DARS - Behavioral Data Analytics & Recommender Systems Research Group @ University of Bergen

The DARS research group performs cutting-edge research in methods (e.g., AI methods to predict user behavior) and applications (e.g., intelligent user interfaces) in the context of behavioral data analytics and recommender systems. We closely collaborate with both academic and industrial partners in Norway, the EU, and beyond, contributing to Norway's national research initiative on Artificial Intelligence (AI), as well as to current AI-related research challenges in the Norwegian industry sectors. Current main research application areas of DARS include: media, finance, energy, and health. The research group is a spin-out of the Intelligent Information Systems (I2S) group at the Department of Information Science and Media Studies at the University of Bergen. DARS closely collaborates with the Centre for Data Science (CEDAS) at the University of Bergen, the Norwegian Artificial Intelligence Research Consortium (NORA), and conducts research in collaboration with a network of renowned national and international scholars in the context of behavioral data analytics and recommender systems. Outputs are published in leading conferences and journals in the field of computer & information science and interdisciplinary venues such as: NATURE Sustainability, PlosOne, JASIST, EPJ Data Science, UMUAI, WWW, ICWSM, ACM IUI, ACM UMAP, ACM SIGIR, and ACM RecSys. The core research group members have won several Best Paper/Poster Awards and Nominations, including, the Best Paper Award Honorable Mention at WWW'17. The core team is involved in the co-organization of leading conferences in their fields of research such as ACM RecSys, ACM UMAP or ACM IUI and teaches tech-related courses such as Information Systems, Advanced Programming in Python or Recommender Systems at the University of Bergen.

• [01/2024] New year, new updates. We have a new PhD, Bilal Mahmood, as well as new research assitants. Several new publications have been added below.
• [11/2022] Publications for Starke have been added for 2022.
• [02/2022] New year, new updates. We've had some more papers that have been accepted. Check them out at publications below
• [08/2021] It has been a while since we have updated you. Quite a few papers have been accepted since then! Please have a look at our publications for a snapshot
• [07/2021] Along with colleagues from Utrecht University and Eindhoven University of Technology, Alain was awarded a Seed Fund Grant worth €50,000 to pursue research on “Empowering Consumers through Intelligible Contracts in the New Energy Era”, a collaboration between HCI researchers and legal scholars. LINK
• [03/2021] Call us Dars Lab no longer! We are excited to announce that the Department Board of InfoMedia has formally decided to accept our application to establish DARS as new resesarch group! We will henceforth be known as the Dars Research Group.
• [03/2021] Happy to announce that Alain got a paper accepted at CHI! He collaborated with ECRs in the Netherlands on the future of conversational interfaces, titled: "Conversational Futures: Emancipating Conversational Interactions for Futures Worth Wanting". PDF
• [03/2021] A number papers in which core members of the DARS Research group collaborated have been accepted at various conferences. Check out our recent publications for an overview!
• [01/2021] Come to the digital opening of our new research centre MediaFutures on Feb 2nd, 2021 :) LINK
• [12/2020] Happy to announce that joint work of Alain and Christoph with a big group of researchers (such as Marieke van Erp, Christian Reynolds, Diana Maynard, and Rebeca Ibañez-Martín) has been accepted for publication at Frontiers in AI. It is titled "Using Natural Language Processing and Artificial Intelligence to explore the nutrition and sustainability of recipes and food": LINK.
• [12/2020] Thrilled to share a preprint of joint work between Alain, Christoph, and Martijn Willemsen of TU Eindhoven. The work, titled 'Nudging Healthy Choices in Food Search Through Visual Attractiveness' is currently under review at Frontiers in AI: PDF. A poster presented at the Dutch-Belgian Information Retrieval Meet-up about the paper can be found here: PDF.
• [11/2020] Alain will serve as the publicity chair of the 2021 ACM Recommender Systems conference in Amsterdam, Sept. 27 - Oct. 1.
• [10/2020] Christoph will give a keynote talk at the Cutting Edge Science and Engineering Symposium: Advances in personalised healthcare and wellbeing support technologies - OzDHI2020.
• [09/2020] Christoph accepted the Editorial Board invitation to the Open Access Journal - Future Internet.
• [08/2020] Mehdi serves as the publicity chair of International Conference on Cloud Computing - EAI CloudComp2020.
• [08/2020] Mehdi accepts to join the Editorial Board of the Open Access Journal - Electronics.
• [07/2020] Happy holidays :)
• [06/2020] Wow wow wow: Won the SFI - Centre for Research-based Innovation - MediaFutures, with our team! What a day :)))
• [06/2020] Very happy to hear that our work "Visual Cultural Biases in Food Classification", authored by Qing Zhang, David Elsweiler and myself has been accepted in Foods, IF: 3.011. Here the pre-print! PDF
• [06/2020] Christoph has been interviewed regarding our SFI application MediaFutures in the context of a #FakeNews breakfast meeting organised by NCE Media along with the CEO of Schibsted and other important media experts from NRK, TV 2, Tinius Trust, and Faktisk.no. Here the YouTube video.
• [06/2020] Happy to announce that our paper "Visually-Aware Video Recommendation in the Cold Start" co-authored by Mehdi Elahi, Reza Hosseini, Mohammad Hossein Rimaz, Farshad Bakhshandegan Moghaddam and Christoph Trattner has been accepted at ACM Hypertext 2020 in the Blue Sky Ideas Track. Here the pre-print! PDF
• [04/2020] Our short paper with Ayoub EL Majjodi, and Nabil EL Ioini, titled "Towards Generating Personalized Country Recommendation" has been accepted for publication at the UMAP'20 conference LBR track.
• [04/2020] Our short paper with Anna Alexander Lambrix, Nabil EL Ioini, and Mouzhi Ge, titled "Exploring Personalized University Ranking and Recommendation" has been accepted for publication at the UMAP'20 conference LBR track.
• [04/2020] Our short paper with Cataldo Musto and Giovanni Semeraro, titled "Towards a Knowledge-aware Food Recommender System Exploiting Holistic User Models" has been accepted for publication at the UMAP'20 conference. Christoph and Alain will present the work at July 14-17.
• [02/2020] Happy to announce that our IKTPLUSS proposal "RE-AIMED: Readjusted responses by use of AI in medical calls" lead by NORCE has been granted by the Norwegian Science Fund! Total budget: 1.5 million EUR.
• [01/2020] Happy to announce that the MediaFutures SFI application "Research Centre for Responsible Media Technology & Innovation" worth around 30 million EUR and which Dr. Trattner has lead as PI was submitted successfully to the Research Council of Norway :)
• [01/2020] Our student Johnny Bjånesøy has been awarded a grant by TV2 for his work on recommender systems interfaces for online streaming services! Congrats :)
• [01/2020] Our students Tim Soltvedt Aadland, Felipe Sepulveda and Gøran A. Slettemark have successfully defended their Master theses! Congrats :)
• [01/2020] Work with us as a PhD student! Deadline: 29th February 2020. Here the job post!

