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Collaborative Filtering Recommender Systems
- Michael D. Ekstrand, J. Riedl, J. Konstan
- Computer ScienceFound. Trends Hum. Comput. Interact.
- 1 February 2011
TLDR
Rethinking the recommender research ecosystem: reproducibility, openness, and LensKit
- Michael D. Ekstrand, Michael Ludwig, J. Konstan, J. Riedl
- Computer ScienceRecSys '11
- 23 October 2011
TLDR
Evaluating Stochastic Rankings with Expected Exposure
- Fernando Diaz, Bhaskar Mitra, Michael D. Ekstrand, Asia J. Biega, Ben Carterette
- Computer ScienceCIKM
- 27 April 2020
TLDR
Automatically building research reading lists
- Michael D. Ekstrand, P. Kannan, J. Stemper, John T. Butler, J. Konstan, J. Riedl
- Computer ScienceRecSys '10
- 26 September 2010
TLDR
User perception of differences in recommender algorithms
- Michael D. Ekstrand, F. M. Harper, M. Willemsen, J. Konstan
- Computer ScienceRecSys '14
- 6 October 2014
TLDR
Rating-Based Collaborative Filtering: Algorithms and Evaluation
- Daniel Kluver, Michael D. Ekstrand, J. Konstan
- Computer ScienceSocial Information Access
- 2018
TLDR
Behaviorism is Not Enough: Better Recommendations through Listening to Users
- Michael D. Ekstrand, M. Willemsen
- Computer ScienceRecSys
- 7 September 2016
TLDR
All The Cool Kids, How Do They Fit In?: Popularity and Demographic Biases in Recommender Evaluation and Effectiveness
- Michael D. Ekstrand, Mucun Tian, M. S. Pera
- Computer ScienceFAT
- 21 January 2018
TLDR
When recommenders fail: predicting recommender failure for algorithm selection and combination
- Michael D. Ekstrand, J. Riedl
- Computer ScienceRecSys '12
- 9 September 2012
TLDR
Exploring author gender in book rating and recommendation
- Michael D. Ekstrand, Mucun Tian, Mohammed R. Imran Kazi, Hoda Mehrpouyan, Daniel Kluver
- Computer ScienceRecSys
- 22 August 2018
TLDR
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