict personality from and perform in general equally well. Combining the two however does not result in an increased predictive power. Seemingly, they are not adding more value than they already consist of independently.

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  • 7.
    Ferwerda, Bruce
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics.
    Tkalcic, Marko
    Free University of Bozen-Bolzano.
    You Are What You Post: What the Content of Instagram Pictures Tells About Users’ Personality2018In: 2nd Workshop on Theory-Informed User Modeling for Tailoring and Personalizing Interfaces (HUMANIZE), CEUR-WS , 2018Conference paper (Refereed)
    Abstract [en]

    Instagram is a popular social networking application that allowsusers to express themselves through the uploaded contentand the different filters they can apply. In this study we look atthe relationship between the content of the uploaded Instagrampictures and the personality traits of users. To collect data, weconducted an online survey where we asked participants tofill in a personality questionnaire, and grant us access to theirInstagram account through the Instagram API. We gathered54,962 pictures of 193 Instagram users. Through the GoogleVision API, we analyzed the pictures on their content and clusteredthe returned labels with the k-means clustering approach.With a total of 17 clusters, we analyzed the relationship withusers’ personality traits. Our findings suggest a relationshipbetween personality traits and picture content. This allow fornew ways to extract personality traits from social media trails,and new ways to facilitate personalized systems.

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  • 8.
    Ferwerda, Bruce
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics.
    Tkalcic, Marko
    Free University of Bozen-Bolzano, Italy.
    Schedl, Markus
    Johannes Kepler University, Austria.
    Personality Traits and Music Genre Preferences: How Music Taste Varies Over Age Groups2017In: Proceedings of the 1st Workshop on Temporal Reasoning in Recommender Systems (RecTemp) at the 11th ACM Conference on Recommender Systems / [ed] Maria Bielikova, Veronika Bogina, Tsvi Kuflik, Roy Sasson, CEUR-WS , 2017, Vol. 1922, p. 16-20Conference paper (Refereed)
    Abstract [en]

    Personality traits are increasingly being incorporated in systems to provide a personalized experience to the user. Current work focusing on identifying the relationship between personality and behavior, preferences, and needs often do not take into account differences between age groups. With music playing an important role in our lives, differences between age groups may be especially prevalent. In this work we investigate whether differences exist in music listening behavior between age groups. We analyzed a dataset with the music listening histories and personality information of 1415 users. Our results show agreements with prior work that identi€ed personality-based music listening preferences. However, our results show that the agreements we found are in some cases divided over different age groups, whereas in other cases additional correlations were found within age groups. With our results personality-based systems can provide better music recommendations that is in line with the user’s age.

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  • 9.
    Ferwerda, Bruce
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics.
    Yang, Emily
    Johannes Kepler University Linz, Austria.
    Schedl, Markus
    Johannes Kepler University Linz, Austria.
    Tkalcic, Marko
    Free University of Bozen-Bolzano, Italy.
    Personality and taxonomy preferences, and the influence of category choice on the user experience for music streaming services2019In: Multimedia tools and applications, ISSN 1380-7501, E-ISSN 1573-7721, Vol. 78, no 14, p. 20157-20190Article in journal (Refereed)
    Abstract [en]

    Music streaming services increasingly incorporate different ways for users to browse for music. Next to the commonly used “genre” taxonomy, nowadays additional taxonomies, such as mood and activities, are often used. As additional taxonomies have shown to be able to distract the user in their search, we looked at how to predict taxonomy preferences in order to counteract this. Additionally, we looked at how the number of categories presented within a taxonomy influences the user experience. We conducted an online user study where participants interacted with an application called “Tune-A-Find”. We measured taxonomy choice (i.e., mood, activity, or genre), individual differences (e.g., personality traits and music expertise factors), and different user experience factors (i.e., choice difficulty and satisfaction, perceived system usefulness and quality) when presenting either 6- or 24-categories within the picked taxonomy. Among 297 participants, we found that personality traits are related to music taxonomy preferences. Furthermore, our findings show that the number of categories within a taxonomy influences the user experience in different ways and is moderated by music expertise. Our findings can support personalized user interfaces in music streaming services. By knowing the user’s personality and expertise, the user interface can adapt to the user’s preferred way of music browsing and thereby mitigate the problems that music listeners are facing while finding their way through the abundance of music choices online nowadays.