Prof. Dr. Christoph Trattner [PI]
(InfoMedia @ University of Bergen)

Christoph Trattner is a Full Professor at the University of Bergen (UiB) in the Information Science & Media Studies Department. Previously to that he was an Asst. Prof. at MODUL University Vienna in the New Media Technology Department and a manager at the Know-Center, Austria's research competence for data driven business and Big Data analytics where he founded and led the Social Computing department. He holds a PhD (with distinction), an MSc (with distinction) and a BSc in Computer Science and Telematics from Graz University of Technology (Austria). Since, 2010 he co-acquired over 22 million euros in funding on European and international level in collaboration with major international industrial partners. Currently, he is leading several international research efforts that try to understand, predict and change online user behavior. He published over 100 scientific articles in top-notch venues, such as NATURE Sustainability, PlosOne, JASIST, EPJ Data Science, UMUAI, WWW, ICWSM or ACM SIGIR. He is the winner of several Best Paper/Poster Awards and Nominations, including, the Best Paper Award Honorable Mention at WWW'17.

Assoc. Prof. Dr. Mehdi Elahi [Co-PI]
(InfoMedia @ University of Bergen)

Mehdi Elahi is an associate professor at the University of Bergen (Norway). He received his M.Sc. degree in Electrical Engineering (Sweden) in 2010, and his Ph.D. degree in Computer Science (Italy) in 2014. Over the last 3 years, he has served as an assistant professor at the Free University of Bozen-Bolzano (Italy), where he has researched various aspects of recommender systems. He has (co-)authored more than 60 peer-reviewed publications in AI, RS, and HCI-related conferences and journals. Moreover, he has co-applied for a US-patent, and has co-authored several EU research proposals. On top of that, he has been awarded a number of industry and academic research grants, e.g., by Amazon and the Polytechnic University of Milan. He has been actively involved in the research and development of up-and-running mobile recommender systems for the food (ChefPad) and tourism domains (South Tyrol Suggests). He has provided various types of community services, such as co-organizing the ACM 2017 RecSys challenge (organized by XING), and has acted as an advisor to the 2018 RecSys challenge (organized by Spotify).

