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
Edward C MalthouseNorthwestern University: integrated marketing communications, industrial engineering and mgmt sciVerified email at northwestern.edu
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
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
Mehdi ElahiAssociate Professor, University of BergenVerified email at uib.no
Khanh-Phuong (KP) Thai, PhDAppleVerified email at apple.com
Yongfeng ZhangRutgers University, Computer ScienceVerified email at rutgers.edu
Alireza AbdollahpouriDept. of Computer, Faculty of Engineering, University of Kurdistan, Sanandaj, IranVerified email at uok.ac.ir
Arman DehpanahSenior ML Research Engineer @ ActivisionVerified email at depaul.edu
Babak LoniMachine Learning Engineer, MetaVerified email at meta.com
Shaghayegh SahebiAssistant Professor, University at Albany, SUNYVerified email at albany.edu
Zahra NazariSenior Research Scientist, SpotifyVerified email at spotify.com
Helma TorkamaanDelft University of TechnologyVerified email at acm.org

Himan Abdollahpouri

Research Scientist at Spotify

Verified email at spotify.com - Homepage

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	288	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	198	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	164	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	154	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	117	2020
Recommender systems as multistakeholder environments H Abdollahpouri, R Burke, B Mobasher Proceedings of the 25th Conference on User Modeling, Adaptation and …, 2017	75	2017
Towards multi-stakeholder utility evaluation of recommender systems. RD Burke, H Abdollahpouri, B Mobasher, T Gupta UMAP (Extended Proceedings) 750, 2016	73	2016
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	67	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	64	2020
Beyond personalization: Research directions in multistakeholder recommendation H Abdollahpouri, G Adomavicius, R Burke, I Guy, D Jannach, ... arXiv preprint arXiv:1905.01986, 2019	58	2019
Popularity Bias in Ranking and Recommendation H Abdollahpouri Conference on AI, Ethic and Society (AIES'19), 2019	56	2019
Multi-sided Exposure Bias in Recommendation H Abdollahpouri, M Mansoury ACM KDD Workshop on Industrial Recommendation, 2020	50	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	43	2020
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	38	2021
Popularity Bias in Recommendation: A Multi-stakeholder Perspective H Abdollahpouri PhD Dissertation, University of Colorado Boulder, 2020	22	2020
Educational recommendation with multiple stakeholders R Burke, H Abdollahpouri 2016 IEEE/WIC/ACM International Conference on Web Intelligence Workshops …, 2016	22	2016
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	21	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	21	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	16	2021
Patterns of Multistakeholder Recommendation R Burke, H Abdollahpouri VAMS@RecSys'17 workshop on value-aware and multistakeholder recommendation, 2017	16	2017

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