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  • 10.
    Graus, M. P.
    et al.
    School of Business and Economics, Maastricht University, Maastricht, Netherlands.
    Ferwerda, Bruce
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics.
    Tkalcic, M.
    Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Koper, Slovenia.
    Germanakos, P.
    UX ICD, Product Engineering, Intelligent Enterprise Group, SAP SE, Walldorf, Germany.
    Fourth HUMANIZE workshop on transparency and explainability in adaptive systems through user modeling grounded in psychological theory: Summary2020In: International Conference on Intelligent User Interfaces, Proceedings IUI, Association for Computing Machinery (ACM), 2020, p. 19-20Conference paper (Refereed)
    Abstract [en]

    The fourth HUMANIZE workshop1 on Transparency and Explainability in Adaptive Systems through User Modeling Grounded in Psychological Theory took place in conjunction with the 25th annual meeting of the Intelligent User Interfaces (IUI)2 community in Cagliari, Italy on March 17, 2020. The workshop provided a venue for researchers from different fields to interact by accepting contributions on the intersection of practical data mining methods and theoretical knowledge for personalization. A total of four papers was accepted for this edition of the workshop. 

  • 11.
    Graus, Mark
    et al.
    Maastricht University.
    Ferwerda, Bruce
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics.
    Theory-grounded user modeling for personalized HCI2019In: Personalized human-computer interaction / [ed] M. Augstein, E. Herder & W. Wörndl, Berlin: Walter de Gruyter, 2019Chapter in book (Refereed)
    Abstract [en]

    Personalized systems are systems that adapt themselves to meet the inferred needs of individual users. The majority of personalized systems mainly rely on data describing how users interacted with these systems. A common approach is to use historical data to predict users’ future needs, preferences, and behavior to subsequently adapt the system to cater to these predictions. However, this adaptation is often done without leveraging the theoretical understanding between behavior and user traits that can be used to characterize individual users or the relationship between user traits and needs that can be used to adapt the system. Adopting a more theoretical perspective can benefit personalization in two ways: (i) letting systems rely on theory can reduce the need for extensive data-driven analysis, and (ii) interpreting the outcomes of data-driven analysis (such as predictive models) from a theoretical perspective can expand our knowledge about users. However, incorporating theoretical knowledge in personalization brings forth a number of challenges. In this chapter, we review literature that taps into aspects of (i) psychological models from traditional psychological theory that can be used in personalization, (ii) relationships between psychological models and online behavior, (iii) automated inference of psychological models from data, and (iv) how to incorporate psychological models in personalized systems. Finally, we propose a step-by-step approach on how to design personalized systems that take users’ traits into account.

  • 12.
    Graus, Mark
    et al.
    Eindhoven University of Technology.
    Ferwerda, BruceJönköping University, School of Engineering, JTH, Computer Science and Informatics.Tkalcic, MarkoFree University of Bozen-Bolzano.Germanakos, PanagiotisSAP.
    Second Workshop on Theory-Informed User Modeling for Tailoring and Personalizing Interfaces (HUMANIZE)2018Conference proceedings (editor) (Refereed)
    Abstract [en]

    The second workshop on Theory-Informed User Modeling for Tailoring and Personalizing Interfaces (HUMANIZE) took place in conjunction with the 23rd annual meeting of the intelligent user interfaces (IUI) community in Tokyo, Japan on March 11, 2018. The goal of the workshop was to attract researchers from different fields by accepting contributions on the intersection of practical data mining methods and theoretical knowledge for personalization. A total of eight papers were accepted for this edition of the workshop.

