2022
|
| Considering Temporal Aspects in Recommender Systems: A Survey Journal Article Veronica Bogina; Tsvi Kuflik; Dietmar Jannach; Maria Bielikova; Michal Kompan; Christoph Trattner
In: UMUAI journal, 2022. @article{Bogina2022,
title = {Considering Temporal Aspects in Recommender Systems: A Survey },
author = {Veronica Bogina and Tsvi Kuflik and Dietmar Jannach and Maria Bielikova and Michal Kompan and Christoph Trattner
},
url = {https://mediafutures.no/revisedversion_considering_temporal_aspects_in_rs_a_survey-6/},
year = {2022},
date = {2022-05-31},
urldate = {2022-05-31},
journal = {UMUAI journal},
abstract = {The widespread use of temporal aspects in user modeling indicates their importance, and their consideration showed to be highly effective in var- ious domains related to user modeling, especially in recommender systems. Still, past and ongoing research, spread over several decades, provided multi- ple ad-hoc solutions, but no common understanding of the issue. There is no standardization and there is often little commonality in considering tempo- ral aspects in different applications. This may ultimately lead to the problem that application developers define ad-hoc solutions for their problems at hand, sometimes missing or neglecting aspects that proved to be effective in similar cases. Therefore, a comprehensive survey of the consideration of temporal as- pects in recommender systems is required. In this work, we provide an overview of various time-related aspects, categorize existing research, present a tempo- ral abstraction and point to gaps that require future research. We anticipate this survey will become a reference point for researchers and practitioners alike when considering the potential application of temporal aspects in their personalized applications.},
keywords = {New},
pubstate = {published},
tppubtype = {article}
}
The widespread use of temporal aspects in user modeling indicates their importance, and their consideration showed to be highly effective in var- ious domains related to user modeling, especially in recommender systems. Still, past and ongoing research, spread over several decades, provided multi- ple ad-hoc solutions, but no common understanding of the issue. There is no standardization and there is often little commonality in considering tempo- ral aspects in different applications. This may ultimately lead to the problem that application developers define ad-hoc solutions for their problems at hand, sometimes missing or neglecting aspects that proved to be effective in similar cases. Therefore, a comprehensive survey of the consideration of temporal as- pects in recommender systems is required. In this work, we provide an overview of various time-related aspects, categorize existing research, present a tempo- ral abstraction and point to gaps that require future research. We anticipate this survey will become a reference point for researchers and practitioners alike when considering the potential application of temporal aspects in their personalized applications. |
| Deep Learning to Encourage Citizen Involvement in Local Journalism Book Chapter Tessem, B., Nyre, L., Mesquita, M.d.S., Mulholland, P. In: Mari K. Niemi Ville J. E. Manninen, Anthony Ridge-Newman (Ed.): Chapter 3, pp. 211-226, Palgrave Macmillan Cham, 2022. @inbook{Tessem2022,
title = {Deep Learning to Encourage Citizen Involvement in Local Journalism},
author = {Tessem, B., Nyre, L., Mesquita, M.d.S., Mulholland, P.},
editor = {Ville J. E. Manninen, Mari K. Niemi, Anthony Ridge-Newman},
url = {https://link.springer.com/chapter/10.1007/978-3-030-95073-6_14},
doi = {https://doi.org/10.1007/978-3-030-95073-6_14},
year = {2022},
date = {2022-05-05},
urldate = {2022-05-05},
pages = {211-226},
publisher = {Palgrave Macmillan Cham},
chapter = {3},
abstract = {We discuss the potential of a mobile app for news tips to local newspapers to be augmented with artificial intelligence. It can be designed to encourage deliberative, consensus-oriented contributions from citizens. We presume that such an app will generate news stories from multi-modal data in the form of photos, videos, text elements, location information, and the identity of the contributor. Three scenarios are presented to show how image recognition, natural language processing, narrative construction, and other AI technologies can be applied. The scenarios address three interrelated challenges for local journalism. First, text and photos in tips are often of low quality for journalism purposes. Second, peer-to-peer dialogue about local news takes place in social media instead of in the newspaper. Third, readers lack news literacy and are prone to confrontational debates and trolling. We show how advances in deep learning technology makes it possible to propose solutions to these problems.},
keywords = {New},
pubstate = {published},
tppubtype = {inbook}
}
We discuss the potential of a mobile app for news tips to local newspapers to be augmented with artificial intelligence. It can be designed to encourage deliberative, consensus-oriented contributions from citizens. We presume that such an app will generate news stories from multi-modal data in the form of photos, videos, text elements, location information, and the identity of the contributor. Three scenarios are presented to show how image recognition, natural language processing, narrative construction, and other AI technologies can be applied. The scenarios address three interrelated challenges for local journalism. First, text and photos in tips are often of low quality for journalism purposes. Second, peer-to-peer dialogue about local news takes place in social media instead of in the newspaper. Third, readers lack news literacy and are prone to confrontational debates and trolling. We show how advances in deep learning technology makes it possible to propose solutions to these problems. |
A Collaborative System of Flying and Ground Robots with Universal Physical Coupling Interface (PCI), and the Potential Interactive Applications Conference Ziming Wang, Ziyi Hu, Yemao Man, Morten Fjeld A Collaborative System of Flying and Ground Robots with Universal Physical Coupling Interface (PCI), and the Potential Interactive Applications, 2022. @conference{Wang2022,
title = {A Collaborative System of Flying and Ground Robots with Universal Physical Coupling Interface (PCI), and the Potential Interactive Applications},
author = {Ziming Wang, Ziyi Hu, Yemao Man, Morten Fjeld},
year = {2022},
date = {2022-04-29},
urldate = {2022-04-29},
booktitle = {A Collaborative System of Flying and Ground Robots with Universal Physical Coupling Interface (PCI), and the Potential Interactive Applications},
abstract = {Flying and ground robots complement each other in terms of their advantages and disadvantages. We propose a collaborative system combining flying and ground robots, using a universal physical coupling interface (PCI) that allows for momentary connections and disconnections between multiple robots/devices. The proposed system may better utilize the complementary advantages of both flying and ground robots. We also describe various potential scenarios where such a system could be of benefit to interact with humans - namely, remote field works and rescue missions, transportation, healthcare, and education. Finally, we discuss the opportunities and challenges of such systems and consider deeper questions which should be studied in future work.},
keywords = {New, WP4: Media Content Interaction and Accessibility},
pubstate = {published},
tppubtype = {conference}
}
Flying and ground robots complement each other in terms of their advantages and disadvantages. We propose a collaborative system combining flying and ground robots, using a universal physical coupling interface (PCI) that allows for momentary connections and disconnections between multiple robots/devices. The proposed system may better utilize the complementary advantages of both flying and ground robots. We also describe various potential scenarios where such a system could be of benefit to interact with humans - namely, remote field works and rescue missions, transportation, healthcare, and education. Finally, we discuss the opportunities and challenges of such systems and consider deeper questions which should be studied in future work. |
RedirectedDoors: Redirection While Opening Doors in Virtual Reality Conference Morten Fjeld, Yukai Hoshikawa, Kazuyuki Fujita, Kazuki Takashima, Yoshifumi Kitamura RedirectedDoors: Redirection While Opening Doors in Virtual Reality., 2022. @conference{Fjeld2022,
