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
Dietmar JannachUniversity of Klagenfurt, Austria; University of Bergen, NorwayVerified email at aau.at
Edward C MalthouseNorthwestern University: integrated marketing communications, industrial engineering and mgmt sciVerified email at northwestern.edu
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, Department of Computer ScienceVerified email at rutgers.edu
Arman DehpanahPhD Candidate, DePaul University, Chicago, USAVerified email at depaul.edu
Mehdi ElahiAssociate Professor, University of BergenVerified email at uib.no
Joseph A. KonstanUniversity of MinnesotaVerified email at umn.edu
Ben CarteretteUniversity of DelawareVerified email at cis.udel.edu
Jesse AndertonNortheastern UniversityVerified email at ccs.neu.edu
Babak LoniMachine Learning Engineer, INGVerified email at ing.com
Shaghayegh SahebiAssistant Professor, University at Albany, SUNYVerified email at albany.edu

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	151	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	92	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	72	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	60	2019
Recommender systems as multistakeholder environments H Abdollahpouri, R Burke, B Mobasher Proceedings of the 25th Conference on User Modeling, Adaptation and …, 2017	54	2017
Towards Multi-Stakeholder Utility Evaluation of Recommender Systems. RD Burke, H Abdollahpouri, B Mobasher, T Gupta UMAP (Extended Proceedings) 750, 2016	53	2016
Beyond personalization: Research directions in multistakeholder recommendation H Abdollahpouri, G Adomavicius, R Burke, I Guy, D Jannach, ... arXiv preprint arXiv:1905.01986, 2019	38	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	33	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	31	2020
Multi-sided Exposure Bias in Recommendation H Abdollahpouri, M Mansoury ACM KDD Workshop on Industrial Recommendation, 2020	19	2020
Popularity Bias in Ranking and Recommendation H Abdollahpouri Conference on AI, Ethic and Society (AIES'19), 2019	19	2019
Educational recommendation with multiple stakeholders R Burke, H Abdollahpouri 2016 IEEE/WIC/ACM International Conference on Web Intelligence Workshops …, 2016	19	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	17	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	15	2020
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
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	12	2019
Patterns of Multistakeholder Recommendation R Burke, H Abdollahpouri VAMS@RecSys'17 workshop on value-aware and multistakeholder recommendation, 2017	12	2017
Multiple stakeholders in music recommender systems H Abdollahpouri, S Essinger VAMS@RecSys'17 workshop on value-aware and multistakeholder recommendation, 2017	10	2017
Vams 2017: Workshop on value-aware and multistakeholder recommendation R Burke, G Adomavicius, I Guy, J Krasnodebski, L Pizzato, Y Zhang, ... Proceedings of the Eleventh ACM Conference on Recommender Systems, 378-379, 2017	8	2017
An approach for personalization of banking services in multi-channel environment using memory-based collaborative filtering H Abdollahpouri, A Abdollahpouri The 5th Conference on Information and Knowledge Technology, 208-213, 2013	8	2013

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