  • 13.
    Graus, Mark
    et al.
    Eindhoven University of Technology, Netherlands.
    Ferwerda, Bruce
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics.
    Tkalcic, Marko
    Free University of Bozen-Bolzano, Italy.
    Germanakos, Panagiotis
    UX, Mobile and Business Services, PandI ICD SAP SE, Walldorf, Germany.
    Second workshop on theory-informed user modeling for tailoring and personalizing interfaces (HUMANIZE): Workshop preface2018In: Second Workshop on Theory-Informed User Modeling for Tailoring and Personalizing Interfaces (HUMANIZE) / [ed] Mark Graus, Bruce Ferwerda, Marko Tkalcic, Panagiotis Germanakos, CEUR-WS , 2018, Vol. 2068Conference paper (Refereed)
    Abstract [en]

    The second workshop on Theory-Informed UserModeling for Tailoring and Personalizing Interfaces (HUMANIZE)1 took place in conjunction with the 23rd annual meeting of the intelligent user interfaces (IUI)2 community in Tokyo, Japan on March 11, 2018. The goal of the workshop was to attract researchers from different fields by accepting contributions on the intersection of practical data mining methods and theoretical knowledge for personalization. A total of eight papers were accepted for this edition of the workshop. 

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  • 14.
    Graus, Mark
    et al.
    School of Business and Economics, Maastricht University, Maastricht, Netherlands.
    Ferwerda, Bruce
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics.
    Tkalcic, Marko
    Faculty of Computer Science, Free University of Bozen-Bolzano, Bozen-Bolzano, Italy.
    Germanakos, Panagiotis
    Dep. of Computer Science, University of Cyprus, Cyprus.
    Third workshop on theory-informed user modeling for tailoring and personalizing interfaces (HUMANIZE): preface2019In: International Conference on Intelligent User Interfaces, Proceedings IUI, Association for Computing Machinery (ACM), 2019, p. 127-128Conference paper (Refereed)
    Abstract [en]

    The third workshop on Theory-Informed User Modeling for Tailoring and Personalizing Interfaces (HUMANIZE)1 took place in conjunction with the 24th annual meeting of the intelligent user interfaces (IUI)2 community in Los Angeles, CA, USA on March 20, 2019. The goal of the workshop was to attract researchers from different fields by accepting contributions on the intersection of practical data mining methods and theoretical knowledge for personalization. A total of six papers were accepted for this edition of the workshop.

  • 15.
    Graus, Mark P.
    et al.
    School of Business and Economics, Maastricht University, Maastricht, Netherlands.
    Ferwerda, Bruce
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics.
    Tkalčič, Marko
    Faculty of Computer Science, Free University of Bozen-Bolzano, Bozen-Bolzano, Italy.
    Germanakos, Panagiotis
    Dep. of Computer Science, University of Cyprus, Cyprus.
    A summary of the third workshop on theory-informed user modeling for tailoring and personalizing interfaces2019In: CEUR Workshop Proceedings / [ed] Parra D.,Riche N.,Trattner C., CEUR-WS , 2019, Vol. 2327Conference paper (Refereed)
    Abstract [en]

    The third workshop on Theory-Informed User Modeling for Tailoring and Personalizing Interfaces (HUMANIZE) 1 took place in conjunction with the 24th annual meeting of the intelligent user interfaces (IUI) 2 community in Los Angeles, CA, USA on March 20, 2019. The goal of the workshop was to attract researchers from different fields by accepting contributions on the intersection of practical data mining methods and theoretical knowledge for personalization. A total of six papers were accepted for this edition of the workshop. 

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  • 16.
    Kiunsi, Domina
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics.
    Ferwerda, Bruce
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics.
    Using a Serious Game to Teach User-Centered Design2019In: Companion Proceedings of the 24th International on Intelligent User Interfaces: 3rd Workshop on Theory-Informed User Modeling for Tailoring and Personalizing Interfaces (HUMANIZE) / [ed] Parra D.,Riche N.,Trattner C., CEUR-WS , 2019, Vol. 2327Conference paper (Refereed)
    Abstract [en]

    The development of products with a good user experience requires a thorough understanding of the prospective users' behaviors, preferences, and needs. One of the design approaches that places emphasis on the needs of users is the user-centered design process. However, there is a general resistance in organizations to incorporate user-centered design practices in product development. One influencing factor is that user-centered design is multidisciplinary. Hence, creates challenges to get a mutual understanding and collaboration across different stakeholders throughout the development. In this paper we present the results of a serious game prototype that was created to describe user-centered design across stakeholders. Results of the prototype evaluation reveal that it has potential to impart knowledge on concepts related to user-centered design. Additionally, we propose further development of the game by personalizing game elements to increase the effectiveness of learning and player experience.

