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Michael D. Ekstrand

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affiliation: Drexel University, Philadelphia, PA, USA
affiliation (former): Boise State University, ID, USA
affiliation (former): Texas State University San Marcos, TX, USA
affiliation (PhD 2014): University of Minnesota Twin Cities, Minneapolis, MN, USA

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2020 – today

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2024
[j15]
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Michael D. Ekstrand, Ben Carterette, Fernando Diaz:
Distributionally-Informed Recommender System Evaluation. Trans. Recomm. Syst. 2(1): 6:1-6:27 (2024)
[j14]
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Jonathan Stray, Alon Y. Halevy, Parisa Assar, Dylan Hadfield-Menell, Craig Boutilier, Amar Ashar, Chloé Bakalar, Lex Beattie, Michael D. Ekstrand, Claire Leibowicz, Connie Moon Sehat, Sara Johansen, Lianne Kerlin, David Vickrey, Spandana Singh, Sanne Vrijenhoek, Amy Xian Zhang, McKane Andrus, Natali Helberger, Polina Proutskova, Tanushree Mitra, Nina Vasan:
Building Human Values into Recommender Systems: An Interdisciplinary Synthesis. Trans. Recomm. Syst. 2(3): 20:1-20:57 (2024)
[c52]
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Amifa Raj, Michael D. Ekstrand:
Towards Optimizing Ranking in Grid-Layout for Provider-Side Fairness. ECIR (5) 2024: 90-105
[c51]
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Michael D. Ekstrand, Lex Beattie, Maria Soledad Pera, Henriette Cramer:
Not Just Algorithms: Strategically Addressing Consumer Impacts in Information Retrieval. ECIR (4) 2024: 314-335
[c50]
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Ngozi Ihemelandu, Michael D. Ekstrand:
Multiple Testing for IR and Recommendation System Experiments. ECIR (3) 2024: 449-457
[i29]
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  - journals/corr/abs-2406-03198
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Jacy Reese Anthis, Kristian Lum, Michael D. Ekstrand, Avi Feller, Alexander D'Amour, Chenhao Tan:
The Impossibility of Fair LLMs. CoRR abs/2406.03198 (2024)
2023
[j13]
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Michael D. Ekstrand, Maria Soledad Pera, Katherine Landau Wright:
Seeking Information with a More Knowledgeable Other. Interactions 30(1): 70-73 (2023)
[c49]
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Christine Pinney, Amifa Raj, Alex Hanna, Michael D. Ekstrand:
Much Ado About Gender: Current Practices and Future Recommendations for Appropriate Gender-Aware Information Access. CHIIR 2023: 269-279
[c48]
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Tobias Vente, Michael D. Ekstrand, Joeran Beel:
Introducing LensKit-Auto, an Experimental Automated Recommender System (AutoRecSys) Toolkit. RecSys 2023: 1212-1216
[c47]
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Michael D. Ekstrand, Jean Garcia-Gathright, Nasim Sonboli, Amifa Raj, Karlijn Dinnissen:
FAccTRec 2023: The 6th Workshop on Responsible Recommendation. RecSys 2023: 1267-1268
[c46]
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  - conf/sigir/IhemelanduE23
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Ngozi Ihemelandu, Michael D. Ekstrand:
Inference at Scale: Significance Testing for Large Search and Recommendation Experiments. SIGIR 2023: 2087-2091
[c45]
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Amifa Raj, Bhaskar Mitra, Nick Craswell, Michael D. Ekstrand:
Patterns of Gender-Specializing Query Reformulation. SIGIR 2023: 2241-2245
[c44]
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  - conf/webi/IhemelanduE23
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Ngozi Ihemelandu, Michael D. Ekstrand:
Candidate Set Sampling for Evaluating Top-N Recommendation. WI/IAT 2023: 88-94
[i28]
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  - journals/corr/abs-2301-04780
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Christine Pinney, Amifa Raj, Alex Hanna, Michael D. Ekstrand:
Much Ado About Gender: Current Practices and Future Recommendations for Appropriate Gender-Aware Information Access. CoRR abs/2301.04780 (2023)
[i27]
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  - journals/corr/abs-2302-05558
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Michael D. Ekstrand, Graham McDonald, Amifa Raj, Isaac Johnson:
Overview of the TREC 2022 Fair Ranking Track. CoRR abs/2302.05558 (2023)
[i26]
