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Himan Abdollahpouri
Himan Abdollahpouri
Research Scientist at Spotify
Verified email at spotify.com - Homepage
Title
Cited by
Cited by
Year
Controlling popularity bias in learning-to-rank recommendation
H Abdollahpouri, R Burke, B Mobasher
Proceedings of the eleventh ACM conference on recommender systems, 42-46, 2017
3932017
Managing Popularity Bias in Recommender Systems with Personalized Re-ranking
H Abdollahpouri, R Burke, B Mobasher
The 32nd International FLAIRS Conference in Cooperation with AAAI, 2019
2962019
The Unfairness of Popularity Bias in Recommendation
H Abdollahpouri, M Mansoury, R Burke, B Mobasher
Proceedings of the RMSE workshop at the ACM Recsys 2019, 2019
2362019
Multistakeholder recommendation: Survey and research directions
H Abdollahpouri, G Adomavicius, R Burke, I Guy, D Jannach, ...
User Modeling and User-Adapted Interaction, 1-32, 2020
2332020
Feedback Loop and Bias Amplification in Recommender Systems
M Mansoury, H Abdollahpouri, M Pechenizkiy, B Mobasher, R Burke
29th ACM International Conference on Information and Knowledge Management …, 2020
2052020
The connection between popularity bias, calibration, and fairness in recommendation
H Abdollahpouri, M Mansoury, R Burke, B Mobasher
Fourteenth ACM Conference on Recommender Systems, 726-731, 2020
1122020
Popularity Bias in Ranking and Recommendation
H Abdollahpouri
Conference on AI, Ethic and Society (AIES'19), 2019
932019
Towards multi-stakeholder utility evaluation of recommender systems.
RD Burke, H Abdollahpouri, B Mobasher, T Gupta
UMAP (Extended Proceedings) 750, 2016
902016
Multi-stakeholder Recommendation and its Connection to Multi-sided Fairness
H Abdollahpouri, R Burke
Proceedings of the RMSE workshop at the ACM Recsys 2019, 2019
882019
Recommender systems as multistakeholder environments
H Abdollahpouri, R Burke, B Mobasher
Proceedings of the 25th Conference on User Modeling, Adaptation and …, 2017
872017
User-centered Evaluation of Popularity Bias in Recommender Systems
H Abdollahpouri, M Mansoury, R Burke, B Mobasher, E Malthouse
29th ACM Conference on User Modeling, Adaptation and Personalization, 2021
832021
Multi-sided Exposure Bias in Recommendation
H Abdollahpouri, M Mansoury
ACM KDD Workshop on Industrial Recommendation, 2020
752020
Beyond personalization: Research directions in multistakeholder recommendation
H Abdollahpouri, G Adomavicius, R Burke, I Guy, D Jannach, ...
arXiv preprint arXiv:1905.01986, 2019
612019
FairMatch: A Graph-based Approach for Improving Aggregate Diversity in Recommender Systems
M Mansoury, H Abdollahpouri, M Pechenizkiy, B Mobasher, R Burke
28th Conference on User Modeling, Adaptation and Personalization (UMAP 2020), 2020
562020
A graph-based approach for mitigating multi-sided exposure bias in recommender systems
M Mansoury, H Abdollahpouri, M Pechenizkiy, B Mobasher, R Burke
ACM Transactions on Information Systems (TOIS) 40 (2), 1-31, 2021
362021
Investigating Potential Factors Associated with Gender Discrimination in Collaborative Recommender Systems
M Mansoury, H Abdollahpouri, J Smith, A Dehpanah, M Pechenizkiy, ...
33rd FLAIRS Conference in Cooperation with AAAI (2020), 2020
322020
Popularity Bias in Recommendation: A Multi-stakeholder Perspective
H Abdollahpouri
PhD Dissertation, University of Colorado Boulder, 2020
292020
The impact of popularity bias on fairness and calibration in recommendation
H Abdollahpouri, M Mansoury, R Burke, B Mobasher
arXiv preprint arXiv:1910.05755, 2019
262019
Multistakeholder recommender systems
H Abdollahpouri, R Burke
Recommender systems handbook, 647-677, 2021
232021
A constrained optimization approach for calibrated recommendations
S Seymen, H Abdollahpouri, EC Malthouse
Proceedings of the 15th ACM Conference on Recommender Systems, 607-612, 2021
232021
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