Details
Role
Centre Director
Centre Director
Connected to
WP6: Centre Management
WP6: Centre Management
WP2: User Modeling, Personalization & Engagement
Affiliation
University of Bergen
University of Bergen
Contact
/ Biography
I am a Full Professor (1404) at the University of Bergen and Center Director of the Research Centre for Responsible Media Technology & Innovation – SFI MediaFutures worth around 26 million EUR. I am also the founder of the DARS research group at UiB and hold a 10% Research Professor (Forsker I) position in NORCE (NKLM, department of Health) one of Norway’s largest research institutions. I am also an External Lecturer at MODUL University Vienna, Austria’s leading international private university. I have received a PhD (with distinction), an MSc (with distinction) and a BSc in Computer Science and Telematics from Graz University of Technology (Austria). I am an ACM Senior Member and a former Austrian Research Promotion Agency (FFG) fellow, Marshall Plan and European Research Consortium for Informatics and Mathematics (ERICM) fellow and have been working at Graz University of Technology from 2009-2012, the University of Pittsburgh from 2011-2012, the Norwegian University of Science and Technology from 2014-2015, and have been visiting Yahoo! Labs Barcelona in 2014 and CWI Amsterdam in 2015 two times. I position my research in two central specializations in the Information Science research field. The first is “Behavioral Data Analytics” and the second is “Recommender Systems”. Since 2009, I published over 100 scientific articles in top venues about my work and have acquired over 54 million Euros in funding on European and (inter) national level – 30 million as the PI. Examples of outlets where my work has been published includes NATURE Sustainability, JASIST, EPJ Data Science, UMUAI, WWW, AAAI ICWSM or ACM SIGIR. I am also the winner of several Best Paper/Poster Awards and Nominations, including, the Best Paper Award Honorable Mention at the prestigious CORE A* ranked The Web Conference in 2017. I regularly act as a (senior) PC member on several top-tier – CORE A* ranked – conferences and co-organize or co-chair a number of workshops and conferences. Recent examples include ACM RecSys in 2018 – 2020 (as Workshop/Late-Breaking Results co-chair) and ACM SIGIR 2020 – 2021 (as senior PC). I am a steering board member of CEDAS (Center for Data Science) at UiB, a member of the study program board of the Information Science program at UiB and a member of the editorial board of Elsevier’s journal on Online Social Networks and Media, the Open Access Journal Future Internet, Frontiers in Big Data, Springer’s Journal of Intelligent Information Systems, and AI and Ethics. Since 2009, I have taught over 2000 students in Austria and Norway, in over 35 courses on BSc, MSc, MBA and PhD level about ICT, and I supervised over 35 students on their Master or PhD thesis. Since June 2021, I am an appointed ACM Distinguished Speaker!
/ Publications
Publications from 2020 and before are not direct results of the SFI MediaFutures, but are key results from our team members working on related topics in MediaFutures.