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  • 17.
    Knees, Peter
    et al.
    TU Wien.
    Schedl, Marku
    Johannes Kepler University.
    Ferwerda, Bruce
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics.
    Laplante, Audrey
    Université de Montréal.
    User Awareness in Music Recommender Systems2019In: Personalized human-computer interaction / [ed] M. Augstein, E. Herder & W. Wörndl, Berlin: Walter de Gruyter, 2019Chapter in book (Refereed)
    Abstract [en]

    Music recommender systems are a widely adopted application of personalized systems and interfaces.By tracking the listening activity of their users and building preference profiles, a user can be given recommendations based on the preference profiles of all users (collaborative filtering), characteristics of the music listened to (content-based methods), meta-data and relational data (knowledge-based methods; sometimes also considered content-based methods) or a mixture of these with other features (hybrid methods).In this chapter, we focus on the listener's aspects of music recommender systems.We discuss different factors influencing relevance for recommendation on both the listener's and the music's side and categorize existing work. In more detail, we then review aspects of (i) listener background in terms of individual, i.e., personality traits and demographic characteristics, and cultural features, i.e., societal and environmental characteristics, (ii) listener context, in particular modeling dynamic properties and situational listening behavior, and (iii) listener intention, in particular by studying music information behavior, i.e., how people seek, find, and use music information.This is followed by a discussion of user-centric evaluation strategies for music recommender systems. We conclude the chapter with a reflection on current barriers, by pointing out current and longer-term limitations of existing approaches and outlining strategies for overcoming these.

  • 18.
    Lay, Alixe
    et al.
    University College London, UK.
    Ferwerda, Bruce
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics.
    Predicting Users’ Personality Based on Their ‘Liked’ Images on Instagram2018In: 2nd Workshop on Theory-Informed User Modeling for Tailoring and Personalizing Interfaces (HUMANIZE), 2018, CEUR-WS , 2018Conference paper (Refereed)
    Abstract [en]

    With the development in technology and the increasing ubiquityof social media services, it has created new opportunities to studypersonality from the digital traces individuals leave behind. Thelarge number of user-generated images on social media hasprompted renewed interests in understanding the psychologicalfactors driving production and consumption behaviours of visualcontent. Instagram is currently the fastest growing photo-sharingsocial media platform, with more than 400 million active usersand nearly 100 million photos shared on the platform daily [1],and generates 1.2 billion likes each day [2]. The understanding ofthe appeal of visual content at an individual level is highlyrelevant to psychometric assessment, social media marketing andinterface personalisation. In this position paper, we address theneed to explore the avenue of automatic personality assessmentusing ‘liked’ images on Instagram.

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  • 19.
    Schedl, Markus
    et al.
    Johannes Kepler University, Austria.
    Ferwerda, Bruce
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics.
    Large-scale Analysis of Group-specific Music Genre Taste From Collaborative Tags2017In: Proceedings - 2017 IEEE International Symposium on Multimedia, ISM 2017, IEEE, 2017, p. 479-482, article id Code 134021Conference paper (Refereed)
    Abstract [en]

    In this paper, we describe the LFM-1b User Genre Profile dataset. It provides detailed information on musical genre preferences for more than 120,000 listeners and links to the LFM-1b dataset. We created the dataset by exploiting social tags, indexing them using two genre term sets, and aggregating the resulting annotated listening events on the user level. We foresee several applications of the dataset in music retrieval and recommendation tasks, among others to build and evaluate decent user models, to alleviate cold-start situations in music recommender systems, and to increase their performance using the additional abstraction layer of genre. We further present results of statistical analyses of the dataset, regarding genre preferences and their consistencies. We do so for the entire user population and for user groups defined by demographic similarities. Moreover, we report interesting insights about correlations between musical preferences on the genre level.