Prof. Dr. Dietmar Jannach [Full Prof. II]
(Infomedia & MediaFutures @ University of Bergen)

Dietmar Jannach is Professor II at the Mediafutures Research Centre and a full professor of Information Systems at AAU Klagenfurt, Austria. Before joining AAU in 2017, he was a full professor of Computer Science at TU Dortmund, Germany. In his research, he focuses on the application of intelligent system technology to practical problems and the development of methods for building knowledge-intensive software applications. In the last years, Dietmar Jannach worked on various practical aspects of recommender systems. He is the main author of the first textbook on the topic published by Cambridge University Press in 2010 and was the co-founder of a tech startup that created an award-winning product for interactive advisory solutions.

Dr. Alain Starke [Assoc. Prof II]
(InfoMedia @ University of Bergen)

Alain Starke (1990) is part of the DARS research group as an adjunct associate professor on recommender systems, working for UiB 1 day per week. He first joined DARS on a ‘Niels Stensen Fellowship’ in 2019. His main affiliation is assistant professor at the Amsterdam School of Communication Research (ASCoR), at the University of Amsterdam in the Netherlands (NL). His research focuses on recommender systems, consumer and marketing psychology, and behavioral change, examining how changes in food and news preferences can be achieved using recommender systems. Previously, Alain was affiliated with TU Eindhoven (NL) as a PhD student (funded by a 'Research Talent' grant from the Netherlands Organization for Scientific Research), and with Wageningen University and Research (NL) as a postdoc.

Erik Knudsen [Researcher]
(InfoMedia & MediaFutures @ UiB)

Knudsen's research focuses on the effects of recommender systems on audience fragmentation and polarization, as well as more generally digital journalism, trust in journalism, selective exposure, and political communication.

Arngeir Berge [PhD Student]
(NORCE / InfoMedia @ UiB)

Arngeir Berge is a PhD student at the DARS research group. After obtaining his MSc at InfoMedia@UiB, he worked two years as an assist.prof. of ICT in Learning in Volda University College, before proceeding to working with e-learning, technology and multimedia in the health area. He currently works with NORCE, the Norwegian Research Centre on the project “RE-AIMED: Readjusted responses by use of AI in medical calls”, where DARS is a WP leader. In his PhD project, he studies intelligent user interfaces of recommender systems that offer decision support to medical call centre operators.

Ayoub El Majjodi [PhD Student]
(InfoMedia @ UiB)

Ayoub El Majjodi is a PhD student at the DARS research group. After he obtained his MSc in Data Science and Big Data in 2019, he worked as a research assistant at Tempere University, Finland. The main focus of his research project lies within Recommender Systems for the food domain, where he will investigate the utility of Recommender systems and Digital nudging to change people's eating behaviors and help them to attain healthier eating habits.

Anastasia Klimashevskaya [PhD Student]
(MediaFutures / InfoMedia @ UiB)

Anastasia Klimashevskaia is a PhD student at the University of Bergen. Born in 1995 in Orel, Russia, she obtained a bachelor degree at Moscow State University for the Humanities with the main focus on Computational Linguistics, Natural Language Processing and Robotics. To broaden the horizons, Anastasia has moved to Graz, Austria and has completed a master program there at Graz University of Technology. Her master thesis was addressing the problem of creating automated summarisation system for American legislation committees’ transcripts, in an attempt to create a news source utilizing the available legislation data and cater the facts in an accessible way to a wider audience minimising any bias in the new articles generated. This thesis was conducted in collaboration with California Polytechnic State University within a half-a-year research work in San Luis Obispo, California. Excited about solving such complicated tasks and make technology more user-friendly, fair and responsible, Anastasiia has decided to pursue further career in research and has been accepted to the University of Bergen and MediaFutures to a PhD position researching Recommender Systems. Apart from research, she is also passionate about painting and drawing, hiking, cooking and gaming.

Jia-Hua Jeng [PhD Student]
(MediaFutures / InfoMedia @ UiB)

Jia-Hua Jeng is a PhD candidate in WP2 Computational Social Science at MediaFutures and DARS research group. He received two master’s degrees from King’s College London in MSc Data Science and National Taichung University of Education in MSc Digital Content Technology, mainly for AI, Data Mining, Machine Learning, Big Data, Data Analytics, Internet Marketing and Learning Science. He also was a research assistant and teaching assistant at National Taichung University of Education. In addition, he received a BS degree from Providence University in Computer Science & Communication Engineering, majoring in media technology, software development and communication. His research interest is developing technologies involving recommender systems that convey health-related issues for helping people to make wiser decisions. Moreover, he keens on jogging, cooking and travelling.

