Responsible Media Technology Crucial for Democracy
by Anna Pacholczyk | July 4, 2022 | News | 0 Comments
MediaFutures Director has been featured in an article by the Bergen Chamber of Commerce and Industry about the importance of developing responsible media technology.
Call for papers – MORS: Workshop on Multi-Objective Recommender Systems.
by Anna Pacholczyk | July 1, 2022 | News, Uncategorized | 0 Comments
The 2nd workshop on Multi-Objective Recommender Systems (MORS) will be held in Seattle, USA in conjunction with the 16th ACM Conference on Recommender Systems. The workshop itself will take place between September 18th and 23rd (exact date to be announced).The organizers now welcome contributions that address the challenges related to having multiple objectives or multiple stakeholders in recommender systems.
Understanding young people’s media experiences
by Anna Pacholczyk | June 25, 2022 | News, Post without featured image | 0 Comments
With the ever-expanding range of available products and elusive media consumption habits of younger audience, providing high quality and original content to the young people remains a central issue for media companies, including legacy media.
Enhancing Movie Recommendation with Visual Analysis
by Anna Pacholczyk | June 16, 2022 | News | 0 Comments
Every minute, hundreds of hours of movies are being uploaded to different media platforms. For a recommender system to match the right user with the right movie, a certain amount of data is required, e.g. in the form of explicit or implicit ratings provided by...
Responsible Media Technology Crucial for Democracy
by Anna Pacholczyk | July 4, 2022 | News | 0 Comments
MediaFutures Director has been featured in an article by the Bergen Chamber of Commerce and Industry about the importance of developing responsible media technology.
Call for papers – MORS: Workshop on Multi-Objective Recommender Systems.
by Anna Pacholczyk | July 1, 2022 | News, Uncategorized | 0 Comments
The 2nd workshop on Multi-Objective Recommender Systems (MORS) will be held in Seattle, USA in conjunction with the 16th ACM Conference on Recommender Systems. The workshop itself will take place between September 18th and 23rd (exact date to be announced).The organizers now welcome contributions that address the challenges related to having multiple objectives or multiple stakeholders in recommender systems.
Understanding young people’s media experiences
by Anna Pacholczyk | June 25, 2022 | News, Post without featured image | 0 Comments
With the ever-expanding range of available products and elusive media consumption habits of younger audience, providing high quality and original content to the young people remains a central issue for media companies, including legacy media.
/ Latest publications
| Nudging Towards Health? Examining the Merits of Nutrition Labels and Personalization in a Recipe Recommender System Conference Ayoub El Majjodi; Alain D. Starke; Christoph Trattner Nudging Towards Health? Examining the Merits of Nutrition Labels and Personalization in a Recipe Recommender System, 2022. @conference{Majjodi2022,Food recommender systems show personalized recipes to users based on content liked previously. Despite their potential, often recommended (popular) recipes in previous studies have turned out to be unhealthy, negatively contributing to prevalent obesity problems worldwide. Changing how foods are presented through digital nudges might help, but these are usually examined in non-personalized contexts, such as a brick-and-mortar supermarket. This study seeks to support healthy food choices in a personalized interface by adding front-of-package nutrition labels to recipes in a food recommender system. After performing an offline evaluation, we conducted an online study (N = 600) with six different recommender interfaces, based on a 2 (non-personalized vs. personalized recipe advice) x 3 (No Label, Multiple Traffic Light, Nutri-Score) between-subjects design. We found that recipe choices made in the non-personalized scenario were healthier, while the use of nutrition labels (our digital nudge) reduced choice difficulty when the content was personalized. |
| Occupational Biases in Norwegian and Multilingual Language Models Workshop Samia Touileb; Lilja Øvrelid; Erik Velldal 2022. @workshop{Touileb2022,In this paper we explore how a demographic distribution of occupations, along gender dimensions, is reflected in pre-trained language models. We give a descriptive assessment of the distribution of occupations, and investigate to what extent these are reflected in four Norwegian and two multilingual models. To this end, we introduce a set of simple bias probes, and perform five different tasks combining gendered pronouns, first names, and a set of occupations from the Norwegian statistics bureau. We show that language specific models obtain more accurate results, and are much closer to the real-world distribution of clearly gendered occupations. However, we see that none of the models have correct representations of the occupations that are demographically balanced between genders. We also discuss the importance of the training data on which the models were trained on, and argue that template-based bias probes can sometimes be fragile, and a simple alteration in a template can change a model’s behavior. |
| 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. |
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, |