2022 |
| Nudging Towards Health? Examining the Merits of Nutrition Labels and Personalization in a Recipe Recommender System Conference Majjodi, Ayoub El; Starke, Alain D.; Trattner, Christoph 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. |
| Considering Temporal Aspects in Recommender Systems: A Survey Journal Article Bogina, Veronica; Kuflik, Tsvi; Jannach, Dietmar; Bielikova, Maria; Kompan, Michal; Trattner, Christoph 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. |
| Developing and Evaluating a University Recommender System Journal Article Elahi, Mehdi; Starke, Alain D.; Ioini, Nabil El; Lambrix, Anna Alexander; Trattner, Christoph 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. |
| Hybrid Recommendation of Movies based on Deep Content Features Inproceedings Kvifte, Tord; Elahi, Mehdi; Trattner, Christoph In: Springer Nature, 2022. @inproceedings{cristin1957037,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. |
2021 |
| Responsible media technology and AI: challenges and research directions Journal Article Trattner, Christoph; Jannach, Dietmar; Motta, Enrico; Meijer, Irene Costera; Diakopoulos, Nicholas; Elahi, Mehdi; Opdahl, Andreas Lothe; Tessem, Bjørnar; Borch, Njål Trygve; Fjeld, Morten; Øvrelid, Lilja; Smedt, Koenraad De; Moe, Hallvard In: AI and Ethics, 2021. @article{cristin2000622, |
| Towards Responsible Media Recommendation Journal Article Elahi, Mehdi; Jannach, Dietmar; Skjærven, Lars; Knudsen, Erik; Sjøvaag, Helle; Tolonen, Kristian; Holmstad, Øyvind; Pipkin, Igor; Throndsen, Eivind; Stenbom, Agnes; Fiskerud, Eivind; Oesch, Adrian; Vredenberg, Loek; Trattner, Christoph In: AI and Ethics, 2021. @article{cristin1953352, |
| Nudging Healthy Choices in Food Search Through Visual Attractiveness Journal Article Starke, Alain D.; Willemsen, Martijn C.; Trattner, Christoph In: no. April 2021, pp. 1-18, 2021. @article{Starke2021,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 Jannach, Dietmar; Jesse, Mathias; Jugovac, Michael; Trattner, Christoph 29th ACM International Conference on User Modeling, Adaptation and Personalization (UMAP '21) 2021. @conference{Jannach2021,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. |
| Promoting Healthy Food Choices Online: A Case for Multi-List Recommender Systems Inproceedings Starke, Alain Dominique; Trattner, Christoph In: Association for Computing Machinery (ACM), 2021. @inproceedings{cristin1956555, |
| Enhanced Movie Recommendation Incorporating Visual Features Working paper Elahi, Mehdi; Moghaddam, Farshad Bakhshandegan; Hosseini, Reza; Rimaz, Mohammad Hossein; Trattner, Christoph 2021. @workingpaper{cristin1957034, |
| Predicting Feature-based Similarity in the News Domain Using Human Judgments Inproceedings Starke, Alain Dominique; Larsen, Sebastian Øverhaug; Trattner, Christoph In: Association for Computing Machinery (ACM), 2021. @inproceedings{cristin1956594, |
| Changing Salty Food Preferences with Visual and Textual Explanations in a Search Interface Inproceedings Berge, Arngeir; Sjøen, Vegard Velle; Starke, Alain Dominique; Trattner, Christoph In: Association for Computing Machinery (ACM), 2021. @inproceedings{cristin1956563, |
| Changing Salty Food Preferences with Visual and Textual
Explanations in a Search Interface Journal Article Berge, Arngeir; Sjøen, Vegard Velle; Starke, Alain; Trattner, Christoph In: CEUR Workshop Proceedings, 2021. @article{cristin1933059,Salt is consumed at too high levels in the general population, causing high blood pressure and related health problems. In this paper, we present results of ongoing research that tries to reduce salt intake via technology and in particular from an interface perspective. In detail, this paper features results of a study that examines the extent to which visual and textual explanations in a search interface can change salty food preferences. An online user study with 200 participants demonstrates that this is possible in food search results by accompanying recipes with a visual taste map that includes salt-replacer herbs and spices in the calculation of salty taste. |
| The Cholesterol Factor: Balancing Accuracy and Health in Recipe Recommendation Through a Nutrient-Specific Metric Inproceedings Starke, Alain Dominique; Trattner, Christoph; Bakken, Hedda; Johannessen, Martin Skivenesvåg; Solberg, Vegard In: Association for Computing Machinery (ACM), 2021. @inproceedings{cristin1956600, |
| Exploring the effects of natural language justifications on food recommender systems Inproceedings Musto, Cataldo; Starke, Alain Dominique; Trattner, Christoph; Rapp, Amon; Semeraro, Giovanni In: Association for Computing Machinery (ACM), 2021. @inproceedings{cristin1956541, |
| “Serving Each User”: Supporting Different Eating Goals Through a Multi-List Recommender Interface Inproceedings Starke, Alain Dominique; Asotic, Edis; Trattner, Christoph In: Association for Computing Machinery (ACM), 2021. @inproceedings{cristin1956504, |