Khadiga Seddik [PhD Student]
(NewsRec / InfoMedia @ UiB)

Khadiga Seddik is a PhD candidate in Computational Social Science at University of Bergen and MediaFutures. She received M.Sc. degree in Natural Language Processing and Computational Linguistics from the faculty of computer sciences and artificial intelligence, Cairo university, Egypt. She worked 6 years as lecturer assistant at Modern Sciences and Arts University in Egypt and 3 years as java developer in an R&D lab where she participated in developing and maintaining an enterprise search engine, NLP applications, and web services. Her PhD project is part of NEWSREC project which focuses on the impact of algorithmic recommender technology on democracy. Her research focuses on designing and developing the first recommender system equipped with factors that increase or decrease selective exposure and sharing.

Bilal Mahmood

Bilal is a Ph.D. Research Fellow at the MediaFutures. He completed his master's in Computational Data Science from the Free University of Bolzano and has worked with startups, mid-sized companies, and as a freelancer in the position of a data scientist. His main interest is in applying data science tools to solve business and societal problems. When not working, he loves learning new things, traveling, and enjoying nature.

Daniel Rosnes

Daniel Rosnes is a Research Assistant at MediaFutures. He has recently completed a Master's degree in Information Science at the University of Bergen as part of the DARS research group.

Anders Sandvik Bremnes

Anders Sandvik Bremnes is a Research Assistant at MediaFutures. He is currently undergoing a Bachelor’s degree in Computer Science at the University of Bergen, and he holds a Bachelor’s degree in Social Economics.

Gloria Anne Babile Kasangu

Gloria Anne Babile Kasangu is a Research Assistant at MediaFutures. She’s currently undertaking a Bachelor’s degree in Information Science at the University of Bergen, and holds a Bachelor’s degree in General Psychology. When she’s not studying or working, she enjoys cooking, writing, and working out.

• Ayoub EL Majjodi (InfoMedia @ University of Bergen)
• Arngeir Berge (NORCE / InfoMedia @ University of Bergen)
• Anastasia Klimashevskaya (MediaFutures / InfoMedia @ UiB)
• Jia-Hua Jeng (MediaFutures / InfoMedia @ UiB)
• Khadiga Seddik (NewsRec / InfoMedia @ UiB)
• Bilal Mahmood (MediaFutures / InfoMedia @ UiB)

• Elias Brynestad (InfoMedia @ University of Bergen)
• Anastasia Vlasenko (Informatics @ University of Bergen) - MA thesis successfully defended
• Frank Rune Espeseth (InfoMedia @ University of Bergen) - MA thesis successfully defended
• Sebastian Cornelius Bergh (InfoMedia @ University of Bergen) - MA thesis successfully defended
• Peter Kolbeinsen Klingenberg (InfoMedia @ University of Bergen) - MA thesis successfully defended
• Daniel Rosnes (InfoMedia @ University of Bergen) - MA thesis successfully defended
• Anna Halvorsen Nilsen (InfoMedia @ University of Bergen) - MA thesis successfully defended
• Tiril Staveteig Taalesen (InfoMedia @ University of Bergen) - MA thesis successfully defended
• David René Bødtker (InfoMedia @ University of Bergen)
• Vegard Rygh Solberg (InfoMedia @ University of Bergen) - MA thesis successfully defended
• David Kvasnes Olsen (InfoMedia @ University of Bergen) - MA thesis successfully defended
• Daniel Christopher Jakobsen (InfoMedia @ University of Bergen) - MA thesis successfully defended
• Andira Aranadita (Faculty of Medicine @ University of Bergen) - MA thesis successfully defended
• Aslaug Angelsen (Faculty of Medicine @ University of Bergen) - MA thesis successfully defended
• Lars Giske Holth (InfoMedia @ University of Bergen) - MA thesis successfully defended
• Arien Shibani (InfoMedia @ University of Bergen) - MA thesis successfully defended
• Edis Asotic (InfoMedia @ University of Bergen) - MA thesis successfully defended
• Oyvind Johannessen (InfoMedia @ University of Bergen) - MA thesis successfully defended
• Tord Kvifte (InfoMedia @ University of Bergen) - MA thesis successfully defended
• Jørgen Nyborg-Christensen (InfoMedia @ University of Bergen) - MA thesis successfully defended
• Johnny Bjånesøy (InfoMedia @ University of Bergen) - MA thesis successfully defended
• Alexandra Kimberly Bobrow (InfoMedia @ University of Bergen) - MA thesis successfully defended
• Sebastian Øverhaug Larsen (InfoMedia @ University of Bergen) - MA thesis successfully defended
• Jørgen Lie Toft (InfoMedia @ University of Bergen) - MA thesis successfully defended
• Markus Torgersen (Norwegian School of Economics) - MA thesis successfully defended
• Marek Vetter (Norwegian School of Economics) - MA thesis successfully defended
• Tim Soltvedt Aadland (InfoMedia @ University of Bergen) - MA thesis successfully defended
• Felipe Sepulveda (InfoMedia @ University of Bergen) - MA thesis successfully defended
• Gøran A. Slettemark (InfoMedia @ University of Bergen) - MA thesis successfully defended