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  - journals/corr/abs-2302-10856
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Michael D. Ekstrand, Graham McDonald, Amifa Raj, Isaac L. Johnson:
Overview of the TREC 2021 Fair Ranking Track. CoRR abs/2302.10856 (2023)
[i25]
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  - journals/corr/abs-2304-13129
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Amifa Raj, Bhaskar Mitra, Nick Craswell, Michael D. Ekstrand:
Patterns of gender-specializing query reformulation. CoRR abs/2304.13129 (2023)
[i24]
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  - journals/corr/abs-2305-02461
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Ngozi Ihemelandu, Michael D. Ekstrand:
Inference at Scale Significance Testing for Large Search and Recommendation Experiments. CoRR abs/2305.02461 (2023)
[i23]
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  - journals/corr/abs-2309-05892
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Michael D. Ekstrand, Ben Carterette, Fernando Diaz:
Distributionally-Informed Recommender System Evaluation. CoRR abs/2309.05892 (2023)
[i22]
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  - journals/corr/abs-2309-10271
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Amifa Raj, Michael D. Ekstrand:
Towards Measuring Fairness in Grid Layout in Recommender Systems. CoRR abs/2309.10271 (2023)
[i21]
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  - journals/corr/abs-2309-11723
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Ngozi Ihemelandu, Michael D. Ekstrand:
Candidate Set Sampling for Evaluating Top-N Recommendation. CoRR abs/2309.11723 (2023)
[i20]
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  - journals/corr/abs-2310-12524
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Amifa Raj, Michael D. Ekstrand:
Unified Browsing Models for Linear and Grid Layouts. CoRR abs/2310.12524 (2023)
[i19]
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  - journals/corr/abs-2311-11776
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Alexandra Olteanu, Michael D. Ekstrand, Carlos Castillo, Jina Suh:
Responsible AI Research Needs Impact Statements Too. CoRR abs/2311.11776 (2023)
2022
[j12]
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  - journals/aim/SonboliBEM22
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Nasim Sonboli, Robin Burke, Michael D. Ekstrand, Rishabh Mehrotra:
The Multisided Complexity of Fairness in Recommender Systems. AI Mag. 43(2): 164-176 (2022)
[j11]
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  - journals/ftir/Ekstrand0B022
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Michael D. Ekstrand, Anubrata Das, Robin Burke, Fernando Diaz:
Fairness in Information Access Systems. Found. Trends Inf. Retr. 16(1-2): 1-177 (2022)
[c43]
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  - conf/sigir/RajE22
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Amifa Raj, Michael D. Ekstrand:
Measuring Fairness in Ranked Results: An Analytical and Empirical Comparison. SIGIR 2022: 726-736
[c42]
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Michael D. Ekstrand, Graham McDonald, Amifa Raj, Isaac Johnson:
Overview of the TREC 2022 Fair Ranking Track. TREC 2022
[e2]
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Jennifer Golbeck, F. Maxwell Harper, Vanessa Murdock, Michael D. Ekstrand, Bracha Shapira, Justin Basilico, Keld T. Lundgaard, Even Oldridge:
RecSys '22: Sixteenth ACM Conference on Recommender Systems, Seattle, WA, USA, September 18 - 23, 2022. ACM 2022, ISBN 978-1-4503-9278-5 [contents]
[r1]
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Michael D. Ekstrand, Anubrata Das, Robin Burke, Fernando Diaz:
Fairness in Recommender Systems. Recommender Systems Handbook 2022: 679-707
[i18]
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  - journals/corr/abs-2206-13747
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Amifa Raj, Michael D. Ekstrand:
Fire Dragon and Unicorn Princess; Gender Stereotypes and Children's Products in Search Engine Responses. CoRR abs/2206.13747 (2022)
[i17]
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  - journals/corr/abs-2207-10192
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Jonathan Stray, Alon Y. Halevy, Parisa Assar, Dylan Hadfield-Menell, Craig Boutilier, Amar Ashar, Lex Beattie, Michael D. Ekstrand, Claire Leibowicz, Connie Moon Sehat, Sara Johansen, Lianne Kerlin, David Vickrey, Spandana Singh, Sanne Vrijenhoek, Amy X. Zhang, McKane Andrus, Natali Helberger, Polina Proutskova, Tanushree Mitra, Nina Vasan:
Building Human Values into Recommender Systems: An Interdisciplinary Synthesis. CoRR abs/2207.10192 (2022)
[i16]
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  - journals/corr/abs-2209-02662
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Michael D. Ekstrand, Maria Soledad Pera:
Matching Consumer Fairness Objectives & Strategies for RecSys. CoRR abs/2209.02662 (2022)
2021
[j10]
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  - journals/umuai/BurkeETV21
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Robin Burke, Michael D. Ekstrand, Nava Tintarev, Julita Vassileva:
Preface to the special issue on fair, accountable, and transparent recommender systems. User Model. User Adapt. Interact. 31(3): 371-375 (2021)
[j9]
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Michael D. Ekstrand, Daniel Kluver:
Exploring author gender in book rating and recommendation. User Model. User Adapt. Interact. 31(3): 377-420 (2021)
[c41]
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  - conf/recsys/SpearMARGEP21
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Lawrence Spear, Ashlee Milton, Garrett Allen, Amifa Raj, Michael Green, Michael D. Ekstrand, Maria Soledad Pera:
Baby Shark to Barracuda: Analyzing Children's Music Listening Behavior. RecSys 2021: 639-644
[c40]
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Michael D. Ekstrand, Pierre-Nicolas Schwab, Toshihiro Kamishima, Nasim Sonboli:
FAccTRec 2021: The 4th Workshop on Responsible Recommendation. RecSys 2021: 778-779
[c39]
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  - conf/recsys/EkstrandCCBRS21
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Michael D. Ekstrand, Allison Chaney, Pablo Castells, Robin Burke, David Rohde, Manel Slokom:
SimuRec: Workshop on Synthetic Data and Simulation Methods for Recommender Systems Research. RecSys 2021: 803-805
[c38]
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  - conf/recsys/IhemelanduE21
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Ngozi Ihemelandu, Michael D. Ekstrand:
Statistical Inference: The Missing Piece of RecSys Experiment Reliability Discourse. Perspectives@RecSys 2021
[c37]
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  - conf/trec/EkstrandRM021
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Michael D. Ekstrand, Amifa Raj, Graham McDonald, Isaac Johnson:
Overview of the TREC 2021 Fair Ranking Track. TREC 2021
[c36]
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  - conf/um/MehdyEKM21
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A. K. M. Nuhil Mehdy, Michael D. Ekstrand, Bart P. Knijnenburg, Hoda Mehrpouyan:
Privacy as a Planned Behavior: Effects of Situational Factors on Privacy Perceptions and Plans. UMAP 2021: 169-178
[c35]
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  - conf/www/Kirnap0BECY21
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Ömer Kirnap, Fernando Diaz, Asia Biega, Michael D. Ekstrand, Ben Carterette, Emine Yilmaz:
Estimation of Fair Ranking Metrics with Incomplete Judgments. WWW 2021: 1065-1075
[i15]
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  - journals/corr/abs-2104-11847
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A. K. M. Nuhil Mehdy, Michael D. Ekstrand, Bart P. Knijnenburg, Hoda Mehrpouyan:
Privacy as a Planned Behavior: Effects of Situational Factors on Privacy Perceptions and Plans. CoRR abs/2104.11847 (2021)
[i14]
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  - journals/corr/abs-2105-05779
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Michael D. Ekstrand, Anubrata Das, Robin Burke, Fernando Diaz:
Fairness and Discrimination in Information Access Systems. CoRR abs/2105.05779 (2021)
[i13]
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  - journals/corr/abs-2105-09296
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Amifa Raj, Ashlee Milton, Michael D. Ekstrand:
Pink for Princesses, Blue for Superheroes: The Need to Examine Gender Stereotypes in Kid's Products in Search and Recommendations. CoRR abs/2105.09296 (2021)
[i12]
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  - journals/corr/abs-2108-05135
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Asia J. Biega, Fernando Diaz, Michael D. Ekstrand, Sergey Feldman, Sebastian Kohlmeier:
Overview of the TREC 2020 Fair Ranking Track. CoRR abs/2108.05135 (2021)
[i11]
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  - journals/corr/abs-2108-05152
