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Michael D. Ekstrand
Michael D. Ekstrand
Associate Professor of Computer Science, Boise State University
Verified email at boisestate.edu - Homepage
Title
Cited by
Cited by
Year
Collaborative filtering recommender systems
MD Ekstrand, JT Riedl, JA Konstan
Foundations and Trends in Human-Computer Interaction 4 (2), 81-173, 2011
15182011
User perception of differences in recommender algorithms
MD Ekstrand, FM Harper, MC Willemsen, JA Konstan
Proceedings of the 8th ACM Conference on Recommender systems, 161-168, 2014
2332014
Rethinking the recommender research ecosystem: reproducibility, openness, and LensKit
MD Ekstrand, M Ludwig, JA Konstan, JT Riedl
Proceedings of the fifth ACM conference on Recommender systems, 133-140, 2011
2212011
All The Cool Kids, How Do They Fit In?: Popularity and Demographic Biases in Recommender Evaluation and Effectiveness
MD Ekstrand, M Tian, IM Azpiazu, JD Ekstrand, O Anuyah, D McNeill, ...
Conference on Fairness, Accountability and Transparency, 172-186, 2018
1852018
Exploring author gender in book rating and recommendation
MD Ekstrand, M Tian, MRI Kazi, H Mehrpouyan, D Kluver
Proceedings of the 12th ACM conference on recommender systems, 242-250, 2018
1312018
Rating-based collaborative filtering: algorithms and evaluation
D Kluver, MD Ekstrand, JA Konstan
Social information access: Systems and technologies, 344-390, 2018
1192018
Automatically building research reading lists
MD Ekstrand, P Kannan, JA Stemper, JT Butler, JA Konstan, JT Riedl
Proceedings of the fourth ACM conference on Recommender systems, 159-166, 2010
1172010
Evaluating stochastic rankings with expected exposure
F Diaz, B Mitra, MD Ekstrand, AJ Biega, B Carterette
Proceedings of the 29th ACM International Conference on Informationá…, 2020
1102020
Letting users choose recommender algorithms: An experimental study
MD Ekstrand, D Kluver, FM Harper, JA Konstan
Proceedings of the 9th ACM Conference on Recommender Systems, 11-18, 2015
1082015
Teaching recommender systems at large scale: evaluation and lessons learned from a hybrid MOOC
JA Konstan, JD Walker, DC Brooks, K Brown, MD Ekstrand
ACM Transactions on Computer-Human Interaction (TOCHI) 22 (2), 1-23, 2015
1042015
Behaviorism is not enough: Better recommendations through listening to users
MD Ekstrand, MC Willemsen
Proceedings of the 10th ACM Conference on Recommender Systems, 221-224, 2016
932016
When recommenders fail: predicting recommender failure for algorithm selection and combination
M Ekstrand, J Riedl
Proceedings of the sixth ACM conference on Recommender systems, 233-236, 2012
752012
Privacy for All: Ensuring Fair and Equitable Privacy Protections
MD Ekstrand, R Joshaghani, H Mehrpouyan
Conference on Fairness, Accountability and Transparency, 35-47, 2018
742018
Fairness in Information Access Systems
MD Ekstrand, A Das, R Burke, F Diaz
Foundations and Trends« in Information Retrieval 16 (1-2), 1-177, 2022
70*2022
Rating support interfaces to improve user experience and recommender accuracy
TT Nguyen, D Kluver, TY Wang, PM Hui, MD Ekstrand, MC Willemsen, ...
Proceedings of the 7th ACM conference on Recommender systems, 149-156, 2013
542013
LensKit for Python: Next-Generation Software for Recommender Systems Experiments
MD Ekstrand
Proceedings of the 29th ACM International Conference on Informationá…, 2020
53*2020
Searching for software learning resources using application context
M Ekstrand, W Li, T Grossman, J Matejka, G Fitzmaurice
Proceedings of the 24th annual ACM symposium on User interface software andá…, 2011
532011
LensKit: a modular recommender framework
MD Ekstrand, M Ludwig, J Kolb, JT Riedl
Proceedings of the fifth ACM conference on Recommender systems, 349-350, 2011
432011
How many bits per rating?
D Kluver, TT Nguyen, M Ekstrand, S Sen, J Riedl
Proceedings of the sixth ACM conference on Recommender systems, 99-106, 2012
412012
Fairness and Discrimination in Retrieval and Recommendation
MD Ekstrand, R Burke, F Diaz
Proceedings of the 42nd International ACM SIGIR Conference on Research andá…, 2019
402019
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