title = {RedirectedDoors: Redirection While Opening Doors in Virtual Reality},
author = {Morten Fjeld, Yukai Hoshikawa, Kazuyuki Fujita, Kazuki Takashima, Yoshifumi Kitamura },
year = {2022},
date = {2022-03-12},
urldate = {2022-03-12},
booktitle = {RedirectedDoors: Redirection While Opening Doors in Virtual Reality.},
abstract = {We propose RedirectedDoors, a novel technique for redirection in VR focused on door-opening behavior. This technique manipulates the user's walking direction by rotating the entire virtual environment at a certain angular ratio of the door being opened, while the virtual door's position is kept unmanipulated to ensure door-opening realism. Results of a user study using two types of door-opening interfaces (with and without a passive haptic prop) revealed that the estimated detection thresholds generally showed a higher space efficiency of redirection. Following the results, we derived usage guidelines for our technique that provide lower noticeability and higher acceptability.},
keywords = {New, Virtual Reality, WP4: Media Content Interaction and Accessibility},
pubstate = {published},
tppubtype = {conference}
}
We propose RedirectedDoors, a novel technique for redirection in VR focused on door-opening behavior. This technique manipulates the user's walking direction by rotating the entire virtual environment at a certain angular ratio of the door being opened, while the virtual door's position is kept unmanipulated to ensure door-opening realism. Results of a user study using two types of door-opening interfaces (with and without a passive haptic prop) revealed that the estimated detection thresholds generally showed a higher space efficiency of redirection. Following the results, we derived usage guidelines for our technique that provide lower noticeability and higher acceptability. |
| Developing and Evaluating a University Recommender System Journal Article Mehdi Elahi; Alain D. Starke; Nabil El Ioini; Anna Alexander Lambrix; Christoph Trattner In: Frontiers in Artificial Intelligence , 2022. @article{Elahi2022,
title = {Developing and Evaluating a University Recommender System},
author = {Mehdi Elahi and Alain D. Starke and Nabil El Ioini and Anna Alexander Lambrix and Christoph Trattner},
url = {https://www.frontiersin.org/articles/10.3389/frai.2021.796268/full},
doi = {https://doi.org/10.3389/frai.2021.796268},
year = {2022},
date = {2022-02-02},
journal = {Frontiers in Artificial Intelligence },
abstract = {A challenge for many young adults is to find the right institution to follow higher education. Global university rankings are a commonly used, but inefficient tool, for they do not consider a person's preferences and needs. For example, some persons pursue prestige in their higher education, while others prefer proximity. This paper develops and evaluates a university recommender system, eliciting user preferences as ratings to build predictive models and to generate personalized university ranking lists. In Study 1, we performed offline evaluation on a rating dataset to determine which recommender approaches had the highest predictive value. In Study 2, we selected three algorithms to produce different university recommendation lists in our online tool, asking our users to compare and evaluate them in terms of different metrics (Accuracy, Diversity, Perceived Personalization, Satisfaction, and Novelty). We show that a SVD algorithm scores high on accuracy and perceived personalization, while a KNN algorithm scores better on novelty. We also report findings on preferred university features.},
keywords = {New, WP2: User Modeling Personalization and Engagement},
pubstate = {published},
tppubtype = {article}
}
A challenge for many young adults is to find the right institution to follow higher education. Global university rankings are a commonly used, but inefficient tool, for they do not consider a person's preferences and needs. For example, some persons pursue prestige in their higher education, while others prefer proximity. This paper develops and evaluates a university recommender system, eliciting user preferences as ratings to build predictive models and to generate personalized university ranking lists. In Study 1, we performed offline evaluation on a rating dataset to determine which recommender approaches had the highest predictive value. In Study 2, we selected three algorithms to produce different university recommendation lists in our online tool, asking our users to compare and evaluate them in terms of different metrics (Accuracy, Diversity, Perceived Personalization, Satisfaction, and Novelty). We show that a SVD algorithm scores high on accuracy and perceived personalization, while a KNN algorithm scores better on novelty. We also report findings on preferred university features. |
| Hybrid Recommendation of Movies based on Deep Content Features Inproceedings Tord Kvifte; Mehdi Elahi; Christoph Trattner In: Springer Nature, 2022. @inproceedings{cristin1957037,
title = {Hybrid Recommendation of Movies based on Deep Content Features},
author = {Tord Kvifte and Mehdi Elahi and Christoph Trattner},
url = {https://app.cristin.no/results/show.jsf?id=1957037, Cristin
https://aip-research-center.github.io/AIPA_workshop/2021/},
year = {2022},
date = {2022-01-01},
booktitle = {Springer Nature},
abstract = {When a movie is uploaded to a movie Recommender System (e.g., YouTube), the system can exploit various forms of descriptive features (e.g., tags and genre) in order to generate personalized recommendation for users. However, there are situations where the descriptive features are missing or very limited and the system may fail to include such a movie in the recommendation list. This paper investigates hybrid recommendation based on a novel form of content features, extracted from movies, in order to generate recommendation for users. Such features represent the visual aspects of movies, based on Deep Learning models, and hence, do not require any human annotation when extracted. We have evaluated our proposed technique using a large dataset of movies and shown that automatically extracted visual features can mitigate the cold-start problem by generating recommendation with a superior quality compared to different baselines, including recommendation based on human-annotated features.},
keywords = {Cristin},
pubstate = {published},
tppubtype = {inproceedings}
}
When a movie is uploaded to a movie Recommender System (e.g., YouTube), the system can exploit various forms of descriptive features (e.g., tags and genre) in order to generate personalized recommendation for users. However, there are situations where the descriptive features are missing or very limited and the system may fail to include such a movie in the recommendation list. This paper investigates hybrid recommendation based on a novel form of content features, extracted from movies, in order to generate recommendation for users. Such features represent the visual aspects of movies, based on Deep Learning models, and hence, do not require any human annotation when extracted. We have evaluated our proposed technique using a large dataset of movies and shown that automatically extracted visual features can mitigate the cold-start problem by generating recommendation with a superior quality compared to different baselines, including recommendation based on human-annotated features. |
| Service-Oriented Computing - ICSOC 2021 Workshops - AIOps, STRAPS, AI-PA, and Satellite Events, Dubai, United Arab Emirates, November 22-25, 2021, Proceedings. Lecture Notes in Computer Science. Proceeding Hakim Hacid; Monther Aldwairi; Mohamed Reda Bouadjenek; Marinella Petrocchi; Noura Faci; Fatma Outay; Amin Beheshti; Lauritz Thamsen; Hai Dong 2022. @proceedings{cristin1957036,
title = {Service-Oriented Computing - ICSOC 2021 Workshops - AIOps, STRAPS, AI-PA, and Satellite Events, Dubai, United Arab Emirates, November 22-25, 2021, Proceedings. Lecture Notes in Computer Science.},
author = {Hakim Hacid and Monther Aldwairi and Mohamed Reda Bouadjenek and Marinella Petrocchi and Noura Faci and Fatma Outay and Amin Beheshti and Lauritz Thamsen and Hai Dong},
url = {https://app.cristin.no/results/show.jsf?id=1957036, Cristin
https://link.springer.com/conference/icsoc},
year = {2022},
date = {2022-01-01},
keywords = {Cristin},
pubstate = {published},
tppubtype = {proceedings}
}
|
2021
|