Prof. Dr. Peter Brusilovsky
(University of Pittsburgh)

Peter Brusilovsky is a professor of Information science and Intelligent Systems (Artificial intelligence) at the University of Pittsburgh. He is known as one of the pioneers of Adaptive hypermedia, Adaptive Web, and Web-based Adaptive learning. He also published numerous articles in user modeling, personalization, educational technology, intelligent tutoring systems, and information access. Brusilovsky is ranked as #1 in the world in the area of Computer Education and #21 in the world in the area of World Wide Web by Microsoft Academic Search. According to Google Scholar, he has over 30,000 citations and h-index of 70. Brusilovsky's group has been awarded best paper awards at Adaptive Hypermedia, User Modeling, Hypertext, IUI, ICALT, and EC-TEL conference series. Among these awards are five prestigious James Chen Best Student paper awards.

Prof. Dr. Judith Masthoff
(Utrecht University)

Judith Masthoff is a chair in Computing Science at Utrecht University, NL. Her research is in personalisation and intelligent user interfaces. She is interested in personalizing behavior change mechanisms for encouraging people to live more healthily and sustainably, and in adapting motivating and emotional support messages to personality. She has co-organized several events such as workshops on behavior change technology, and given tutorials on personalization for behaviour change. She serves as the editor in-chief of User Modeling and User-Adapted Interaction, and has guest-edited a special issue on Personalization and Behavior Change. She is a director of User Modeling Inc., the professional association of user modeling researchers.

Prof. Dr. Francesco Ricci
(Free University of Bozen)

Francesco Ricci is full professor and dean of the Faculty of Computer Science at the Free University of Bozen. Since November 2012 he is with the Information and Database System Engineering research area. F. Ricci has established in Bolzano a reference point for the research on Recommender Systems. He has co-edited the Recommender Systems Handbook (Springer 2011, 2015), and has been actively working in this community as President of the Steering Committee of the ACM conference on Recommender Systems (2007-2010). He was previously (from 2000 to 2006) technical director of the eCommerce and Tourism Research Lab (eCTRL) at ITC-irst (Trento, Italy). F. Ricci is author of more than one hundred fifty refereed publications and, according to Google Scholar, has H-index 52 and around 17,000 citations.

Dr. Martin Atzmüller
(University of Tilburg)

Dr. Matthias Braunhofer
(Microsoft)

Dr. Iván Cantador
(Aut. University of Madrid)

Dr. Paolo Cremonesi
(Politecnico di Milano)

Dr. David Elsweiler
(University of Regensburg)

Dr. Mouzhi Ge
(Masaryk University)

Dr. Frode Guribye
(University of Bergen)

Dr. Eelco Herder
(Rad. Universiteit Nijmegen)

Dr. Nabil El Ioini
(Free University of Bozen)

Dr. Leandro Marinho
(UFCG)

Farshad B. Moghaddam
(Karlsruhe Institute of Tech.)

Dr. Cataldo Musto
(University of Bari)

Dr. Duc Tien Dang Nguyen
(University of Bergen)

Dr. Andreas Opdahl
(University of Bergen)

Dr. Denis Parra
(PUC Chile)

Dr. Neil Rubens
(Stanford University)

Dr. Alan Said
(University of Gothenburg)

Hanna Schäfer
(University of Konstanz)

Dr. Marija Slavkovik
(University of Bergen)

Dr. Bjørnar Tessem
(University of Bergen)

Dr. Marko Tkalčič
(University of Primorska)

Dr. Barbara Wasson
(University of Bergen)

Dr. Martijn Willemsen
(TU Eindhoven / Jheronimus Academy of Data Science)

