• E-mailmehdi.elahi@uib.no
  • Phone+47 55 58 91 79
  • Visitor Address
    Fosswinckels gate 6
    Lauritz Meltzers hus
    5007 Bergen
  • Postal Address
    Postboks 7802
    5020 Bergen

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).

  • Show author(s) (2022). Social Data Analytics. CRC Press.
Academic article
  • Show author(s) (2024). Predicting movies’ eudaimonic and hedonic scores: A machine learning approach using metadata, audio and visual features. Information Processing & Management.
  • Show author(s) (2023). Hybrid recommendation by incorporating the sentiment of product reviews. Information Sciences. 738-756.
  • Show author(s) (2023). Benchmarking equivariance for Deep Learning based optical flow estimators. Signal processing. Image communication.
  • Show author(s) (2022). Parallel Fractional Stochastic Gradient Descent With Adaptive Learning for Recommender Systems. IEEE Transactions on Parallel and Distributed Systems. 470-483.
  • Show author(s) (2022). News Images in MediaEval 2022. CEUR Workshop Proceedings.
  • Show author(s) (2022). Developing and Evaluating a University Recommender System. Frontiers in Artificial Intelligence.
  • Show author(s) (2022). Adaptive trust-aware collaborative filtering for cold start recommendation. Behaviormetrika.
  • Show author(s) (2022). A Convolutional Attention Network for Unifying General and Sequential Recommenders. Information Processing & Management.
  • Show author(s) (2021). Responsible media technology and AI: challenges and research directions. AI and Ethics.
  • Show author(s) (2021). News Images in MediaEval 2021. CEUR Workshop Proceedings.
  • Show author(s) (2021). Investigating the impact of recommender systems on user-based and item-based popularity bias. Information Processing & Management.
  • Show author(s) (2020). From Trustworthy Data to Trustworthy IoT: A Data Collection Methodology Based on Blockchain. ACM Transactions on Cyber-Physical Systems.
  • Show author(s) (2020). Addressing the New Item problem in video recommender systems by incorporation of visual features with restricted Boltzmann machines. Expert Systems.
Academic anthology/Conference proceedings
  • Show author(s) (2023). Addressing Popularity Bias in Recommender Systems: An Exploration of Self-Supervised Learning Models. Association for Computing Machinery (ACM).
  • Show author(s) (2021). MORS 2021: Multi-Objective Recommender Systems 2021. Association for Computing Machinery (ACM).
Masters thesis
  • Show author(s) (2023). Using content- and behavioural data for recommendations in the Norwegian news market.
  • Show author(s) (2023). Personalized Recommendations of Upcoming Sport Events.
  • Show author(s) (2023). Media Analytics for Personalization in Advertisement.
  • Show author(s) (2022). Movie recommendation based on stylistic visual features.
  • Show author(s) (2021). Video Recommendations Based on Visual Features Extracted with Deep Learning.
  • Show author(s) (2021). Novel Methods Using Human Emotion and Visual Features for Recommending Movies.
Academic chapter/article/Conference paper
  • Show author(s) (2023). The Interplay between Food Knowledge, Nudges, and Preference Elicitation Methods Determines the Evaluation of a Recipe Recommender System.
  • Show author(s) (2023). Evaluating The Effects of Calibrated Popularity Bias Mitigation: A Field Study. 6 pages.
  • Show author(s) (2022). Mitigating Popularity Bias in Recommendation: Potential and Limits of Calibration Approaches. 9 pages.
  • Show author(s) (2022). Hybrid Recommendation of Movies based on Deep Content Features.
  • Show author(s) (2021). Recommending Videos in Cold Start With Automatic Visual Tags.
  • Show author(s) (2021). MORS 2021: 1st Workshop on Multi Objective Recommender Systems.
  • Show author(s) (2021). Beyond Algorithmic Fairness in Recommender Systems.
  • Show author(s) (2020). Visually-Aware Video Recommendation in the Cold Start. 5 pages.
  • Show author(s) (2020). Towards Generating Personalized Country Recommendation. 6 pages.
  • Show author(s) (2020). Simulating the Impact of Recommender Systems on the Evolution of Collective Users' Choices. 6 pages.
  • Show author(s) (2022). Capacity-Based Trust System in Untrusted MEC Environments.
  • Show author(s) (2021). Enhanced Movie Recommendation Incorporating Visual Features.
  • Show author(s) (2021). Beyond Algorithmic Fairness in Recommender System.
Academic literature review
  • Show author(s) (2021). Towards Responsible Media Recommendation. AI and Ethics. 12 pages.

More information in national current research information system (CRIStin)


Research groups