• Developing and Evaluating a University Recommender System 

      Elahi, Mehdi; Starke, Alain Dominique; El Ioini, Nabil; Lambrix, Anna Alexander; Trattner, Christoph (Journal article; Peer reviewed, 2022)
      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. ...
    • Hybrid recommendation by incorporating the sentiment of product reviews 

      Elahi, Mehdi; Khosh Kholgh, Danial; Kiarostami, Mohammad Sina; Oussalah, Mourad; Saghari, Sorush (Journal article; Peer reviewed, 2023)
      Hybrid recommender systems utilize advanced algorithms capable of learning heterogeneous sources of data and generating personalized recommendations for users. The data can range from user preferences (e.g., ratings or ...
    • The Interplay between Food Knowledge, Nudges, and Preference Elicitation Methods Determines the Evaluation of a Recipe Recommender System 

      el Majjodi, Ayoub; Starke, Alain Dominique; Elahi, Mehdi; Trattner, Christoph (Chapter, 2023)
      Domain knowledge can affect how a user evaluates different aspects of a recommender system. Recipe recommendations might be difficult to understand, as some health aspects are implicit. The appropri- ateness of a recommender’s ...
    • Investigating the impact of recommender systems on user-based and item-based popularity bias 

      Elahi, Mehdi; Khosh Kholgh, Danial; Kiarostami, Mohammad Sina; Saghari, Sorush; Parsa Rad, Shiva; Tkalcic, Marko (Journal article; Peer reviewed, 2021)
      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 ...
    • Responsible media technology and AI: challenges and research directions 

      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; De Smedt, Koenraad; Moe, Hallvard (Journal article; Peer reviewed, 2021)
      The last two decades have witnessed major disruptions to the traditional media industry as a result of technological breakthroughs. New opportunities and challenges continue to arise, most recently as a result of the rapid ...
    • A survey on popularity bias in recommender systems 

      Klimashevskaia, Anastasiia; Jannach, Dietmar; Elahi, Mehdi; Trattner, Christoph (Journal article; Peer reviewed, 2024)
      Recommender systems help people find relevant content in a personalized way. One main promise of such systems is that they are able to increase the visibility of items in the long tail, i.e., the lesser-known items in a ...
    • Towards Responsible Media Recommendation 

      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 (Journal article; Peer reviewed, 2021)
      Reading or viewing recommendations are a common feature on modern media sites. What is shown to consumers as recommendations is nowadays often automatically determined by AI algorithms, typically with the goal of helping ...