| Responsible media technology and AI: challenges and research directions Journal Article Christoph Trattner; Dietmar Jannach; Enrico Motta; Irene Costera Meijer; Nicholas Diakopoulos; Mehdi Elahi; Andreas Lothe Opdahl; Bjørnar Tessem; Njål Trygve Borch; Morten Fjeld; Lilja Øvrelid; Koenraad De Smedt; Hallvard Moe In: AI and Ethics, 2021. @article{cristin2000622,
title = {Responsible media technology and AI: challenges and research directions},
author = {Christoph Trattner and Dietmar Jannach and Enrico Motta and Irene Costera Meijer and Nicholas Diakopoulos and Mehdi Elahi and Andreas Lothe Opdahl and Bjørnar Tessem and Njål Trygve Borch and Morten Fjeld and Lilja Øvrelid and Koenraad De Smedt and Hallvard Moe},
url = {https://app.cristin.no/results/show.jsf?id=2000622, Cristin
https://link.springer.com/content/pdf/10.1007/s43681-021-00126-4.pdf},
doi = {https://doi.org/10.1007/s43681-021-00126-4},
year = {2021},
date = {2021-12-20},
journal = {AI and Ethics},
keywords = {Cristin},
pubstate = {published},
tppubtype = {article}
}
|
| Towards Responsible Media Recommendation Journal Article Mehdi Elahi; Dietmar Jannach; Lars Skjærven; Erik Knudsen; Helle Sjøvaag; Kristian Tolonen; Øyvind Holmstad; Igor Pipkin; Eivind Throndsen; Agnes Stenbom; Eivind Fiskerud; Adrian Oesch; Loek Vredenberg; Christoph Trattner In: AI and Ethics, 2021. @article{cristin1953352,
title = {Towards Responsible Media Recommendation},
author = {Mehdi Elahi and Dietmar Jannach and Lars Skjærven and Erik Knudsen and Helle Sjøvaag and Kristian Tolonen and Øyvind Holmstad and Igor Pipkin and Eivind Throndsen and Agnes Stenbom and Eivind Fiskerud and Adrian Oesch and Loek Vredenberg and Christoph Trattner},
url = {https://app.cristin.no/results/show.jsf?id=1953352, Cristin
https://link.springer.com/article/10.1007%2Fs43681-021-00107-7},
doi = {https://doi.org/10.1007/s43681-021-00107-7},
year = {2021},
date = {2021-11-02},
journal = {AI and Ethics},
keywords = {Cristin},
pubstate = {published},
tppubtype = {article}
}
|
| Når kunstig intelligens inntar redaksjonen Medium Bjørnar Tessem 2021. @media{cristin1942282,
title = {Når kunstig intelligens inntar redaksjonen},
author = {Bjørnar Tessem},
url = {https://app.cristin.no/results/show.jsf?id=1942282, Cristin},
year = {2021},
date = {2021-10-01},
keywords = {Cristin},
pubstate = {published},
tppubtype = {media}
}
|
| WP3 2021 M3.1 Report The industrial expectations to, needs from and wishes for the work package Technical Report Are Tverberg, Ingrid Agasøster, Mads Grønbæck, Marius Monsen, Robert Strand, Kristian Eikeland, Eivind Throndsen, Lars Westvang, Tove B. Knudsen, Eivind Fiskerud, Rune Skår, Sergej Stoppel, Arne Berven, Glenn Skare Pedersen, Paul Macklin, Kenneth Cuomo, Loek Vredenberg, Kristian Tolonen,
Andreas L Opdahl, Bjørnar Tessem, Csaba Veres, Duc Tien Dang Nguyen, Enrico Motta, Vinay Jayarama Setty University of Bergen, MediaFutures 2021. @techreport{Tverberg2021,
title = {WP3 2021 M3.1 Report The industrial expectations to, needs from and wishes for the work package},
author = {Are Tverberg, Ingrid Agasøster, Mads Grønbæck, Marius Monsen, Robert Strand, Kristian Eikeland, Eivind Throndsen, Lars Westvang, Tove B. Knudsen, Eivind Fiskerud, Rune Skår, Sergej Stoppel, Arne Berven, Glenn Skare Pedersen, Paul Macklin, Kenneth Cuomo, Loek Vredenberg, Kristian Tolonen,
Andreas L Opdahl, Bjørnar Tessem, Csaba Veres, Duc Tien Dang Nguyen, Enrico Motta, Vinay Jayarama Setty},
url = {https://mediafutures.no/wp3-q2-2021-m3-1-report-by-the-industrial-partners-final-2/},
year = {2021},
date = {2021-07-25},
institution = {University of Bergen, MediaFutures},
keywords = {New, WP3: Media Content Production and Analysis},
pubstate = {published},
tppubtype = {techreport}
}
|
| VXSlate: Exploring Combination of Head Movements and Mobile Touch for Large Virtual Display Interaction Proceeding Khanh-Duy Le; Tanh Quang Tran; Karol Chlasta; Krzysztof Krejtz; Morten Fjeld; Andreas Kunz Association for Computing Machinery, New York, NY, USA, 2021, ISBN: 978-1-4503-8476-6. @proceedings{Kunz2021,
title = {VXSlate: Exploring Combination of Head Movements and Mobile Touch for Large Virtual Display Interaction},
author = {Khanh-Duy Le and Tanh Quang Tran and Karol Chlasta and Krzysztof Krejtz and Morten Fjeld and Andreas Kunz},
doi = {https://doi.org/10.1145/3461778.3462076},
isbn = {978-1-4503-8476-6},
year = {2021},
date = {2021-06-28},
journal = {DIS '21: Designing Interactive Systems Conference 2021},
pages = {283–297},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
keywords = {WP4: Media Content Interaction and Accessibility},
pubstate = {published},
tppubtype = {proceedings}
}
|
| Conversational Futures: Emancipating Conversational Interactions for Futures Worth Wanting Conference Minha Lee; Renee Noortman; Cristina Zaga; Alain D. Starke; Gijs Huisman; Kristina Andersen no. May 2021, 2021. @conference{Lee2021,
title = {Conversational Futures: Emancipating Conversational Interactions for Futures Worth Wanting},
author = {Minha Lee and Renee Noortman and Cristina Zaga and Alain D. Starke and Gijs Huisman and Kristina Andersen},
url = {https://minha-lee.github.io/files/mlee_Conversational_Futures_CHI2021.pdf},
year = {2021},
date = {2021-05-13},
number = {May 2021},
pages = {1-13},
abstract = {We present a vision for conversational user interfaces (CUIs) asprobes forspeculating with, rather than as objects to speculateabout. Popular CUIs, e.g., Alexa, are changing the way we converse,narrate, and imagine the world(s) to come. Yet, current conversa-tional interactions normatively may promote non-desirable ends,delivering a restricted range of request-response interactions withsexist and digital colonialist tendencies. Our critical design ap-proach envisions alternatives by considering how future voices canreside in CUIs as enabling probes. We present novel explorationsthat illustrate the potential of CUIs as critical design material, bycritiquing present norms and conversing with imaginary species.As micro-level interventions, we show that conversationswithdi-verse futuresthroughCUIs can persuade us to critically shape ourdiscourse on macro-scale concerns of the present, e.g., sustainabil-ity. We reflect on how conversational interactions with pluralistic,imagined futures can contribute to howbeing humanstands tochange.},
keywords = {Conversational user interfaces, critical design, design fiction, futuring, speculative design},
pubstate = {published},
tppubtype = {conference}
}
We present a vision for conversational user interfaces (CUIs) asprobes forspeculating with, rather than as objects to speculateabout. Popular CUIs, e.g., Alexa, are changing the way we converse,narrate, and imagine the world(s) to come. Yet, current conversa-tional interactions normatively may promote non-desirable ends,delivering a restricted range of request-response interactions withsexist and digital colonialist tendencies. Our critical design ap-proach envisions alternatives by considering how future voices canreside in CUIs as enabling probes. We present novel explorationsthat illustrate the potential of CUIs as critical design material, bycritiquing present norms and conversing with imaginary species.As micro-level interventions, we show that conversationswithdi-verse futuresthroughCUIs can persuade us to critically shape ourdiscourse on macro-scale concerns of the present, e.g., sustainabil-ity. We reflect on how conversational interactions with pluralistic,imagined futures can contribute to howbeing humanstands tochange. |
| Nudging Healthy Choices in Food Search Through Visual Attractiveness Journal Article Alain D. Starke; Martijn C. Willemsen; Christoph Trattner In: no. April 2021, pp. 1-18, 2021. @article{Starke2021,
title = {Nudging Healthy Choices in Food Search Through Visual Attractiveness},
author = {Alain D. Starke and Martijn C. Willemsen and Christoph Trattner},
url = {https://www.frontiersin.org/articles/10.3389/frai.2021.621743/full},
doi = {10.3389/frai.2021.621743},
year = {2021},
date = {2021-04-22},
number = {April 2021},
pages = {1-18},
abstract = {Recipe websites are becoming increasingly popular to support people in their home cooking. However, most of these websites prioritize popular recipes, which tend to be unhealthy. Drawing upon research on visual biases and nudges, this paper investigates whether healthy food choices can be supported in food search by depicting attractive images alongside recipes, as well as by re-ranking search results on health. After modelling the visual attractiveness of recipe images, we asked 239 users to search for specific online recipes and to select those they liked the most. Our analyses revealed that users tended to choose a healthier recipe if a visually attractive image was depicted alongside it, as well as if it was listed at the top of a list of search results. Even though less popular recipes were promoted this way, it did not come at the cost of a user’s level of satisfaction},