| Recommending Videos in Cold Start With Automatic Visual Tags Inproceedings Elahi, Mehdi; Moghaddam, Farshad Bakhshandegan; Hosseini, Reza; Rimaz, Mohammad Hossein; Ioini, Nabil El; Tkalcic, Marko; Trattner, Christoph; Tillo, Tammam In: Association for Computing Machinery (ACM), 2021. @inproceedings{cristin1956967,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. |
2019 |
| Learning to Recommend Similar Items from Human Judgements Journal Article Trattner, Christoph; Jannach, Dietmar In: User Modeling and User-Adapted Interaction Journal, pp. 1-50, 2019, (Pre SFI). @article{Trattner2020,Similar item recommendations—a common feature of many Web sites—point users to other interesting objects given a currently inspected item. A common way of computing such recommendations is to use a similarity function, which expresses how much alike two given objects are. Such similarity functions are usually designed based on the specifics of the given application domain. In this work, we explore how such functions can be learned from human judgments of similarities between objects, using two domains of “quality and taste”—cooking recipe and movie recommendation—as guiding scenarios. In our approach, we first collect a few thousand pairwise similarity assessments with the help of crowdworkers. Using these data, we then train different machine learning models that can be used as similarity functions to compare objects. Offline analyses reveal for both application domains that models that combine different types of item characteristics are the best predictors for human-perceived similarity. To further validate the usefulness of the learned models, we conducted additional user studies. In these studies, we exposed participants to similar item recommendations using a set of models that were trained with different feature subsets. The results showed that the combined models that exhibited the best offline prediction performance led to the highest user-perceived similarity, but also to recommendations that were considered useful by the participants, thus confirming the feasibility of our approach. |
2018 |
| Towards a big data platform for news angles Workshop Ocaña, Marc Gallofré; Nyre, Lars; Opdahl, Andreas Lothe; Tessem, Bjørnar; Trattner, Christoph; Veres, Csaba Norwegian Big Data Symposium 2018, 2018, (Pre SFI). @workshop{Ocaña2018,Finding good angles on news events is a central journalistic and editorial skill. As news work becomes increasingly computer-assisted and big-data based, journalistic tools therefore need to become better able to support news angles too. This paper outlines a big-data platform that is able to suggest appropriate angles on news events to journalists. We first clarify and discuss the central characteristics of news angles. We then proceed to outline a big-data architecture that can propose news angles. Important areas for further work include: representing news angles formally; identifying interesting and unexpected angles on unfolding events; and designing a big-data architecture that works on a global scale. |
| The Roadmap to User-Controllable Social Exploratory Search Journal Article Sciascio, Cecilia Di; Brusilovsky, Peter; Trattner, Christoph; Veas, Eduardo In: ACM Transactions on Interactive Intelligent Systems, pp. 1-37, 2018, (Pre SFI). @article{Sciascio2018,Information-seeking tasks with learning or investigative purposes are usually referred to as exploratory search. Exploratory search unfolds as a dynamic process where the user, amidst navigation, trial-and-error and on-the-!y selections, gathers and organizes information (resources). A range of innovative interfaces with increased user control have been developed to support exploratory search process. In this work we present our attempt to increase the power of exploratory search interfaces by using ideas of social search, i.e., leveraging information left by past users of information systems. Social search technologies are highly popular nowadays, especially for improving ranking. However, current approaches to social ranking do not allow users to decide to what extent social information should be taken into account for result ranking. This paper presents an interface that integrates social search functionality into an exploratory search system in a user-controlled way that is consistent with the nature of exploratory search. The interface incorporates control features that allow the user to (i) express information needs by selecting keywords and (ii) to express preferences for incorporating social wisdom based on tag matching and user similarity. The interface promotes search transparency through color-coded stacked bars and rich tooltips. This work presents the full series of evaluations conducted to, "rst, assess the value of the social models in contexts independent to the user interface, in terms of objective and perceived accuracy. Then, in a study with the full-!edged system, we investigated system accuracy and subjective aspects with a structural model that revealed that, when users actively interacted with all its control features, the hybrid system outperformed a baseline content-based-only tool and users were more satis"ed. |
