Himan Abdollahpouri

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Robin BurkeUniversity of Colorado, BoulderVerified email at colorado.edu
Bamshad MobasherSchool of Computing, DePaul UniversityVerified email at cs.depaul.edu
Masoud MansouryAssistant Professor, Delft University of TechnologyVerified email at tudelft.nl
Mykola PechenizkiyEindhoven University of TechnologyVerified email at tue.nl
Edward C MalthouseNorthwestern University: integrated marketing communications, industrial engineering and mgmt sciVerified email at northwestern.edu
Jan KrasnodebskiSenior Director Data ScienceVerified email at expediagroup.com
Ido GuyResearch Scientist ManagerVerified email at meta.com
Gediminas AdomaviciusProfessor of Information and Decision Sciences, University of MinnesotaVerified email at umn.edu
Luiz Augusto PizzatoCommonwealth Bank of AustraliaVerified email at acm.org
Dietmar JannachUniversity of Klagenfurt, Austria; University of Bergen, NorwayVerified email at aau.at
Mehdi ElahiAssociate Professor, University of BergenVerified email at uib.no
Toshihiro KamishimaIndependent ResearcherVerified email at kamishima.net
Steven D. EssingerDrexel UniversityVerified email at drexel.edu
Mounia LalmasSpotifyVerified email at acm.org
Babak LoniMachine Learning Engineer, MetaVerified email at meta.com
Khanh-Phuong (KP) Thai, PhDAppleVerified email at apple.com
Yongfeng ZhangProfessor of Computer Science, Rutgers UniversityVerified email at rutgers.edu
Arman DehpanahSenior ML Research Engineer @ ActivisionVerified email at depaul.edu
Joseph A. KonstanUniversity of MinnesotaVerified email at umn.edu
Maria DimakopoulouDirector of Machine Learning & Head of Homepage Personalization, SpotifyVerified email at spotify.com

Himan Abdollahpouri

Research Scientist at Spotify

Verified email at spotify.com - Homepage

Recommender Systems Personalization Machine Learning


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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	450	2017
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	346	2019
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	289	2020
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	275	2019
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	264	2020
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	139	2020
Popularity Bias in Ranking and Recommendation H Abdollahpouri Conference on AI, Ethic and Society (AIES'19), 2019	116	2019
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	107	2021
Multi-stakeholder recommendation and its connection to multi-sided fairness H Abdollahpouri, R Burke arXiv preprint arXiv:1907.13158, 2019	103	2019
Towards multi-stakeholder utility evaluation of recommender systems. RD Burke, H Abdollahpouri, B Mobasher, T Gupta UMAP (Extended Proceedings) 750, 2016	95	2016
Recommender systems as multistakeholder environments H Abdollahpouri, R Burke, B Mobasher Proceedings of the 25th Conference on User Modeling, Adaptation and …, 2017	92	2017
Multi-sided Exposure Bias in Recommendation H Abdollahpouri, M Mansoury ACM KDD Workshop on Industrial Recommendation, 2020	88	2020
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	69	2020
Beyond personalization: Research directions in multistakeholder recommendation H Abdollahpouri, G Adomavicius, R Burke, I Guy, D Jannach, ... arXiv preprint arXiv:1905.01986, 2019	67	2019
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	51	2021
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	37	2020
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	35	2019
Multistakeholder recommender systems H Abdollahpouri, R Burke Recommender systems handbook, 647-677, 2021	33	2021
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	33	2021
Popularity Bias in Recommendation: A Multi-stakeholder Perspective H Abdollahpouri PhD Dissertation, University of Colorado Boulder, 2020	32	2020

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