Mehdi Elahi is an Associate Professor at the University of Bergen (UiB). He obtained his Ph.D. in Computer Science and has since published more than 100 peer-reviewed journal and conference papers. His research interest primarily focuses on AI and Data Analytics, with an emphasis on their industrial applications, particularly in Recommender Systems. He is also the co-inventor and co-owner of a US patent in this field. Mehdi Elahi has been involved in the development of several grant proposals, including a large-scale project with a budget of nearly NOK 300 million (i.e., MediaFutures) where he has served as WP-Leader (ongoing for 8 years). In his research, he has collaborated with the largest media platforms in Norway. Previously, he received prestigious research credits from major IT companies such as Amazon. His research findings have been published in some of the top outlets in the field, including a book on Social Data Analytics. He has also organized international events, including data challenges, in collaboration with leading companies such as Spotify and XING.

Masters thesis

• Haaland, Vanessa Marie; Elahi, Mehdi; Pipkin, Igor (2024). Personalized Advertisement Recommendations Using Implicit Feedback. (external link)
• Elahi, Mehdi; Skjærven, Lars; Johnsen Tessem, Astrid et al. (2024). Large-scale Evaluation of Context-Aware Recommender Systems in Media Domain. (external link)
• Elahi, Mehdi; Espeseth, Frank Rune; Pipkin, Igor (2023). Media Analytics for Personalization in Advertisement. (external link)
• Elahi, Mehdi; Bergh, Sebastian Cornelius; Skjærven, Lars et al. (2023). Personalized Recommendations of Upcoming Sport Events. (external link)
• Elahi, Mehdi; Klingenberg, Peter Kolbeinsen; Husken, Thomas (2023). Using content- and behavioural data for recommendations in the Norwegian news market. (external link)
• Elahi, Mehdi; Olsen, David Kvasnes; Skjærven, Lars (2022). Movie recommendation based on stylistic visual features.. (external link)
• Elahi, Mehdi; Johannessen, Øyvind (2021). Novel Methods Using Human Emotion and Visual Features for Recommending Movies. (external link)
• Elahi, Mehdi; Kvifte, Tord (2021). Video Recommendations Based on Visual Features Extracted with Deep Learning. (external link)

Academic article

• Motamedi, Elham; Kholgh, Danial Khosh; Saghari, Sorush et al. (2024). Predicting movies’ eudaimonic and hedonic scores: A machine learning approach using metadata, audio and visual features. (external link)
• Mahmood, Bilal; Elahi, Mehdi; Touileb, Samia et al. (2024). Can Large Language Models Support Editors Pick Related News Articles?. (external link)
• el Majjodi, Ayoub; Khan, Sohail Ahmed; Starke, Alain Dominique et al. (2024). Advancing Visual Food Attractiveness Predictions for Healthy Food Recommender Systems. (external link)
• Savian, Stefano; Elahi, Mehdi; Janes, Andrea Alexander et al. (2023). Benchmarking equivariance for Deep Learning based optical flow estimators. (external link)
• Elahi, Mehdi; Khosh Kholgh, Danial; Kiarostami, Mohammad Sina et al. (2023). Hybrid recommendation by incorporating the sentiment of product reviews. (external link)
• Elahi, Mehdi; Starke, Alain Dominique; El Ioini, Nabil et al. (2022). Developing and Evaluating a University Recommender System. (external link)
• Yakhchi, Shahpar; Beheshti, Amin; Ghafari, Seyed-mohssen et al. (2022). A Convolutional Attention Network for Unifying General and Sequential Recommenders. (external link)
• Zarei, Mohammad Reza; Moosavi, Mohammad R.; Elahi, Mehdi (2022). Adaptive trust-aware collaborative filtering for cold start recommendation. (external link)
• Kille, Benjamin Uwe; Lommatzsch, Andreas; Özgöbek, Özlem et al. (2022). News Images in MediaEval 2022. (external link)
• Elahi, Fatemeh; Fazlali, Mahmood; Tabatabaee Malazi, Hadi et al. (2022). Parallel Fractional Stochastic Gradient Descent With Adaptive Learning for Recommender Systems. (external link)
• Elahi, Mehdi; Khosh Kholgh, Danial; Kiarostami, Mohammad Sina et al. (2021). Investigating the impact of recommender systems on user-based and item-based popularity bias. (external link)
• Trattner, Christoph; Jannach, Dietmar; Motta, Enrico et al. (2021). Responsible media technology and AI: challenges and research directions. (external link)
• Kille, Benjamin Uwe; Lommatzsch, Andreas; Özgöbek, Özlem et al. (2021). News Images in MediaEval 2021. (external link)
• Hazrati, Naieme; Elahi, Mehdi (2020). Addressing the New Item problem in video recommender systems by incorporation of visual features with restricted Boltzmann machines. (external link)
• Ardagna, Claudio Agostino; Asal, Rasool; Damiani, Ernesto et al. (2020). From Trustworthy Data to Trustworthy IoT: A Data Collection Methodology Based on Blockchain. (external link)

