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 MansouryUniversity of AmsterdamVerified email at uva.nl
Mykola PechenizkiyEindhoven University of TechnologyVerified email at tue.nl
Jan KrasnodebskiSenior Director Data ScienceVerified email at expediagroup.com
Ido GuyResearch Scientist Manager, FacebookVerified email at fb.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
Edward C MalthouseNorthwestern University: integrated marketing communications, industrial engineering and mgmt sciVerified email at northwestern.edu
Dietmar JannachUniversity of Klagenfurt, Austria; University of Bergen, NorwayVerified email at aau.at
Toshihiro KamishimaNational Institute of Advanced Industrial Science and Technology (AIST)Verified email at kamishima.net
Alireza AbdollahpouriDept. of Computer, Faculty of Engineering, University of Kurdistan, Sanandaj, IranVerified email at uok.ac.ir
Yongfeng ZhangRutgers University, Computer ScienceVerified email at rutgers.edu
Mehdi ElahiAssociate Professor, University of BergenVerified email at uib.no
Arman DehpanahPhD Candidate, DePaul University, Chicago, USAVerified email at depaul.edu
Joseph A. KonstanUniversity of MinnesotaVerified email at umn.edu
Helma TorkamaanUniversity of Duisburg-EssenVerified email at acm.org
Zahra NazariSenior Research Scientist, SpotifyVerified email at spotify.com
Ben CarteretteUniversity of DelawareVerified email at cis.udel.edu
Jesse AndertonSpotifyVerified email at spotify.com

Himan Abdollahpouri

Research Scientist at Spotify

Verified email at spotify.com

Recommender Systems Machine Learning User Modeling Personalization


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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	202	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	127	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	102	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	93	2019
Recommender systems as multistakeholder environments H Abdollahpouri, R Burke, B Mobasher Proceedings of the 25th Conference on User Modeling, Adaptation and …, 2017	61	2017
Towards multi-stakeholder utility evaluation of recommender systems. RD Burke, H Abdollahpouri, B Mobasher, T Gupta UMAP (Extended Proceedings) 750, 2016	60	2016
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	54	2020
Beyond personalization: Research directions in multistakeholder recommendation H Abdollahpouri, G Adomavicius, R Burke, I Guy, D Jannach, ... arXiv preprint arXiv:1905.01986, 2019	45	2019
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	41	2019
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	33	2020
Popularity Bias in Ranking and Recommendation H Abdollahpouri Conference on AI, Ethic and Society (AIES'19), 2019	31	2019
Multi-sided Exposure Bias in Recommendation H Abdollahpouri, M Mansoury ACM KDD Workshop on Industrial Recommendation, 2020	29	2020
Educational recommendation with multiple stakeholders R Burke, H Abdollahpouri 2016 IEEE/WIC/ACM International Conference on Web Intelligence Workshops …, 2016	21	2016
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	20	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	16	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	14	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	13	2020
Patterns of Multistakeholder Recommendation R Burke, H Abdollahpouri VAMS@RecSys'17 workshop on value-aware and multistakeholder recommendation, 2017	13	2017
Multiple stakeholders in music recommender systems H Abdollahpouri, S Essinger VAMS@RecSys'17 workshop on value-aware and multistakeholder recommendation, 2017	12	2017
Popularity Bias in Recommendation: A Multi-stakeholder Perspective H Abdollahpouri PhD Dissertation, University of Colorado Boulder, 2020	10	2020

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