• The Interplay between Food Knowledge, Nudges, and Preference Elicitation Methods Determines the Evaluation of a Recipe Recommender System. El Majjodi, A., Starke, A., Elahi, M. & Trattner, C., INTRS workshop at ACM RecSys 2023, 2023. PDF
• Topical Preference Trumps Other Features in News Recommendation: A Conjoint Analysis on a Representative Sample from Norway. Knudsen, E., Starke, A. & Trattner, C. INRA workshop at ACM RecSys 2023, 2023. PDF
• Evaluating The Effects of Calibrated Popularity Bias Mitigation: A Field Study. Klimashevskaia, A., Elahi, M., Jannach, D., Sjærven, L., Tessem, A. & Trattner, C. ACM Recsys 2023 LBR, 2023. PDF
• Understanding How News Recommender Systems Influence Selective Exposure. Seddik, K., Knudsen, E., Trilling, D. & Trattner, C. BehavRec workshop at ACM RecSys 2023, 2023. PDF
• "Tell Me Why": Using Natural Language Justifications in a Recipe Recommender System to Support Healthier Food Choices. Starke, A. D., Cataldo, M., Rapp, A., Semeraro, G. & Trattner, C. UMUAI, 2023. PDF
• Designing for Control in Nurse-AI Collaboration During Emergency Medical Calls. Berge, A., Guribye, F., Schmidt Fotland, S., Fonnes, G., Hjulstad Johansen, I., Trattner, C. ACM DIS 2023. Best Paper Award Honorable Mention PDF
• Understanding and predicting cross-cultural food preferences with online recipe images. Zhang, Q., Elsweiler, D. & Trattner, C. Information Processing & Management, 2023. PDF
• Addressing Popularity Bias in Recommender Systems: An Exploration of Self-Supervised Learning Models. Klimashevskaia, A., Elahi, M., Trattner, C. ACM UMAP 2023. PDF
• Towards Attitudinal Change in News Recommender Systems: A Pilot Study on Climate Change. Jeng, H. J., Starke, A., Trattner, C. Persuasion AI workshop at Persuasive 2023, 2023. PDF
• Healthiness and environmental impact of dinner recipes vary widely across developed countries. Angelsen, A., Starke, A. D. & Trattner, C. Nature Food, 2023. PDF
• Unifying Recommender Systems and Conversational User Interfaces. Starke, A.D., Lee, M., 4th Conference on Conversational User Interfaces (CUI 2022), July 26–28, 2022, Glasgow, United Kingdom. ACM, New York, NY, USA, 7 pages. PDF
• Examining Choice Overload across Single-list and Multi-list User Interfaces. Starke, A.D., Sedkowska, J., Chouhan, M., Ferwerda, B., IntRS'22: Joint Workshop on Interfaces and Human Decision Making for Recommender Systems, September 22, 2022, Seattle, US (hybrid event). PDF
• Boosting Health? Examining the Role of Nutrition Labels and Preference Elicitation Methods in Food Recommendation. El Majjodi, A., Starke, A.D., Trattner, C., IntRS'22: Joint Workshop on Interfaces and Human Decision Making for Recommender Systems, September 22, 2022, Seattle, US (hybrid event). PDF
• Nudging Towards Health in a Conversational Food Recommender System Using Multi-Modal Interactions and Nutrition Labels. Castiglia, G., El Majjodi, A., Calò, F., Deldjoo, Y., Narducci, F., Starke, A.D., Trattner, C., RecSys’22: 4th Workshop of Knowledge-aware and Conversational Recommender Systems, Seattle, WA, USA. 2022. PDF
• Nudging towards health? Examining the merits of nutrition labels and personalization in a recipe recommender system. El Majjodi, A., Starke, A.D., Trattner, C., 29th ACM International Conference on User Modeling, Adaptation and Personalization (UMAP '22). 2022. PDF
• Promoting Energy-Efficient Behavior by Depicting Social Norms in a Recommender Interface. Starke, A.D., Willemsen, M.C., Snijders, C.C.P., ACM Transactions on Interactive Intelligent Systems (TiiS). PDF
• Developing and Evaluating a University Recommender System. Elahi, M., Starke, A. D., El Ioini, N., Lambrix, A. A., & Trattner, C., Frontiers in Artificial Intelligence, 2022. PDF
• A Convolutional Attention Network for Unifying General and Sequential Recommenders. S. Yakhchi, A. Behehsti, S.-m. Ghafari, I. Razzak, M. Orgun, and M. Elahi, Information Processing & Management 59 102755, 2022 LINK
• Towards Responsible Media Recommendation. Elahi, M., Jannach, D., Skjærven, L., Knudsen, E., Sjøvaag, H., Tolonen, K., Holmstad, Ø., Pipkin, I., Throndsen, E., Stenbom, A., Fiskerud, E., Oesch, A., Vredenberg, L. and Trattner, C. AI & Ethics, Springer Nature. 2021. PDF
• Predicting Feature-based Similarity in the News Domain Using Human Judgments. Starke, A., Øverhaug L. and Trattner, C. 9th International Workshop on News Recommendation and Analytics (INRA 2021) held at RecSys'21. 2021. PDF
