Enhancing Movie Recommendation with Visual Analysis
by Anna Pacholczyk | June 16, 2022 | News | 0 Comments
New Research Paper – Considering Temporal Aspects in Recommender Systems: A Survey
by Anna Pacholczyk | June 1, 2022 | News | 0 Comments
The new work of Prof. Christoph Trattner et al. fills a gap and answers the needs for updated reference point for researchers and practitioners who consider integrating temporal aspects in their personalised systems.
Work Package 1’ Researchers to Present at the ICA Conference
by Anna Pacholczyk | May 24, 2022 | News | 0 Comments
Professors Brita Ytre-Arne and Hallvard Moe, alongside researcher Erik Knudsen and Postdoctoral Fellow John Magnus Dahl will present their research at the 72nd International Communication Association conference in Paris.
New Publication
by Anna Pacholczyk | May 16, 2022 | News | 0 Comments
MediaFutures’ Work Package 3 Leader Bjørnar Tessem alongside Lars Nyre, Michel d.S. Mesquita& Paul Mulholland contributed with a chapter in the book “Futures of Journalism Technology-stimulated Evolution in the Audience-News Media Relationship».
Enhancing Movie Recommendation with Visual Analysis
by Anna Pacholczyk | June 16, 2022 | News | 0 Comments
New Research Paper – Considering Temporal Aspects in Recommender Systems: A Survey
by Anna Pacholczyk | June 1, 2022 | News | 0 Comments
The new work of Prof. Christoph Trattner et al. fills a gap and answers the needs for updated reference point for researchers and practitioners who consider integrating temporal aspects in their personalised systems.
Work Package 1’ Researchers to Present at the ICA Conference
by Anna Pacholczyk | May 24, 2022 | News | 0 Comments
Professors Brita Ytre-Arne and Hallvard Moe, alongside researcher Erik Knudsen and Postdoctoral Fellow John Magnus Dahl will present their research at the 72nd International Communication Association conference in Paris.
/ Latest publications
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, 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, 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, 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, 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, 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. |
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. BibTeX | Links: @techreport{Tverberg2021, |