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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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publications
Investigating Potential Factors Associated with Gender Discrimination in Collaborative Recommender Systems
Masoud Mansoury, Himan Abdollahpouri, Jessie Smith, Arman Dehpanah, Mykola Pechenizkiy, Bamshad Mobasher
In the 33nd International Florida Artificial Intelligence Research Society Conference in Cooperation with AAAI, 2020
FairMatch: A Graph-based Approach for Improving Aggregate Diversity in Recommender Systems
Masoud Mansoury, Himan Abdollahpouri, Mykola Pechenizkiy, Bamshad Mobasher, Robin Burke
In the 28th Conference on User Modeling, Adaptation and Personalization, 2020
Multi-sided Exposure Bias in Recommendation
Himan Abdollahpouri, Masoud Mansoury
In Workshop on Industrial Recommendation Systems in Conjuction with ACM KDD, 2020
Feedback Loop and Bias Amplification in Recommender Systems
Masoud Mansoury, Himan Abdollahpouri, Mykola Pechenizkiy, Bamshad Mobasher, Robin Burke
In the 29th ACM International Conference on Information and Knowledge Management, 2020
Fairness-aware Recommendation with librec-auto
Nasim Sonboli, Robin Burke, Zijun Liu, Masoud Mansoury
In the Proceedings of the 14th ACM Conference on Recommender Systems, 2020
The Connection between Popularity Bias, Calibration, and Fairness in Recommendation
Himan Abdollahpouri, Masoud Mansoury, Robin Burke, Bamshad Mobasher
In the Proceedings of the 14th ACM Conference on Recommender Systems, 2020
Fairness-aware Recommendation in Multi-sided Platforms
Masoud Mansoury
In Proceedings of the 14th ACM International Conference on Web Search and Data Mining, 2021
Flatter is better: Percentile Transformations for Recommender Systems
Masoud Mansoury, Robin Burke, Bamshad Mobasher
ACM Transactions on Intelligent Systems and Technology, 12 no. 2, 2021
Beyond Algorithmic Fairness in Recommender Systems
Mehdi Elahi, Himan Abdollahpouri, Masoud Mansoury, Helma Torkamaan
In Adjunct proceedings of the 29th ACM conference on user modeling, adaptation and personalization, 2021
User-centered evaluation of popularity bias in recommender systems
Himan Abdollahpouri, Masoud Mansoury, Robin Burke, Bamshad Mobasher, Edward Malthouse
In Proceedings of the 29th ACM conference on user modeling, adaptation and personalization, 2021
Librec-auto: A tool for recommender systems experimentation
Nasim Sonboli, Masoud Mansoury, Ziyue Guo, Shreyas Kadekodi, Weiwen Liu, Zijun Liu, Andrew Schwartz, Robin Burke
In the 30th ACM International Conference on Information and Knowledge Management, 2021
A Graph-based Approach for Mitigating Multi-sided Exposure Bias in Recommender Systems
Masoud Mansoury, Himan Abdollahpouri, Mykola Pechenizkiy, Bamshad Mobasher, Robin Burke
ACM Transactions on Information Systems, 40 no. 2, 2021
Exposure-Aware Recommendation using Contextual Bandits
Masoud Mansoury, Bamshad Mobasher, Herke van Hoof
In 5th FAccTRec Workshop on Responsible Recommendation in conjunction with ACM RecSys 2022
Career Path Recommendations for Long-term Income Maximization: A Reinforcement Learning Approach
Spyros Avlonitis, Dor Lavi, Masoud Mansoury, David Graus
In Workshop on Recommender Systems for Human Resources in conjunction with ACM RecSys 2023
Fairness of Exposure in Dynamic Recommendation
Masoud Mansoury, Bamshad Mobasher
In CONSEQUENCE Workshop on Causality, Counterfactuals, and Sequential Decision-Making in conjunction with ACM RecSys 2023
Predictive Uncertainty-based Bias Mitigation in Ranking