• Addressing the New Item problem in video recommender systems by incorporation of visual features with restricted Boltzmann machines. N. Hazrati and M. Elahi, Expert Systems 38, 2021 PDF
• From Trustworthy Data to Trustworthy IoT: A Data Collection Methodology Based on Blockchain. C. A. Ardagna, R. Asal, E. Damiani, N. E. Ioini, M. Elahi, and C. Pahl, ACM Transactions on Cyber-Physical Systems 5 1--26, 2021 LINK
• The Cholesterol Factor: Balancing Accuracy and Health in Recipe Recommendation Through a Nutrient-Specific Metric. Starke, A., Trattner, C., Bakken, H., Johannessen, M. and Solberg, V. Workshop on Multi-Objective Recommender Systems (MORS) held at RecSys'21. PDF
• Serving Each User: Supporting Different Eating Goals Through a Multi-List Recommender Interface. Starke, A.D., Asotic, E., Trattner, C. Fifteenth ACM Conference on Recommender Systems (RecSys ’21). PDF
• A day at the races: using best arm identification algorithms to reduce the cost of information retrieval user studies. Losada, D., Elsweiler, D., Harvey, M. and Trattner, C. Applied Intelligence. 2021. PDF
• Exploring the Effects of Natural Language Justifications on Food Recommender Systems. Musto, C., Starke, A., Trattner, C., Rapp, A. and Semeraro, G. 29th ACM International Conference on User Modeling, Adaptation and Personalization (UMAP '21). 2021. PDF
• Nudging Healthy Choices in Food Search Through List Re-Ranking. Starke, A.D., Kløverød Brynestad, E., Hauge, S. Sandal Løkeland, L. UMAP '21: Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization LINK
• Investigating the impact of recommender systems on user-based and item-based popularity bias. M. Elahi, D. K. Kholgh, M. S. Kiarostami, S. Saghari, S. P. Rad, and M. Tkalčič, Information Processing & Management 58 102655, 2021 PDF
• Beyond Algorithmic Fairness in Recommender Systems. M. Elahi, H. Abdollahpouri, M. Mansoury, and H. Torkamaan, Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization 41--46, 2021 PDF
• AudioLens: Audio-Aware Video Recommendation for Mitigating New Item Problem. M. H. Rimaz, R. Hosseini, M. Elahi, and F. B. Moghaddam, AI-PA 2020 - 1st International Workshop on AI-enabled Process Automation - (ICSOC 2020), 2021 PDF
• Recommender Systems: Challenges and Opportunities in the Age of Big Data and Artificial Intelligence. M. Elahi, A. Beheshti, and S. R. Goluguri, 15--39, 2021 PDF
• MORS 2021: 1st Workshop on Multi-Objective Recommender Systems. H. Abdollahpouri, M. Elahi, M. Mansoury, S. Sahebi, Z. Nazari, A. Chaney, and B. Loni, Fifteenth ACM Conference on Recommender Systems 787--788, 2021 PDF
• Using Explanations as Energy-Saving Frames: A User-Centric Recommender Study. Starke, A.D., Willemsen, M.C., Snijders, C. UMAP '21: Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization. LINK
• Conversational Futures: Emancipating Conversational Interactions for Futures Worth Wanting. Lee, M., Noortman, R., Zaga, C., Starke, A., Huisman, G., Andersen, K. In CHI'21: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 2021. PDF
• Promoting Healthy Food Choices Online: A Case for Multi-list Recommender Systems. Starke, A.D., & Trattner, C. In: HEALTHI’21: Joint Proceedings of ACM IUI 2021 Workshops. 2021. PDF
• Changing Salty Food Preferences with Visual and Textual Explanations in a Search Interface. Berge, A., Velle Sjøen, V., Starke, A.D., & Trattner, C. In: HEALTHI’21: Joint Proceedings of ACM IUI 2021 Workshops. 2021. PDF
• Using Natural Language Processing and Artificial Intelligence to Explore the Nutrition and Sustainability of Recipes and Food. Erp, M., Reynolds, C., Maynard, D., Ibañez-Martín, R., Starke, A.D., Andres, F., Leite, M.A.C., Alvarez de Toledo, D., Schmidt Rivera, X., Trattner, C., and others. Frontiers in Artificial Intelligence. Full Article: LINK.
• Nudging Healthy Choices in Food Search Through Visual Attractiveness. Starke, A.D., Willemsen, M.C., Trattner, C. Frontiers in Artificial Intelligence, 2021. Preprint: PDF
• Recommender Systems in the Healthcare Domain: State-of-the-Art and Research Issues. Tran, T.N.T, Felfernig, A., Trattner, C. and Holzinger, A. Journal of Intelligent Information Systems. 2020. PDF
• Videos in Cold Start with Automatic Visual Tags Elahi, M., Moghaddam, F., Hoseini, R., Rimaz, M., Ioini, N.E., Tkalcic, M., Trattner, C. and Tillo, T. 29th ACM International Conference on User Modeling, Adaptation and Personalization (UMAP '21). 2021. PDF DATA
