Michael D. Ekstrand
Michael D. Ekstrand
Assistant Professor of Computer Science, Boise State University
Bestätigte E-Mail-Adresse bei boisestate.edu - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Collaborative filtering recommender systems
MD Ekstrand, JT Riedl, JA Konstan
Foundations and Trends in Human-Computer Interaction 4 (2), 81-173, 2011
11332011
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
1882011
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
1372014
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
1052010
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
832015
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
822015
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
582012
Rating-based collaborative filtering: algorithms and evaluation
D Kluver, MD Ekstrand, JA Konstan
Social Information Access, 344-390, 2018
552018
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
482011
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
462018
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
422013
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
412016
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
382018
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
382011
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
372012
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
352009
Privacy for All: Ensuring Fair and Equitable Privacy Protections
MD Ekstrand, R Joshaghani, H Mehrpouyan
Conference on Fairness, Accountability and Transparency, 35-47, 2018
242018
The LKPY Package for Recommender Systems Experiments: Next-Generation Tools and Lessons Learned from the LensKit Project
MD Ekstrand
arXiv preprint arXiv:1809.03125, 2018
152018
The LKPY package for recommender systems experiments: Next-generation tools and lessons learned from the LensKit project
MD Ekstrand
arXiv preprint arXiv:1809.03125, 2018
152018
RecStore: an extensible and adaptive framework for online recommender queries inside the database engine
JJ Levandoski, M Sarwat, MF Mokbel, MD Ekstrand
Proceedings of the 15th International Conference on Extending Database …, 2012
152012
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