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  • 20.
    Schedl, Markus
    et al.
    Johannes Kepler University, Austria.
    Lemmerich, Florian
    Leibniz Institute for the Social Sciences, Germany.
    Ferwerda, Bruce
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics.
    Skowron, Marcin
    Austrian Research Institute for Artificial Intelligence, Austria.
    Knees, Peter
    Vienna University of Vienna, Austria.
    Indicators of Country Similarity in Terms of Music Taste, Cultural, and Socio-economic Factors2017In: Proceedings - 2017 IEEE International Symposium on Multimedia, ISM 2017, IEEE, 2017, p. 308-311, article id Code 134021Conference paper (Refereed)
    Abstract [en]

    Considering the cultural background of users is known to improve recommender systems for multimedia items. In this work, we focus on music and analyze user demographics and music listening events in a large corpus (120,000 users, 109 events) from Last.fm to investigate whether similarity between countries in terms of cultural and socio-economic factors is reflected in music taste. To this end, we propose a tag-based model to describe the music taste of a country and correlate the resulting music profiles to Hofstede’s cultural dimensions and the Quality of Government data. Spearman’s rank-order correlation and Quadratic Assignment Procedure indeed indicate statistically significant weak to medium correlations of music taste and several cultural and socio-economic factors. The results will help elaborating culture-aware models of music listeners and in turn likely yield improved music recommender systems.

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  • 21.
    Tkalcic, Marko
    et al.
    Free University of Bozen-Bolzano.
    Ferwerda, Bruce
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics.
    Eudaimonic Modeling of Moviegoers2018In: UMAP ’18- Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization, 2018Conference paper (Refereed)
    Abstract [en]

    One of the important aspects of movie-making is to trigger emotional responses in viewers. These emotional experiences can be divided into hedonic and eudaimonic. While the former are characterized as plain enjoyment, the latter deal with getting greater insight, self-reflection or contemplation. So far, modeling of user preferences about movies and personalization algorithms have largely ignored the eudaimonic aspect of the consumption of movies. In this paper we fill this gap by exploring what are the relationship between (i) eudaimonic and hedonic characteristics of movies, (ii) users' preferences and (iii) users' personality. Our results show that eudaimonic user profiling effectively divides users into pleasure-seekers and meaning-seekers.

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  • 22.
    Tkalcic, Marko
    et al.
    Free University of Bozen-Bolzano.
    Ferwerda, Bruce
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics.
    Theory-driven Recommendations: Modeling Hedonic and Eudaimonic Movie Preferences2018Conference paper (Refereed)
    Abstract [en]

    Most of the research in recommender systems focuses on data-driven approaches. In this paper we present our vision for complementing data-driven approaches with model-driven ones. We present a preliminary experimental set-up and we expose our research plan. In the experimental set-up we acquired eudaimonic characteristics of movies and user preferences. Furthermore, we performed a preliminary analysis of the acquired data.

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  • 23.
    Xu, Yu
    et al.
    New Jersey Institute of Technology Newark, New Jersey, USA.
    Ferwerda, Bruce
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics.
    Lee, Michael J.
    New Jersey Institute of Technology Newark, New Jersey, USA.
    A Qualitative Study of User Participation and Challenges in a Social Shopping Context2020Conference paper (Refereed)
    Abstract [en]

    Social shopping enables people to share and discuss about shopping in collaborative shopping environments. While much work has focused on using social data to promote shopping, fewer works have examined the way people socialize in the context of shopping as a personalized collaborative activity. In this paper, we propose to use qualitative methods to gain insight into people’s perceptions, concerns, and challenges in social shopping-related activities. Based on the findings, our work may contribute to the design of future online shopping sites and social media platforms that improve user engagement and participation in social shopping interactions, as well as facilitating personalized shopping and social experiences in online communities.

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