• Visually-Aware Video Recommendation in the Cold Start. M. Elahi, R. Hosseini, M. H. Rimaz, F. B. Moghaddam, and C. Trattner, ACM Conference on Hypertext and Social Media (ACM HT'20), 2020 PDF
• Towards a Knowledge-aware Food Recommender System Exploiting Holistic User Models Musto, C., Trattner, C., Starke, A.D., Semeraro, G. Proceedings of the 28th Conference on User Modeling, Adaptation and Personalization (UMAP'20) PDF DOI.
• Towards Generating Personalized Country Recommendation. A. E. Majjodi, M. Elahi, N. E. Ioini, and C. Trattner, ACM Conference on User Modeling, Adaptation and Personalization (ACM UMAP 2020), 2020 PDF
• Exploring Personalized University Ranking and Recommendation.. Elahi, M., EL Ioini, N., Alexander Lambrix, A., Ge, M. Adjunct proceedings of the 28th Conference on User Modeling, Adaptation and Personalization (UMAP'20), Upcoming. PDF
• Beyond "one-size-fits-all" platforms: Applying Campbell's paradigm to test personalized energy advice in the Netherlands. Starke, A.D., Willemsen, M.C. and Snijders, C.C.P. Energy Research and Social Science, 2020. PDF
• Optical flow estimation with deep learning, a survey on recent advances. S. Savian, M. Elahi, and T. Tillo, 257--287, 2020 PDF
• Simulating the Impact of Recommender Systems on the Evolution of Collective Users' Choices. N. Hazrati, M. Elahi, and F. Ricci, Proceedings of the 31st ACM Conference on Hypertext and Social Media 207--212, 2020 PDF
• Fashion Recommender Systems in Cold Start. M. Elahi and L. Qi, 3--21, Book Chapter in Fashion Recommender Systems (pp. 3-21). Springer, 2020 PDF
• With a little help from my peers: Depicting social norms in a recommender interface to promote energy conservation. Starke, A.D., Willemsen, M.C. and Snijders, C.C.P. Proceedings of the 25th International Conference of Intelligent User Interfaces (IUI), 2020. PDF
• Learning to Recommend Similar Items from Human Judgements. Trattner, C. and Jannach, D. User Modeling and User-Adapted Interaction Journal. 2019. PDF
• What online data say about eating habits. Trattner, C. and Elsweiler, D. NATURE Sustainability, 2019. PDF
• Tag-Based Information Access in Image Collections: Insights from Log and Eye-Gaze Analyses. Lin, Y., Parra, D., Trattner, C. and Brusilovsky, P. Knowledge and Information Systems. 2019. PDF
• The Roadmap to User-Controllable Social Exploratory Search. di Sciascio, C., Brusilovsky, P., Trattner, C. and Veas, E. ACM Transactions on Interactive Intelligent Systems. 2019. PDF
• Investigating and Predicting Online Food Recipe Upload Behavior. Trattner, C., Kusmierczyk, T. and Norvag, K. Information Processing and Management. 2019. PDF
• Movie Genome Recommender: A Novel Recommender System Based on Multimedia Content. Deldjoo, Y., Schedl, M. and Elahi, M. International Conference on Content-Based Multimedia Indexing (CBMI), 2019 PDF
• Supporting energy-efficient choices using Rasch-based recommender interfaces. Starke, A.D. Dissertation - Eindhoven University of Technology, 2019. PDF
• RecSys Challenges in achieving sustainable eating habits. Starke, A.D. Proceedings of the 4th workshop on Health Recommender Systems, 2019. PDF
• The effectiveness of advice solicitation and social peers in an energy recommender system. Starke, A.D. Proceedings of the 6th Joint Workshop on Interfaces and Human Decision-making, 2019. PDF
• Prediction of Music Pairwise Preferences from Facial Expressions. Tkalčič, M., Maleki, N., Pesek, M., Elahi, M., Ricci, F. and Marolt, M. Proceedings of the 24th International Conference on Intelligent User Interfaces (IUI), 2019. PDF
• Exploring the Power of Visual Features for Recommendation of Movies. Rimaz, M. H., Elahi, M., Bakhshandegan Moghadam, F., Trattner, C., Hosseini, R. and Tkalčič, M. ACM Conference on User Modelling, Adaptation and Personalization (UMAP), 2019. PDF
• Cold Start Solutions For Recommendation Systems. Moghaddam, F. B. and Elahi, M., Big Data Recommender Systems: Recent Trends and Advances, 2019. PDF
• Analysing Recommender Systems Impact on Users’ Choices. Hazrati, N., Elahi, M. and Ricci, F. ImpactRS Workshop, 2019. PDF
• Predicting Movie Popularity and Ratings with Visual Features. Moghaddam, F. B., Elahi, M., Hosseini, R., Trattner, C., & Tkalčič, M. IEEE SMAP, 2019. PDF