Maria Heuss, Daniel Cohen, Masoud Mansoury, Maarten de Rijke, Carsten Eickhoff
In the 32nd ACM International Conference on Information and Knowledge Management, 2023
Potential Factors Leading to Popularity Unfairness in Recommender Systems: A User-Centered Analysis
Masoud Mansoury, Finn Duijvestijn, Imane Mourabet
In BNAIC Joint International Scientific Conference on AI and Machine Learning, 2023
Measuring Item Fairness in Next Basket Recommendation: A Reproducibility Study
Yuanna Liu, Ming Li, Mozhdeh Ariannezhad, Masoud Mansoury, Mohammad Aliannejadi, Maarten de Rijke
In the 46th European Conference on Information Retrieval, 2024
Beyond Static Calibration: The Impact of User Preference Dynamics on Calibrated Recommendation
Kun Lin, Masoud Mansoury, Farzad Eskandanian, Milad Sabouri, Bamshad Mobasher
The 32nd ACM Conference on User Modeling, Adaptation and Personalization (LBR), 2024
Going Beyond Popularity and Positivity Bias: Correcting for Multifactorial Bias in Recommender Systems (Best Paper Nominee)
Jin Huang, Harrie Oosterhuis, Masoud Mansoury, Herke van Hoof, Maarten de Rijke
In the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2024
Correcting for Popularity Bias in Recommender Systems via Item Loss Equalization
Juno Prent, Masoud Mansoury
In International Workshop on Recommender Systems for Sustainability and Social Good in conjunction with ACM RecSys 2024
Mitigating Exposure Bias in Online Learning to Rank Recommendation: A Novel Reward Model for Cascading Bandits
Masoud Mansoury, Bamshad Mobasher, Herke van Hoof
In the 33rd ACM International Conference on Information and Knowledge Management, 2024
Towards Carbon Footprint-Aware Recommender Systems for Greener Item Recommendation
Raoul Kalisvaart, Masoud Mansoury, Alan Hanjalic, Elvin Isufi
In ACM Transactions on Recommender Systems, 2025
Towards Explainable Temporal User Profiling with LLMs
Milad Sabouri, Masoud Mansoury, Kun Lin, Bamshad Mobasher
In Adjunct Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization, 2025
A Reproducibility Study of Product-side Fairness in Bundle Recommendation
Huy Son Nguyen, Yuanna Liu, Masoud Mansoury, Mohammad Alian Nejadi, Alan Hanjalic, Maarten de Rijke
In Proceedings of the 19th ACM Conference on Recommender Systems, 2025
Mitigating Popularity Bias in Counterfactual Explanations using Large Language Models
Arjan Hasami, Masoud Mansoury
In Proceedings of the 19th ACM Conference on Recommender Systems, 2025
Opening the Black Box: Interpretable Remedies for Popularity Bias in Recommender Systems
Parviz Ahmadov, Masoud Mansoury
In Proceedings of the 19th ACM Conference on Recommender Systems, 2025
Using LLMs to Capture Users’ Temporal Context for Recommendation
Milad Sabouri, Masoud Mansoury, Kun Lin, Bamshad Mobasher
In Workshop on Context-Aware Recommender Systems in conjunction with ACM RecSys 2025
RaMen: Multi-Strategy Multi-Modal Learning for Bundle Construction
Huy-Son Nguyen, Quang-Huy Nguyen, Duc-Hoang Pham, Duc-Trong Le, Hoang-Quynh Le, Padipat Sitkrongwong, Atsuhiro Takasu, Masoud Mansoury
In Proceedings of the 28th European Conference on Artificial Intelligence (ECAI), 2025
talks
Talk 1 on Relevant Topic in Your Field
UC San Francisco, Department of Testing
Tutorial 1 on Relevant Topic in Your Field
UC-Berkeley Institute for Testing Science
Talk 2 on Relevant Topic in Your Field
London School of Testing
Conference Proceeding talk 3 on Relevant Topic in Your Field
Testing Institute of America 2014 Annual Conference
teaching
Teaching experience 1
University 1, Department
Teaching experience 2
University 1, Department
