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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
14482011
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
2162011
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
2122014
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
1582018
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
1162018
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
1152010
Rating-based collaborative filtering: algorithms and evaluation
D Kluver, MD Ekstrand, JA Konstan
Social Information Access, 344-390, 2018
1122018
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
1062015
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
1042015
Evaluating stochastic rankings with expected exposure
F Diaz, B Mitra, MD Ekstrand, AJ Biega, B Carterette
Proceedings of the 29th ACM International Conference on Information …, 2020
892020
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
842016
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
742012
Privacy for All: Ensuring Fair and Equitable Privacy Protections
MD Ekstrand, R Joshaghani, H Mehrpouyan
Conference on Fairness, Accountability and Transparency, 35-47, 2018
682018
LensKit for Python: Next-Generation Software for Recommender Systems Experiments
MD Ekstrand
Proceedings of the 29th ACM International Conference on Information …, 2020
62*2020
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
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 and …, 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
rv you're dumb: identifying discarded work in Wiki article history
MD Ekstrand, JT Riedl
Proceedings of the 5th international Symposium on Wikis and Open …, 2009
342009
Fairness and discrimination in recommendation and retrieval
MD Ekstrand, R Burke, F Diaz
Proceedings of the 13th ACM Conference on Recommender Systems, 576-577, 2019
332019
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Articles 1–20