2017 |
| Exploiting Food Choice Biases for Healthier Recipe Recommendation Conference Elsweiler, David; Trattner, Christoph; Harvey, Morgan ACM SIGIR Conference 2017, (Pre SFI). @conference{Elsweiler2017,By incorporating healthiness into the food recommendation / ranking process we have the potential to improve the eating habits of a growing number of people who use the Internet as a source of food inspiration. In this paper, using insights gained from various data sources, we explore the feasibility of substituting meals that would typically be recommended to users with similar, healthier dishes. First, by analysing a recipe collection sourced from Allrecipes.com, we quantify the potential for nding replacement recipes, which are comparable but have di erent nutritional characteristics and are nevertheless highly rated by users. Building on this, we present two controlled user studies (n=107, n=111) investigating how people perceive and select recipes. We show participants are unable to reliably identify which recipe contains most fat due to their answers being biased by lack of information, misleading cues and limited nutritional knowledge on their part. By applying machine learning techniques to predict the preferred recipes, good performance can be achieved using low-level image features and recipe meta-data as predictors. Despite not being able to consciously determine which of two recipes contains most fat, on average, participants select the recipe with the most fat as their preference. The importance of image features reveals that recipe choices are often visually driven. A nal user study (n=138) investigates to what extent the predictive models can be used to select recipe replacements such that users can be “nudged” towards choosing healthier recipes. Our ndings have important implications for online food systems. |
| Visually-Aware Video Recommendation in the Cold Start Conference Elahi, Mehdi; Hosseini, Reza; Rimaz, Mohammad H.; Moghaddam, Farshad B.; Trattner, Christoph Proccedings of theACM Hypertext 2020 2017, (Pre SFI). @conference{Elahi2017b,Recommender Systems (RSs) have become essential tools in any video-sharing platforms (such as YouTube) by generating video suggestions for users. Although, RSs have been e!ective, however, they su!er from the so-called New Item problem. New item problem, as part of Cold Start problem, happens when a new item is added to the system catalogue and the RS has no or little data available for that new item. In such a case, the system may fail to meaningfully recommend the new item to any user. In this paper, we propose a novel recommendation technique based on visual tags, i.e., tags that are automatically annotated to videos based on visual description of videos. Such visual tags can be used in an extreme cold start situation, where neither any rating, nor any tag is available for the new video item. The visual tags could also be used in the moderate cold start situation when the new video item has been annotated with few tags. This type of content features can be extracted automatically without any human involvement and have been shown to be very e!ective in representing the video content. We have used a large dataset of videos and shown that 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. |
2016 |
| VizRec: Recommending Personalized Visualizations Journal Article Mutlu, Belgin; Veas, Eduardo; Trattner, Christoph In: ACM Transactions on Interactive Intelligent Systems (TiiS), vol. 6, no. 4, pp. 1-40, 2016, (Pre SFI). @article{Mutlu2016,Visualizations have a distinctive advantage when dealing with the information overload problem: since they are grounded in basic visual cognition, many people understand them. However, creating the appropriate representation requires specific expertise of the domain and underlying data. Our quest in this paper is to study methods to suggest appropriate visualizations autonomously. To be appropriate, a visualization has to follow studied guidelines to find and distinguish patterns visually, and encode data therein. Thus, a visualization tells a story of the underlying data; yet, to be appropriate, it has to clearly represent those aspects of the data the viewer is interested in. Which aspects of a visualization are important to the viewer? Can we capture and use those aspects to recommend visualizations? This paper investigates strategies to recommend visualizations considering different aspects of user preferences. A multi-dimensional scale is used to estimate aspects of quality for charts for collaborative filtering. Alternatively, tag vectors describing charts are used to recommend potentially interesting charts based on content. Finally, a hybrid approach combines information on what a chart is about (tags) and how good it is (ratings). We present the design principles behind VizRec, our visual recommender. We describe its architecture, the data acquisition approach with a crowd sourced study, and the analysis of strategies for visualization recommendation. |