Academic literature review

• Klimashevskaia, Anastasiia; Jannach, Dietmar; Elahi, Mehdi et al. (2024). A survey on popularity bias in recommender systems. (external link)
• Elahi, Mehdi; Jannach, Dietmar; Skjærven, Lars et al. (2021). Towards Responsible Media Recommendation. (external link)

Academic chapter/article/Conference paper

• Mahmood, Bilal; Elahi, Mehdi; Touileb, Samia et al. (2024). Incorporating Editorial Feedback in the Evaluation of News Recommender Systems. (external link)
• Klimashevskaia, Anastasiia; Elahi, Mehdi; Jannach, Dietmar et al. (2023). Evaluating The Effects of Calibrated Popularity Bias Mitigation: A Field Study. (external link)
• el Majjodi, Ayoub; Starke, Alain Dominique; Elahi, Mehdi et al. (2023). The Interplay between Food Knowledge, Nudges, and Preference Elicitation Methods Determines the Evaluation of a Recipe Recommender System. (external link)
• Kvifte, Tord; Elahi, Mehdi; Trattner, Christoph (2022). Hybrid Recommendation of Movies based on Deep Content Features. (external link)
• Klimashevskaia, Anastasiia; Elahi, Mehdi; Jannach, Dietmar et al. (2022). Mitigating Popularity Bias in Recommendation: Potential and Limits of Calibration Approaches.. (external link)
• Elahi, Mehdi; Abdollahpouri, Himan; Mansoury, Masoud et al. (2021). Beyond Algorithmic Fairness in Recommender Systems. (external link)
• Abdollahpouri, Himan; Elahi, Mehdi; Mansoury, Masoud et al. (2021). MORS 2021: 1st Workshop on Multi Objective Recommender Systems. (external link)
• Elahi, Mehdi; Bakhshandegan Moghaddam, Farshad; Hosseini, Reza et al. (2021). Recommending Videos in Cold Start With Automatic Visual Tags. (external link)
• Elahi, Mehdi; Hosseini, Reza; Rimaz, Mohammad H. et al. (2020). Visually-Aware Video Recommendation in the Cold Start. (external link)
• Hazrati, Naieme; Elahi, Mehdi; Ricci, Francesco (2020). Simulating the Impact of Recommender Systems on the Evolution of Collective Users' Choices. (external link)
• El Majjodi, Ayoub; Elahi, Mehdi; El Ioini, Nabil et al. (2020). Towards Generating Personalized Country Recommendation. (external link)

Academic anthology/Conference proceedings

• Klimashevskaia, Anastasiia; Elahi, Mehdi; Trattner, Christoph (2023). Addressing Popularity Bias in Recommender Systems: An Exploration of Self-Supervised Learning Models. (external link)
• Abdollahpouri, Himan; Elahi, Mehdi (2021). MORS 2021: Multi-Objective Recommender Systems 2021. (external link)

Textbook

• Beheshti, Amin; Ghodratnama, Samira; Elahi, Mehdi et al. (2022). Social Data Analytics. (external link)

Poster

• Thanh Le, Van; El Ioini, Nabil; Pahl, Claus et al. (2022). Capacity-Based Trust System in Untrusted MEC Environments. (external link)
• Elahi, Mehdi; Moghaddam, Farshad Bakhshandegan; Hosseini, Reza et al. (2021). Enhanced Movie Recommendation Incorporating Visual Features. (external link)
• Elahi, Mehdi; Abdollahpouri, Himan; Mansoury, Masoud et al. (2021). Beyond Algorithmic Fairness in Recommender System. (external link)

See a complete overview of publications in Cristin.

Google Scholar page: https://scholar.google.com/citations?user=aUWF7LYAAAAJ 

MediaFutures page: https://mediafutures.no/2020/11/25/mehdi-elahi/