keywords = {WP2: User Modeling Personalization and Engagement},
pubstate = {published},
tppubtype = {article}
}
Recipe websites are becoming increasingly popular to support people in their home cooking. However, most of these websites prioritize popular recipes, which tend to be unhealthy. Drawing upon research on visual biases and nudges, this paper investigates whether healthy food choices can be supported in food search by depicting attractive images alongside recipes, as well as by re-ranking search results on health. After modelling the visual attractiveness of recipe images, we asked 239 users to search for specific online recipes and to select those they liked the most. Our analyses revealed that users tended to choose a healthier recipe if a visually attractive image was depicted alongside it, as well as if it was listed at the top of a list of search results. Even though less popular recipes were promoted this way, it did not come at the cost of a user’s level of satisfaction |
| Exploring Multi-List User Interfaces for Similar-Item Recommendations Conference Dietmar Jannach; Mathias Jesse; Michael Jugovac; Christoph Trattner 29th ACM International Conference on User Modeling, Adaptation and Personalization (UMAP '21) 2021. @conference{Jannach2021,
title = {Exploring Multi-List User Interfaces for Similar-Item Recommendations},
author = {Dietmar Jannach and Mathias Jesse and Michael Jugovac and Christoph Trattner},
url = {https://mediafutures.no/conference_umap_2021-2/},
year = {2021},
date = {2021-03-26},
organization = {29th ACM International Conference on User Modeling, Adaptation and Personalization (UMAP '21)},
abstract = {On many e-commerce and media streaming sites, the user inter-face (UI) consists of multiple lists of item suggestions. The itemsin each list are usually chosen based on pre-defined strategies and,e.g., show movies of the same genre or category. Such interfacesare common in practice, but there is almost no academic researchregarding the optimal design and arrangement of such multi-listUIs for recommenders. In this paper, we report the results of anexploratory user study that examined the effects of various designalternatives on the decision-making behavior of users in the con-text of similar-item recommendations. Our investigations showed,among other aspects, that decision-making is slower and more de-manding with multi-list interfaces, but that users also explore moreoptions before making a decision. Regarding the selection of thealgorithm to retrieve similar items, our study furthermore revealsthe importance of considering social-based similarity measures.},
keywords = {Recommender system, User Interface, User Study},
pubstate = {published},
tppubtype = {conference}
}
On many e-commerce and media streaming sites, the user inter-face (UI) consists of multiple lists of item suggestions. The itemsin each list are usually chosen based on pre-defined strategies and,e.g., show movies of the same genre or category. Such interfacesare common in practice, but there is almost no academic researchregarding the optimal design and arrangement of such multi-listUIs for recommenders. In this paper, we report the results of anexploratory user study that examined the effects of various designalternatives on the decision-making behavior of users in the con-text of similar-item recommendations. Our investigations showed,among other aspects, that decision-making is slower and more de-manding with multi-list interfaces, but that users also explore moreoptions before making a decision. Regarding the selection of thealgorithm to retrieve similar items, our study furthermore revealsthe importance of considering social-based similarity measures. |
| Automatiske nyhende Online Lars Nyre; Bjørnar Tessem Dag og Tid 2021, visited: 26.03.2021. @online{Tessem2021,
title = {Automatiske nyhende},
author = {Lars Nyre and Bjørnar Tessem},
url = {https://www.dagogtid.no/feature/automatiske-nyhende-6.3.20626.2396ffd8e5},
year = {2021},
date = {2021-03-26},
urldate = {2021-03-26},
journal = {Dag og Tid},
number = {12},
organization = {Dag og Tid},
keywords = {WP3: Media Content Production and Analysis},
pubstate = {published},
tppubtype = {online}
}
|
| VXSlate: Combining Head Movement and Mobile Touch for Large Virtual Display Interaction Conference Khanh-Duy Le; Tanh Quang Tran; Karol Chlasta; Krzysztof Krejtz; Morten Fjeld; Andreas Kunz 2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW). IEEE The Institute of Electrical and Electronics Engineers, Inc., 2021. @conference{Le2021b,
title = {VXSlate: Combining Head Movement and Mobile Touch for Large Virtual Display Interaction},
author = {Khanh-Duy Le and Tanh Quang Tran and Karol Chlasta and Krzysztof Krejtz and Morten Fjeld and Andreas Kunz},
url = {https://conferences.computer.org/vrpub/pdfs/VRW2021-2ANNoldm4A10Ml9f63uYC9/136700a528/136700a528.pdf
https://www.youtube.com/watch?v=N8ZJlKWj4mk&ab_channel=DuyL%C3%AAKh%C3%A1nh},
doi = { 10.1109/VRW52623.2021.00146},
year = {2021},
date = {2021-02-12},
pages = {528-529},
publisher = {IEEE The Institute of Electrical and Electronics Engineers, Inc.},
organization = {2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW).},
abstract = {Virtual Reality (VR) headsets can open opportunities for users to accomplish complex tasks on large virtual displays, using compact setups. However, interacting with large virtual displays using existing interaction techniques might cause fatigue, especially for precise manipulations, due to the lack of physical surfaces. We designed VXSlate, an interaction technique that uses a large virtual display, as an expansion of a tablet. VXSlate combines a user’s head movements, as tracked by the VR headset, and touch interaction on the tablet. The user’s head movements position both a virtual representation of the tablet and of the user’s hand on the large virtual display. The user’s multi-touch interactions perform finely-tuned content manipulations.},
keywords = {Human computer interaction, Human-centered computing, Interaction techniques, SFI MediaFutures, Virtual Reality, WP4: Media Content Interaction and Accessibility},
pubstate = {published},
tppubtype = {conference}
}
Virtual Reality (VR) headsets can open opportunities for users to accomplish complex tasks on large virtual displays, using compact setups. However, interacting with large virtual displays using existing interaction techniques might cause fatigue, especially for precise manipulations, due to the lack of physical surfaces. We designed VXSlate, an interaction technique that uses a large virtual display, as an expansion of a tablet. VXSlate combines a user’s head movements, as tracked by the VR headset, and touch interaction on the tablet. The user’s head movements position both a virtual representation of the tablet and of the user’s hand on the large virtual display. The user’s multi-touch interactions perform finely-tuned content manipulations. |
| Framing Protest in Online News and Readers’ Comments: The Case of Serbian Protest “Against Dictatorship” Journal Article Jelena Kleut; Ana Milojevic In: International Journal of Communication, vol. 15, no. 21, pp. 82-102, 2021, (Pre SFI). @article{Kleut2021,
title = {Framing Protest in Online News and Readers’ Comments: The Case of Serbian Protest “Against Dictatorship”},
author = {Jelena Kleut and Ana Milojevic},
url = {https://www.researchgate.net/publication/348787747_Framing_Protest_in_Online_News_and_Readers\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'_Comments_The_Case_of_Serbian_Protest_Against_Dictatorship},
year = {2021},
date = {2021-01-01},
journal = {International Journal of Communication},
volume = {15},
number = {21},
pages = {82-102},
series = {Not connected to SFI MediaFutures},
abstract = {This research examines the "protest paradigm" in the digital news environment of a politically polarized media system by considering relations between news and online readers' comments about the Serbian protest Against Dictatorship, which was held in 2017. Applying content analysis to news and comments from two news websites, our study indicates the need to account for opposing framing of the protest (violence/peacefulness, de/legitimizing and un/democratic) in a polarized environment. The results show that the distribution of opposing frames is guided by the media relations with the government. Online readers' comments generally enhance this polarized pattern of frame distribution, with the exception of the performance frame, which remains prolific in the media, but absent from readers' comments.},
