Mehdi Elahi is an Associate Professor at University of Bergen (Norway). He received M.Sc. degree in Electrical Engineering (Sweden) in 2010, and Ph.D. degree in Computer Science (Italy) in 2014. Over the last 3 years, he has been serving as an Assistant Professor at Free University of Bozen - Bolzano (Italy), where has researched on various aspects of Recommender Systems. As a result of his research work, he served as a primary author or co-author of more than 60 peer-reviewed publications in AI, RS, and HCI related conferences and journals. He has been actively involved in co-authorship of a US-patent as well as co-authorship of several EU research proposals. He has been awarded a number of industry and academic research grants, e.g., by a world-class company (Amazon), and a well-known academic institute in Italy (Polytechnic University of Milan). He has provided various types of community services such as co-organization of the ACM RecSys challenge 2017 (organized by XING), and advisor of RecSys challenge 2018 (organized by Spotify).

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 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)
• Elahi, Mehdi; Khosh Kholgh, Danial; Kiarostami, Mohammad Sina et al. (2023). Hybrid recommendation by incorporating the sentiment of product reviews. (external link)
• Savian, Stefano; Elahi, Mehdi; Janes, Andrea Alexander et al. (2023). Benchmarking equivariance for Deep Learning based optical flow estimators. (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)
• Yakhchi, Shahpar; Beheshti, Amin; Ghafari, Seyed-mohssen et al. (2022). A Convolutional Attention Network for Unifying General and Sequential Recommenders. (external link)
• Elahi, Mehdi; Starke, Alain Dominique; El Ioini, Nabil et al. (2022). Developing and Evaluating a University Recommender System. (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)
• Trattner, Christoph; Jannach, Dietmar; Motta, Enrico et al. (2021). Responsible media technology and AI: challenges and research directions. (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)
• Kille, Benjamin Uwe; Lommatzsch, Andreas; Özgöbek, Özlem et al. (2021). News Images in MediaEval 2021. (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)
• 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)

Academic chapter/article/Conference paper

• 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)
• Klimashevskaia, Anastasiia; Elahi, Mehdi; Jannach, Dietmar et al. (2023). Evaluating The Effects of Calibrated Popularity Bias Mitigation: A Field Study. (external link)
• Klimashevskaia, Anastasiia; Elahi, Mehdi; Jannach, Dietmar et al. (2022). Mitigating Popularity Bias in Recommendation: Potential and Limits of Calibration Approaches.. (external link)
• Kvifte, Tord; Elahi, Mehdi; Trattner, Christoph (2022). Hybrid Recommendation of Movies based on Deep Content Features. (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)

Masters thesis

• 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; Espeseth, Frank Rune; Pipkin, Igor (2023). Media Analytics for Personalization in Advertisement. (external link)
• Elahi, Mehdi; Olsen, David Kvasnes; Skjærven, Lars (2022). Movie recommendation based on stylistic visual features.. (external link)
• Elahi, Mehdi; Kvifte, Tord (2021). Video Recommendations Based on Visual Features Extracted with Deep Learning. (external link)
• Elahi, Mehdi; Johannessen, Øyvind (2021). Novel Methods Using Human Emotion and Visual Features for Recommending Movies. (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; Abdollahpouri, Himan; Mansoury, Masoud et al. (2021). Beyond Algorithmic Fairness in Recommender System. (external link)
• Elahi, Mehdi; Moghaddam, Farshad Bakhshandegan; Hosseini, Reza et al. (2021). Enhanced Movie Recommendation Incorporating Visual Features. (external link)

See a complete overview of publications in Cristin.

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