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Himan Abdollahpouri
Himan Abdollahpouri
Research Scientist at Spotify
E-mail confirmado em spotify.com - Página inicial
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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
4892017
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
3662019
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
3212020
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
3072019
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
2962020
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
1612020
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
1352021
Popularity Bias in Ranking and Recommendation
H Abdollahpouri
Conference on AI, Ethic and Society (AIES'19), 2019
1242019
Multi-stakeholder recommendation and its connection to multi-sided fairness
H Abdollahpouri, R Burke
arXiv preprint arXiv:1907.13158, 2019
1122019
Recommender systems as multistakeholder environments
H Abdollahpouri, R Burke, B Mobasher
Proceedings of the 25th Conference on User Modeling, Adaptation and …, 2017
992017
Towards multi-stakeholder utility evaluation of recommender systems.
RD Burke, H Abdollahpouri, B Mobasher, T Gupta
UMAP (Extended Proceedings) 750, 2016
992016
Multi-sided Exposure Bias in Recommendation
H Abdollahpouri, M Mansoury
ACM KDD Workshop on Industrial Recommendation, 2020
982020
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
762020
Beyond personalization: Research directions in multistakeholder recommendation
H Abdollahpouri, G Adomavicius, R Burke, I Guy, D Jannach, ...
arXiv preprint arXiv:1905.01986, 2019
712019
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
532021
Multistakeholder recommender systems
H Abdollahpouri, R Burke
Recommender systems handbook, 647-677, 2021
422021
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
392020
Popularity Bias in Recommendation: A Multi-stakeholder Perspective
H Abdollahpouri
PhD Dissertation, University of Colorado Boulder, 2020
382020
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
382019
Toward the Next Generation of News Recommender Systems
H Abdollahpouri, E Malthouse, J Konstan, B Mobasher, J Gilbert
Workshop on News Recommendation and Intelligence at WWW’21, 2021
352021
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