note = {Pre SFI},
keywords = {framing, Online Media, polarized media system, protest paradigm, user comments, WP1: Understanding Media Experiences},
pubstate = {published},
tppubtype = {article}
}
This research examines the "protest paradigm" in the digital news environment of a politically polarized media system by considering relations between news and online readers' comments about the Serbian protest Against Dictatorship, which was held in 2017. Applying content analysis to news and comments from two news websites, our study indicates the need to account for opposing framing of the protest (violence/peacefulness, de/legitimizing and un/democratic) in a polarized environment. The results show that the distribution of opposing frames is guided by the media relations with the government. Online readers' comments generally enhance this polarized pattern of frame distribution, with the exception of the performance frame, which remains prolific in the media, but absent from readers' comments. |
| Auka røynd (AR) Medium Lars Nyre; Bjørnar Tessem Dag og Tid, 2021. @media{cristin1894361,
title = {Auka røynd (AR)},
author = {Lars Nyre and Bjørnar Tessem},
url = {https://app.cristin.no/results/show.jsf?id=1894361, Cristin},
year = {2021},
date = {2021-01-01},
howpublished = {Dag og Tid},
keywords = {Cristin},
pubstate = {published},
tppubtype = {media}
}
|
| Ting i internettet Medium Lars Nyre; Bjørnar Tessem Dag og Tid, 2021. @media{cristin1931531,
title = {Ting i internettet},
author = {Lars Nyre and Bjørnar Tessem},
url = {https://app.cristin.no/results/show.jsf?id=1931531, Cristin},
year = {2021},
date = {2021-01-01},
howpublished = {Dag og Tid},
keywords = {Cristin},
pubstate = {published},
tppubtype = {media}
}
|
| Automatiske nyhende Medium Lars Nyre; Bjørnar Tessem Dag og Tid, 2021. @media{cristin1905817,
title = {Automatiske nyhende},
author = {Lars Nyre and Bjørnar Tessem},
url = {https://app.cristin.no/results/show.jsf?id=1905817, Cristin},
year = {2021},
date = {2021-01-01},
howpublished = {Dag og Tid},
keywords = {Cristin},
pubstate = {published},
tppubtype = {media}
}
|
| EN ANALYSE AV SAMMENHENGEN MELLOM BRUK AV NRKS DIGITALE NYHETSTILBUD OG BETALINGSVILJE FOR DIGITALE NYHETER Technical Report Erik Knudsen; Hallvard Moe 2021. @techreport{cristin1958452,
title = {EN ANALYSE AV SAMMENHENGEN MELLOM BRUK AV NRKS DIGITALE NYHETSTILBUD OG BETALINGSVILJE FOR DIGITALE NYHETER},
author = {Erik Knudsen and Hallvard Moe},
url = {https://app.cristin.no/results/show.jsf?id=1958452, Cristin},
year = {2021},
date = {2021-01-01},
keywords = {Cristin},
pubstate = {published},
tppubtype = {techreport}
}
|
| Using Gender- and Polarity-informed Models to Investigate Bias Working paper Samia Touileb; Lilja Øvrelid; Erik Velldal 2021. @workingpaper{cristin1958571,
title = {Using Gender- and Polarity-informed Models to Investigate Bias},
author = {Samia Touileb and Lilja Øvrelid and Erik Velldal},
url = {https://app.cristin.no/results/show.jsf?id=1958571, Cristin},
year = {2021},
date = {2021-01-01},
keywords = {Cristin},
pubstate = {published},
tppubtype = {workingpaper}
}
|
| Novel Methods Using Human Emotion and Visual Features for Recommending Movies Masters Thesis Mehdi Elahi; Øyvind Johannessen Universitetet i Bergen, 2021. @mastersthesis{cristin1957008,
title = {Novel Methods Using Human Emotion and Visual Features for Recommending Movies},
author = {Mehdi Elahi and Øyvind Johannessen},
url = {https://app.cristin.no/results/show.jsf?id=1957008, Cristin},
year = {2021},
date = {2021-01-01},
school = {Universitetet i Bergen},
abstract = {This master thesis investigates novel methods using human emotion as contextual information to estimate and elicit ratings when watching movie trailers. The aim is to acquire user preferences without the intrusive and time-consuming behavior of Explicit Feedback strategies, and generate quality recommendations. The proposed preference-elicitation technique is implemented as an Emotion-based Filtering technique (EF) to generate recommendations, and is evaluated against two other recommendation techniques. One Visual-based Filtering technique, using low-level visual features of movies, and one Collaborative Filtering (CF) using explicit ratings. In terms of Accuracy, we found the Emotion-based Filtering technique (EF) to perform better than the two other filtering techniques. In terms of Diversity, the Visual-based Filtering (VF) performed best. We further analyse the obtained data to see if movie genres tend to induce specific emotions, and the potential correlation between emotional responses of users and visual features of movie trailers. When investigating emotional responses, we found that joy and disgust tend to be more prominent in movie genres than other emotions. Our findings also suggest potential correlations on a per movie level. The proposed Emotion-based Filtering technique can be adopted as an Implicit Feedback strategy to obtain user preferences. For future work, we will extend the experiment with more participants and build stronger affective profiles to be studied when recommending movies.},
keywords = {Cristin},
pubstate = {published},
tppubtype = {mastersthesis}
}
This master thesis investigates novel methods using human emotion as contextual information to estimate and elicit ratings when watching movie trailers. The aim is to acquire user preferences without the intrusive and time-consuming behavior of Explicit Feedback strategies, and generate quality recommendations. The proposed preference-elicitation technique is implemented as an Emotion-based Filtering technique (EF) to generate recommendations, and is evaluated against two other recommendation techniques. One Visual-based Filtering technique, using low-level visual features of movies, and one Collaborative Filtering (CF) using explicit ratings. In terms of Accuracy, we found the Emotion-based Filtering technique (EF) to perform better than the two other filtering techniques. In terms of Diversity, the Visual-based Filtering (VF) performed best. We further analyse the obtained data to see if movie genres tend to induce specific emotions, and the potential correlation between emotional responses of users and visual features of movie trailers. When investigating emotional responses, we found that joy and disgust tend to be more prominent in movie genres than other emotions. Our findings also suggest potential correlations on a per movie level. The proposed Emotion-based Filtering technique can be adopted as an Implicit Feedback strategy to obtain user preferences. For future work, we will extend the experiment with more participants and build stronger affective profiles to be studied when recommending movies. |
| Unify Media and UX with timed variables Working paper Ingar M Arntzen; Njål Trygve Borch; Anders Andersen 2021. @workingpaper{cristin1959749,
title = {Unify Media and UX with timed variables},
author = {Ingar M Arntzen and Njål Trygve Borch and Anders Andersen},
url = {https://app.cristin.no/results/show.jsf?id=1959749, Cristin},
year = {2021},
date = {2021-01-01},
keywords = {Cristin},
pubstate = {published},
tppubtype = {workingpaper}
}
|
| Video Recommendations Based on Visual Features Extracted with Deep Learning Masters Thesis Mehdi Elahi; Tord Kvifte Universitetet i Bergen, 2021. @mastersthesis{cristin1956990,
title = {Video Recommendations Based on Visual Features Extracted with Deep Learning},
author = {Mehdi Elahi and Tord Kvifte},
url = {https://app.cristin.no/results/show.jsf?id=1956990, Cristin
https://hdl.handle.net/11250/2760300},
year = {2021},
date = {2021-01-01},
school = {Universitetet i Bergen},
abstract = {When a movie is uploaded to a movie Recommender System (e.g., YouTube), the system can exploit various forms of descriptive features (e.g., tags and genre) in order to generate personalized recommendation for users. However, there are situations where the descriptive features are missing or very limited and the system may fail to include such a movie in the recommendation list, known as Cold-start problem. This thesis investigates recommendation based on a novel form of content features, extracted from movies, in order to generate recommendation for users. Such features represent the visual aspects of movies, based on Deep Learning models, and hence, do not require any human annotation when extracted. The proposed technique has been evaluated in both offline and online evaluations using a large dataset of movies. The online evaluation has been carried out in a evaluation framework developed for this thesis. Results from the offline and online evaluation (N=150) show that automatically extracted visual features can mitigate the cold-start problem by generating recommendation with a superior quality compared to different baselines, including recommendation based on human-annotated features. The results also point to subtitles as a high-quality future source of automatically extracted features. The visual feature dataset, named DeepCineProp13K and the subtitle dataset, CineSub3K, as well as the proposed evaluation framework are all made openly available online in a designated Github repository.},
keywords = {Cristin},
pubstate = {published},
tppubtype = {mastersthesis}
}
When a movie is uploaded to a movie Recommender System (e.g., YouTube), the system can exploit various forms of descriptive features (e.g., tags and genre) in order to generate personalized recommendation for users. However, there are situations where the descriptive features are missing or very limited and the system may fail to include such a movie in the recommendation list, known as Cold-start problem. This thesis investigates recommendation based on a novel form of content features, extracted from movies, in order to generate recommendation for users. Such features represent the visual aspects of movies, based on Deep Learning models, and hence, do not require any human annotation when extracted. The proposed technique has been evaluated in both offline and online evaluations using a large dataset of movies. The online evaluation has been carried out in a evaluation framework developed for this thesis. Results from the offline and online evaluation (N=150) show that automatically extracted visual features can mitigate the cold-start problem by generating recommendation with a superior quality compared to different baselines, including recommendation based on human-annotated features. The results also point to subtitles as a high-quality future source of automatically extracted features. The visual feature dataset, named DeepCineProp13K and the subtitle dataset, CineSub3K, as well as the proposed evaluation framework are all made openly available online in a designated Github repository. |
| Beyond Algorithmic Fairness in Recommender System Working paper Mehdi Elahi; Himan Abdollahpouri; Masoud Mansoury; Helma Torkamaan 2021. @workingpaper{cristin1957035,
title = {Beyond Algorithmic Fairness in Recommender System},
author = {Mehdi Elahi and Himan Abdollahpouri and Masoud Mansoury and Helma Torkamaan},
url = {https://app.cristin.no/results/show.jsf?id=1957035, Cristin
https://dl.acm.org/doi/abs/10.1145/3450614.3461685},
year = {2021},
date = {2021-01-01},
keywords = {Cristin},
pubstate = {published},
tppubtype = {workingpaper}
}
|
| Enhanced Movie Recommendation Incorporating Visual Features Working paper Mehdi Elahi; Farshad Bakhshandegan Moghaddam; Reza Hosseini; Mohammad Hossein Rimaz; Christoph Trattner 2021. @workingpaper{cristin1957034,
title = {Enhanced Movie Recommendation Incorporating Visual Features},
author = {Mehdi Elahi and Farshad Bakhshandegan Moghaddam and Reza Hosseini and Mohammad Hossein Rimaz and Christoph Trattner},
url = {https://app.cristin.no/results/show.jsf?id=1957034, Cristin},
year = {2021},
date = {2021-01-01},
keywords = {Cristin},
pubstate = {published},
tppubtype = {workingpaper}
}
|
| Medieundersøkelsen 2021: Har koronadekningen svekket tilliten til mediene? Presentation Erik Knudsen Nordiske Mediedager 2021, 01.01.2021. @misc{cristin1957213,
title = {Medieundersøkelsen 2021: Har koronadekningen svekket tilliten til mediene?},
author = {Erik Knudsen},
url = {https://app.cristin.no/results/show.jsf?id=1957213, Cristin
https://www.nordiskemediedager.no/medieundersoekelsen/medieundersoekelsen/},
year = {2021},
date = {2021-01-01},
howpublished = {Nordiske Mediedager 2021},
keywords = {Cristin},
pubstate = {published},
tppubtype = {presentation}
}
|
| The Effect of the COVID-19 Pandemic Crisis on Trust in the News Media: Evidence From Three Panel Waves With a Pre-Crisis Baseline Erik Knudsen Presentation Erik Knudsen; Åsta Dyrnes Nordø; Magnus Hoem Iversen 71st Annual ICA Conference, 01.01.2021. @misc{cristin1957204,
title = {The Effect of the COVID-19 Pandemic Crisis on Trust in the News Media: Evidence From Three Panel Waves With a Pre-Crisis Baseline Erik Knudsen},
author = {Erik Knudsen and Åsta Dyrnes Nordø and Magnus Hoem Iversen},
url = {https://app.cristin.no/results/show.jsf?id=1957204, Cristin},
year = {2021},
date = {2021-01-01},
howpublished = {71st Annual ICA Conference},
keywords = {Cristin},
pubstate = {published},
tppubtype = {presentation}
}
|
| The Promise and Perils of Algorithmic News Recommenders' Influence on Democracy Presentation Erik Knudsen TEDxBergen2021, 01.01.2021. @misc{cristin1957207,
title = {The Promise and Perils of Algorithmic News Recommenders' Influence on Democracy},
author = {Erik Knudsen},
url = {https://app.cristin.no/results/show.jsf?id=1957207, Cristin
https://youtu.be/B3qHJC5LwJQ},
year = {2021},
date = {2021-01-01},
howpublished = {TEDxBergen2021},
keywords = {Cristin},
pubstate = {published},
tppubtype = {presentation}
}
|
| How can news sites personalize audiences's news experiences without making audiences more polarized and fragmented? Working paper Erik Knudsen 2021. @workingpaper{cristin1957727,
title = {How can news sites personalize audiences's news experiences without making audiences more polarized and fragmented? },
author = {Erik Knudsen},
url = {https://app.cristin.no/results/show.jsf?id=1957727, Cristin},
year = {2021},
date = {2021-01-01},
keywords = {Cristin},
pubstate = {published},
tppubtype = {workingpaper}
}
|
| Datafication, Media and Democracy: Transformation of news work in datafied society Working paper Ana Milojevic 2021. @workingpaper{cristin1957739,
title = {Datafication, Media and Democracy: Transformation of news work in datafied society},
author = {Ana Milojevic},
url = {https://app.cristin.no/results/show.jsf?id=1957739, Cristin},
year = {2021},
date = {2021-01-01},
keywords = {Cristin},
pubstate = {published},
tppubtype = {workingpaper}
}
|
| MORS 2021: 1st Workshop on Multi Objective Recommender Systems Inproceedings Himan Abdollahpouri; Mehdi Elahi; Masoud Mansoury; Shaghayegh Sahebi; Zahra Nazari; Allison Chaney; Babak Loni In: Association for Computing Machinery (ACM), 2021. @inproceedings{cristin1956978,
title = {MORS 2021: 1st Workshop on Multi Objective Recommender Systems},
author = {Himan Abdollahpouri and Mehdi Elahi and Masoud Mansoury and Shaghayegh Sahebi and Zahra Nazari and Allison Chaney and Babak Loni},
url = {https://app.cristin.no/results/show.jsf?id=1956978, Cristin
https://dl.acm.org/doi/10.1145/3460231.3470936},
year = {2021},
date = {2021-01-01},
booktitle = {Association for Computing Machinery (ACM)},
abstract = {Historically, the main criterion for a successful recommender system was the relevance of the recommended items to the user. In other words, the only objective for the recommendation algorithm was to learn user’s preferences for different items and generate recommendations accordingly. However, real-world recommender systems are well beyond a simple objective and often need to take into account multiple objectives simultaneously. These objectives can be either from the users’ perspective or they could come from other stakeholders such as item providers or any party that could be impacted by the recommendations. Such multi-objective and multi-stakeholder recommenders present unique challenges and these challenges were the focus of the MORS workshop.},
keywords = {Cristin},
pubstate = {published},
tppubtype = {inproceedings}
}
Historically, the main criterion for a successful recommender system was the relevance of the recommended items to the user. In other words, the only objective for the recommendation algorithm was to learn user’s preferences for different items and generate recommendations accordingly. However, real-world recommender systems are well beyond a simple objective and often need to take into account multiple objectives simultaneously. These objectives can be either from the users’ perspective or they could come from other stakeholders such as item providers or any party that could be impacted by the recommendations. Such multi-objective and multi-stakeholder recommenders present unique challenges and these challenges were the focus of the MORS workshop. |
| Beyond Algorithmic Fairness in Recommender Systems Inproceedings Mehdi Elahi; Himan Abdollahpouri; Masoud Mansoury; Helma Torkamaan In: Association for Computing Machinery (ACM), 2021. @inproceedings{cristin1956964,
title = {Beyond Algorithmic Fairness in Recommender Systems},
author = {Mehdi Elahi and Himan Abdollahpouri and Masoud Mansoury and Helma Torkamaan},
url = {https://app.cristin.no/results/show.jsf?id=1956964, Cristin
https://dl.acm.org/doi/abs/10.1145/3450614.3461685},
doi = {https://doi.org/https://doi.org/10.1145/3450614.3461685},
year = {2021},
date = {2021-01-01},
booktitle = {Association for Computing Machinery (ACM)},
keywords = {Cristin},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| Changing Salty Food Preferences with Visual and Textual Explanations in a Search Interface Inproceedings Arngeir Berge; Vegard Velle Sjøen; Alain Dominique Starke; Christoph Trattner In: Association for Computing Machinery (ACM), 2021. @inproceedings{cristin1956563,
title = {Changing Salty Food Preferences with Visual and Textual Explanations in a Search Interface},
author = {Arngeir Berge and Vegard Velle Sjøen and Alain Dominique Starke and Christoph Trattner},
url = {https://app.cristin.no/results/show.jsf?id=1956563, Cristin},
year = {2021},
date = {2021-01-01},
booktitle = {Association for Computing Machinery (ACM)},
keywords = {Cristin},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| Exploring the effects of natural language justifications on food recommender systems Inproceedings Cataldo Musto; Alain Dominique Starke; Christoph Trattner; Amon Rapp; Giovanni Semeraro In: Association for Computing Machinery (ACM), 2021. @inproceedings{cristin1956541,
title = {Exploring the effects of natural language justifications on food recommender systems},
author = {Cataldo Musto and Alain Dominique Starke and Christoph Trattner and Amon Rapp and Giovanni Semeraro},
url = {https://app.cristin.no/results/show.jsf?id=1956541, Cristin},
year = {2021},
date = {2021-01-01},
booktitle = {Association for Computing Machinery (ACM)},
keywords = {Cristin},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| “Serving Each User”: Supporting Different Eating Goals Through a Multi-List Recommender Interface Inproceedings Alain Dominique Starke; Edis Asotic; Christoph Trattner In: Association for Computing Machinery (ACM), 2021. @inproceedings{cristin1956504,
title = {“Serving Each User”: Supporting Different Eating Goals Through a Multi-List Recommender Interface},
author = {Alain Dominique Starke and Edis Asotic and Christoph Trattner},
url = {https://app.cristin.no/results/show.jsf?id=1956504, Cristin},
doi = {https://doi.org/10.1145/3460231.3474232},
year = {2021},
date = {2021-01-01},
booktitle = {Association for Computing Machinery (ACM)},
keywords = {Cristin},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| Recommending Videos in Cold Start With Automatic Visual Tags Inproceedings Mehdi Elahi; Farshad Bakhshandegan Moghaddam; Reza Hosseini; Mohammad Hossein Rimaz; Nabil El Ioini; Marko Tkalcic; Christoph Trattner; Tammam Tillo In: Association for Computing Machinery (ACM), 2021. @inproceedings{cristin1956967,
title = {Recommending Videos in Cold Start With Automatic Visual Tags},
author = {Mehdi Elahi and Farshad Bakhshandegan Moghaddam and Reza Hosseini and Mohammad Hossein Rimaz and Nabil El Ioini and Marko Tkalcic and Christoph Trattner and Tammam Tillo},
url = {https://app.cristin.no/results/show.jsf?id=1956967, Cristin
https://dl.acm.org/doi/10.1145/3450614.3461687},
year = {2021},
date = {2021-01-01},
booktitle = {Association for Computing Machinery (ACM)},
abstract = {This paper addresses the so-called New Item problem in video Recommender Systems, as part of Cold Start. New item problem occurs when a new item is added to the system catalog, and the recommender system has no or little data describing that item. This could cause the system to fail to meaningfully recommend the new item to the users. We propose a novel technique that can generate cold start recommendation by utilizing automatic visual tags, i.e., tags that are automatically annotated by deeply analyzing the content of the videos and detecting faces, objects, and even celebrities within the videos. The automatic visual tags do not need any human involvement and have been shown to be very effective in representing the video content. In order to evaluate our proposed technique, we have performed a set of experiments using a large dataset of videos. The results have shown that the automatically extracted visual tags can be incorporated into the cold start recommendation process and achieve superior results compared to the recommendation based on human-annotated tags.},
keywords = {Cristin},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper addresses the so-called New Item problem in video Recommender Systems, as part of Cold Start. New item problem occurs when a new item is added to the system catalog, and the recommender system has no or little data describing that item. This could cause the system to fail to meaningfully recommend the new item to the users. We propose a novel technique that can generate cold start recommendation by utilizing automatic visual tags, i.e., tags that are automatically annotated by deeply analyzing the content of the videos and detecting faces, objects, and even celebrities within the videos. The automatic visual tags do not need any human involvement and have been shown to be very effective in representing the video content. In order to evaluate our proposed technique, we have performed a set of experiments using a large dataset of videos. The results have shown that the automatically extracted visual tags can be incorporated into the cold start recommendation process and achieve superior results compared to the recommendation based on human-annotated tags. |
| The Cholesterol Factor: Balancing Accuracy and Health in Recipe Recommendation Through a Nutrient-Specific Metric Inproceedings Alain Dominique Starke; Christoph Trattner; Hedda Bakken; Martin Skivenesvåg Johannessen; Vegard Solberg In: Association for Computing Machinery (ACM), 2021. @inproceedings{cristin1956600,
title = {The Cholesterol Factor: Balancing Accuracy and Health in Recipe Recommendation Through a Nutrient-Specific Metric},
author = {Alain Dominique Starke and Christoph Trattner and Hedda Bakken and Martin Skivenesvåg Johannessen and Vegard Solberg},
url = {https://app.cristin.no/results/show.jsf?id=1956600, Cristin},
year = {2021},
date = {2021-01-01},
booktitle = {Association for Computing Machinery (ACM)},
keywords = {Cristin},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| Promoting Healthy Food Choices Online: A Case for Multi-List Recommender Systems Inproceedings Alain Dominique Starke; Christoph Trattner In: Association for Computing Machinery (ACM), 2021. @inproceedings{cristin1956555,
title = {Promoting Healthy Food Choices Online: A Case for Multi-List Recommender Systems},
author = {Alain Dominique Starke and Christoph Trattner},
url = {https://app.cristin.no/results/show.jsf?id=1956555, Cristin},
year = {2021},
date = {2021-01-01},
booktitle = {Association for Computing Machinery (ACM)},
keywords = {Cristin},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| Investigating the impact of recommender systems on user-based and item-based popularity bias Journal Article Mehdi Elahi; Danial Khosh Kholgh; Mohammad Sina Kiarostami; Sorush Saghari; Shiva Parsa Rad; Marko Tkalcic In: Information Processing & Management, 2021. @article{cristin1953363,
title = {Investigating the impact of recommender systems on user-based and item-based popularity bias},
author = {Mehdi Elahi and Danial Khosh Kholgh and Mohammad Sina Kiarostami and Sorush Saghari and Shiva Parsa Rad and Marko Tkalcic},
url = {https://app.cristin.no/results/show.jsf?id=1953363, Cristin
https://www.sciencedirect.com/science/article/pii/S0306457321001436},
doi = {https://doi.org/10.1016/j.ipm.2021.102655},
year = {2021},
date = {2021-01-01},
journal = {Information Processing & Management},
abstract = {Recommender Systems are decision support tools that adopt advanced algorithms in order to help users to find less-explored items that can be interesting for them. While recommender systems may offer a range of attractive benefits, they may also intensify undesired effects, such as the Popularity Bias, where a few popular users/items get more popular and many unpopular users/items get more unpopular.
In this paper, we study the impact of different recommender algorithms on the popularity bias in different application domains and recommendation scenarios. We have designed a comprehensive evaluation methodology by considering two different recommendation scenarios, i.e., the user-based scenario (e.g., recommending users to users to follow), and the item-based scenario (e.g., recommending items to users to consume). We have used two large datasets, Twitter and Movielens, and compared a wide range of classical and modern recommender algorithms by considering a diverse range of metrics, such as PR-AUC, RCE, Gini index, and Entropy Score.
The results have shown a substantial difference between different scenarios and different recommendation domains. According to our observations, while the recommendation of users to users may increase the popularity bias in the system, the recommendation of items to users may indeed decrease it. Moreover, while we have measured a different level of popularity bias in different languages (i.e., English, Spanish, Portuguese, and Japaneses), the above-noted phenomena has been consistently observed in all of these languages.},
keywords = {Cristin},
pubstate = {published},
tppubtype = {article}
}
Recommender Systems are decision support tools that adopt advanced algorithms in order to help users to find less-explored items that can be interesting for them. While recommender systems may offer a range of attractive benefits, they may also intensify undesired effects, such as the Popularity Bias, where a few popular users/items get more popular and many unpopular users/items get more unpopular.
In this paper, we study the impact of different recommender algorithms on the popularity bias in different application domains and recommendation scenarios. We have designed a comprehensive evaluation methodology by considering two different recommendation scenarios, i.e., the user-based scenario (e.g., recommending users to users to follow), and the item-based scenario (e.g., recommending items to users to consume). We have used two large datasets, Twitter and Movielens, and compared a wide range of classical and modern recommender algorithms by considering a diverse range of metrics, such as PR-AUC, RCE, Gini index, and Entropy Score.
The results have shown a substantial difference between different scenarios and different recommendation domains. According to our observations, while the recommendation of users to users may increase the popularity bias in the system, the recommendation of items to users may indeed decrease it. Moreover, while we have measured a different level of popularity bias in different languages (i.e., English, Spanish, Portuguese, and Japaneses), the above-noted phenomena has been consistently observed in all of these languages. |
| Predicting Feature-based Similarity in the News Domain Using Human Judgments Inproceedings Alain Dominique Starke; Sebastian Øverhaug Larsen; Christoph Trattner In: Association for Computing Machinery (ACM), 2021. @inproceedings{cristin1956594,
title = {Predicting Feature-based Similarity in the News Domain Using Human Judgments},
author = {Alain Dominique Starke and Sebastian Øverhaug Larsen and Christoph Trattner},
url = {https://app.cristin.no/results/show.jsf?id=1956594, Cristin},
year = {2021},
date = {2021-01-01},
booktitle = {Association for Computing Machinery (ACM)},
keywords = {Cristin},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| Developing a Software Reference Architecture for Journalistic Knowledge Platforms Journal Article Marc Gallofré Ocaña; Andreas Lothe Opdahl In: CEUR Workshop Proceedings, 2021. @article{cristin1949655,
title = {Developing a Software Reference Architecture for Journalistic Knowledge Platforms},
author = {Marc Gallofré Ocaña and Andreas Lothe Opdahl},
url = {https://app.cristin.no/results/show.jsf?id=1949655, Cristin
https://ceur-ws.org/Vol-2978/saml-paper2.pdf},
year = {2021},
date = {2021-01-01},
journal = {CEUR Workshop Proceedings},
keywords = {Cristin},
pubstate = {published},
tppubtype = {article}
}
|
| Kva datamaskiner kan gjere Medium Lars Nyre; Bjørnar Tessem Dag og Tid, 2021. @media{cristin1957728,
title = {Kva datamaskiner kan gjere},
author = {Lars Nyre and Bjørnar Tessem},
url = {https://app.cristin.no/results/show.jsf?id=1957728, Cristin},
year = {2021},
date = {2021-01-01},
howpublished = {Dag og Tid},
keywords = {Cristin},
pubstate = {published},
tppubtype = {media}
}
|
| What Matters in Professional Drone Pilots’ Practice? An Interview Study to Understand the Complexity of Their Work and Inform Human-Drone Interaction Research Proceeding Sarah Ljungblad; Yemao Man; Mehmet Aydın Baytaş; Mafalda Gamboa; Morten Fjeld; Mohammad Obaid ACM CHI on human factors in computing systems conference proceeding, 2021. @proceedings{cristin2003885,
title = {What Matters in Professional Drone Pilots’ Practice? An Interview Study to Understand the Complexity of Their Work and Inform Human-Drone Interaction Research},
author = {Sarah Ljungblad and Yemao Man and Mehmet Aydın Baytaş and Mafalda Gamboa and Morten Fjeld and Mohammad Obaid},
url = {https://app.cristin.no/results/show.jsf?id=2003885, Cristin},
year = {2021},
date = {2021-01-01},
howpublished = {ACM CHI on human factors in computing systems conference proceeding},
keywords = {Cristin},
pubstate = {published},
tppubtype = {proceedings}
}
|
| Fake news og mediebruk Presentation Hallvard Moe Vestlandsseminaret, 01.01.2021. @misc{cristin1957718,
title = {Fake news og mediebruk},
author = {Hallvard Moe},
url = {https://app.cristin.no/results/show.jsf?id=1957718, Cristin},
year = {2021},
date = {2021-01-01},
howpublished = {Vestlandsseminaret},
keywords = {Cristin},
pubstate = {published},
tppubtype = {presentation}
}
|
| Contagious "Corona" Compounding by Journalists in a CLARIN Newspaper Monitor Corpus Inproceedings Koenraad De Smedt In: 2021. @inproceedings{cristin1918658,
title = {Contagious "Corona" Compounding by Journalists in a CLARIN Newspaper Monitor Corpus},
author = {Koenraad De Smedt},
url = {https://app.cristin.no/results/show.jsf?id=1918658, Cristin
https://ecp.ep.liu.se/index.php/clarin/article/view/10/209},
doi = {https://doi.org/https://doi.org/10.3384/ecp18010},
year = {2021},
date = {2021-01-01},
keywords = {Cristin},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| Why won't young people pay for news? Working paper Marianne Borchgrevink-Brækhus 2021. @workingpaper{cristin1957735,
title = {Why won't young people pay for news?},
author = {Marianne Borchgrevink-Brækhus},
url = {https://app.cristin.no/results/show.jsf?id=1957735, Cristin},
year = {2021},
date = {2021-01-01},
keywords = {Cristin},
pubstate = {published},
tppubtype = {workingpaper}
}
|
| What can TV companies do for teenagers who are on their phone all the time? Working paper John Magnus Ragnhildson Dahl 2021. @workingpaper{cristin1957717,
title = {What can TV companies do for teenagers who are on their phone all the time?},
author = {John Magnus Ragnhildson Dahl},
url = {https://app.cristin.no/results/show.jsf?id=1957717, Cristin},
year = {2021},
date = {2021-01-01},
keywords = {Cristin},
pubstate = {published},
tppubtype = {workingpaper}
}
|