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Ömer Kirnap, Fernando Diaz, Asia Biega, Michael D. Ekstrand, Ben Carterette, Emine Yilmaz:
Estimation of Fair Ranking Metrics with Incomplete Judgments. CoRR abs/2108.05152 (2021)
[i10]
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  - journals/corr/abs-2109-06424
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Ngozi Ihemelandu, Michael D. Ekstrand:
Statistical Inference: The Missing Piece of RecSys Experiment Reliability Discourse. CoRR abs/2109.06424 (2021)
[i9]
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  - journals/corr/abs-2110-00811
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Michael D. Ekstrand:
Multiversal Simulacra: Understanding Hypotheticals and Possible Worlds Through Simulation. CoRR abs/2110.00811 (2021)
2020
[j8]
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Michael D. Ekstrand, Katherine Landau Wright, Maria Soledad Pera:
Enhancing classroom instruction with online news. Aslib J. Inf. Manag. 72(5): 725-744 (2020)
[c34]
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  - conf/chiir/TianE20
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Mucun Tian, Michael D. Ekstrand:
Estimating Error and Bias in Offline Evaluation Results. CHIIR 2020: 392-396
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  - conf/cikm/0001MEBC20
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Fernando Diaz, Bhaskar Mitra, Michael D. Ekstrand, Asia J. Biega, Ben Carterette:
Evaluating Stochastic Rankings with Expected Exposure. CIKM 2020: 275-284
[c32]
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Michael D. Ekstrand:
LensKit for Python: Next-Generation Software for Recommender Systems Experiments. CIKM 2020: 2999-3006
[c31]
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  - conf/recsys/EkstrandSGKS20
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Michael D. Ekstrand, Pierre-Nicolas Schwab, Jean Garcia-Gathright, Toshihiro Kamishima, Nasim Sonboli:
3rd FAccTRec Workshop: Responsible Recommendation. RecSys 2020: 607-608
[c30]
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  - conf/um/MobasherKBEOT20
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Bamshad Mobasher, Styliani Kleanthous, Bettina Berendt, Michael D. Ekstrand, Jahna Otterbacher, Avital Shulner-Tal:
UMAP 2020 Fairness in User Modeling, Adaptation and Personalization (FairUMAP 2020) Chairs' Welcome. UMAP (Adjunct Publication) 2020: 241-243
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Bamshad Mobasher, Styliani Kleanthous, Michael D. Ekstrand, Bettina Berendt, Jahna Otterbacher, Avital Shulner-Tal:
FairUMAP 2020: The 3rd Workshop on Fairness in User Modeling, Adaptation and Personalization. UMAP 2020: 404-405
[i8]
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Mucun Tian, Michael D. Ekstrand:
Estimating Error and Bias in Offline Evaluation Results. CoRR abs/2001.09455 (2020)
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Asia J. Biega, Fernando Diaz, Michael D. Ekstrand, Sebastian Kohlmeier:
Overview of the TREC 2019 Fair Ranking Track. CoRR abs/2003.11650 (2020)
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Fernando Diaz, Bhaskar Mitra, Michael D. Ekstrand, Asia J. Biega, Ben Carterette:
Evaluating Stochastic Rankings with Expected Exposure. CoRR abs/2004.13157 (2020)
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Amifa Raj, Connor Wood, Ananda Montoly, Michael D. Ekstrand:
Comparing Fair Ranking Metrics. CoRR abs/2009.01311 (2020)

2010 – 2019

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2019
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Alexandra Olteanu, Jean Garcia-Gathright, Maarten de Rijke, Michael D. Ekstrand, Adam Roegiest, Aldo Lipani, Alex Beutel, Ana Lucic, Ana-Andreea Stoica, Anubrata Das, Asia Biega, Bart Voorn, Claudia Hauff, Damiano Spina, David D. Lewis, Douglas W. Oard, Emine Yilmaz, Faegheh Hasibi, Gabriella Kazai, Graham McDonald, Hinda Haned, Iadh Ounis, Ilse van der Linden, Joris Baan, Kamuela N. Lau, Krisztian Balog, Mahmoud F. Sayed, Maria Panteli, Mark Sanderson, Matthew Lease, Preethi Lahoti, Toshihiro Kamishima:
FACTS-IR: fairness, accountability, confidentiality, transparency, and safety in information retrieval. SIGIR Forum 53(2): 20-43 (2019)
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Ashlee Milton, Michael Green, Adam Keener, Joshua Ames, Michael D. Ekstrand, Maria Soledad Pera:
StoryTime: eliciting preferences from children for book recommendations. RecSys 2019: 544-545
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Michael D. Ekstrand, Robin Burke, Fernando Diaz:
Fairness and discrimination in recommendation and retrieval. RecSys 2019: 576-577
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Michael D. Ekstrand, Robin Burke, Fernando Diaz:
Fairness and Discrimination in Retrieval and Recommendation. SIGIR 2019: 1403-1404
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Alexandra Olteanu, Jean Garcia-Gathright, Maarten de Rijke, Michael D. Ekstrand:
Workshop on Fairness, Accountability, Confidentiality, Transparency, and Safety in Information Retrieval (FACTS-IR). SIGIR 2019: 1423-1425
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Bettina Berendt, Veronika Bogina, Robin Burke, Michael D. Ekstrand, Alan Hartman, Styliani Kleanthous, Tsvi Kuflik, Bamshad Mobasher, Jahna Otterbacher:
FairUMAP 2019 Chairs' Welcome Overview. UMAP (Adjunct Publication) 2019: 279-281
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Michael D. Ekstrand, Joseph A. Konstan:
Recommender Systems Notation: Proposed Common Notation for Teaching and Research. CoRR abs/1902.01348 (2019)
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Alexandra Olteanu, Jean Garcia-Gathright, Maarten de Rijke, Michael D. Ekstrand:
Proceedings of FACTS-IR 2019. CoRR abs/1907.05755 (2019)
2018
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Nicola Ferro, Norbert Fuhr, Gregory Grefenstette, Joseph A. Konstan, Pablo Castells, Elizabeth M. Daly, Thierry Declerck, Michael D. Ekstrand, Werner Geyer, Julio Gonzalo, Tsvi Kuflik, Krister Lindén, Bernardo Magnini, Jian-Yun Nie, Raffaele Perego, Bracha Shapira, Ian Soboroff, Nava Tintarev, Karin Verspoor, Martijn C. Willemsen, Justin Zobel:
From Evaluating to Forecasting Performance: How to Turn Information Retrieval, Natural Language Processing and Recommender Systems into Predictive Sciences (Dagstuhl Perspectives Workshop 17442). Dagstuhl Manifestos 7(1): 96-139 (2018)
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Nicola Ferro, Norbert Fuhr, Gregory Grefenstette, Joseph A. Konstan, Pablo Castells, Elizabeth M. Daly, Thierry Declerck, Michael D. Ekstrand, Werner Geyer, Julio Gonzalo, Tsvi Kuflik, Krister Lindén, Bernardo Magnini, Jian-Yun Nie, Raffaele Perego, Bracha Shapira, Ian Soboroff, Nava Tintarev, Karin Verspoor, Martijn C. Willemsen, Justin Zobel:
The Dagstuhl Perspectives Workshop on Performance Modeling and Prediction. SIGIR Forum 52(1): 91-101 (2018)
[c23]
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Michael D. Ekstrand, Rezvan Joshaghani, Hoda Mehrpouyan:
Privacy for All: Ensuring Fair and Equitable Privacy Protections. FAT 2018: 35-47
[c22]
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Michael D. Ekstrand, Mucun Tian, Ion Madrazo Azpiazu, Jennifer D. Ekstrand, Oghenemaro Anuyah, David McNeill, Maria Soledad Pera:
All The Cool Kids, How Do They Fit In?: Popularity and Demographic Biases in Recommender Evaluation and Effectiveness. FAT 2018: 172-186
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Michael D. Ekstrand, Mucun Tian, Mohammed R. Imran Kazi, Hoda Mehrpouyan, Daniel Kluver:
Exploring author gender in book rating and recommendation. RecSys 2018: 242-250
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Toshihiro Kamishima, Pierre-Nicolas Schwab, Michael D. Ekstrand:
2nd FATREC workshop: responsible recommendation. RecSys 2018: 516
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Bamshad Mobasher, Robin Burke, Michael D. Ekstrand, Bettina Berendt:
UMAP 2018 Fairness in User Modeling, Adaptation and Personalization (FairUMAP 2018) Chairs' Welcome & Organization: Preface. UMAP (Adjunct Publication) 2018: 3-5
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Daniel Kluver, Michael D. Ekstrand, Joseph A. Konstan:
Rating-Based Collaborative Filtering: Algorithms and Evaluation. Social Information Access 2018: 344-390
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Sole Pera, Michael D. Ekstrand, Xavier Amatriain, John O'Donovan:
Proceedings of the 12th ACM Conference on Recommender Systems, RecSys 2018, Vancouver, BC, Canada, October 2-7, 2018. ACM 2018, ISBN 978-1-4503-5901-6 [contents]
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Michael D. Ekstrand, Mucun Tian, Mohammed R. Imran Kazi, Hoda Mehrpouyan, Daniel Kluver:
Exploring Author Gender in Book Rating and Recommendation. CoRR abs/1808.07586 (2018)
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Michael D. Ekstrand:
The LKPY Package for Recommender Systems Experiments: Next-Generation Tools and Lessons Learned from the LensKit Project. CoRR abs/1809.03125 (2018)
2017
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Michael D. Ekstrand, Vaibhav Mahant:
Sturgeon and the Cool Kids: Problems with Random Decoys for Top-N Recommender Evaluation. FLAIRS 2017: 639-644
[c17]
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Sushma Channamsetty, Michael D. Ekstrand:
Recommender Response to Diversity and Popularity Bias in User Profiles. FLAIRS 2017: 657-660
[c16]
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Michael D. Ekstrand, Maria Soledad Pera:
The Demographics of Cool: Popularity and Recommender Performance for Different Groups of Users. RecSys Posters 2017
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Michael D. Ekstrand, Amit Sharma:
FATREC Workshop on Responsible Recommendation. RecSys 2017: 382-383
2016
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Michael D. Ekstrand, Michael Ludwig:
Dependency Injection with Static Analysis and Context-Aware Policy. J. Object Technol. 15(1): 1:1-31 (2016)
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Michael D. Ekstrand, Martijn C. Willemsen:
Behaviorism is Not Enough: Better Recommendations through Listening to Users. RecSys 2016: 221-224
2015
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Joseph A. Konstan, J. D. Walker, D. Christopher Brooks, Keith Brown, Michael D. Ekstrand:
Teaching Recommender Systems at Large Scale: Evaluation and Lessons Learned from a Hybrid MOOC. ACM Trans. Comput. Hum. Interact. 22(2): 10:1-10:23 (2015)
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Michael D. Ekstrand, Daniel Kluver, F. Maxwell Harper, Joseph A. Konstan:
Letting Users Choose Recommender Algorithms: An Experimental Study. RecSys 2015: 11-18
2014
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Joseph A. Konstan, J. D. Walker, D. Christopher Brooks, Keith Brown, Michael D. Ekstrand:
Teaching recommender systems at large scale: evaluation and lessons learned from a hybrid MOOC. L@S 2014: 61-70
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Michael D. Ekstrand, F. Maxwell Harper, Martijn C. Willemsen, Joseph A. Konstan:
User perception of differences in recommender algorithms. RecSys 2014: 161-168
2013
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Tien T. Nguyen, Daniel Kluver, Ting-Yu Wang, Pik-Mai Hui, Michael D. Ekstrand, Martijn C. Willemsen, John Riedl:
Rating support interfaces to improve user experience and recommender accuracy. RecSys 2013: 149-156
2012
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Justin J. Levandoski, Mohamed Sarwat, Mohamed F. Mokbel, Michael D. Ekstrand:
RecStore: an extensible and adaptive framework for online recommender queries inside the database engine. EDBT 2012: 86-96
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Daniel Kluver, Tien T. Nguyen, Michael D. Ekstrand, Shilad Sen, John Riedl:
How many bits per rating? RecSys 2012: 99-106
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Michael D. Ekstrand, John Riedl:
When recommenders fail: predicting recommender failure for algorithm selection and combination. RecSys 2012: 233-236
2011
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Michael D. Ekstrand, John Riedl, Joseph A. Konstan:
Collaborative Filtering Recommender Systems. Found. Trends Hum. Comput. Interact. 4(2): 175-243 (2011)
[j1]
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Justin J. Levandoski, Michael D. Ekstrand, Michael Ludwig, Ahmed Eldawy, Mohamed F. Mokbel, John Riedl:
RecBench: Benchmarks for Evaluating Performance of Recommender System Architectures. Proc. VLDB Endow. 4(11): 911-920 (2011)
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Michael D. Ekstrand, Michael Ludwig, Joseph A. Konstan, John Riedl:
Rethinking the recommender research ecosystem: reproducibility, openness, and LensKit. RecSys 2011: 133-140
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Michael D. Ekstrand, Michael Ludwig, Jack Kolb, John Riedl:
LensKit: a modular recommender framework. RecSys 2011: 349-350
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Martijn C. Willemsen, Dirk G. F. M. Bollen, Michael D. Ekstrand:
UCERSTI 2: second workshop on user-centric evaluation of recommender systems and their interfaces. RecSys 2011: 395-396
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Michael D. Ekstrand, Wei Li, Tovi Grossman, Justin Matejka, George W. Fitzmaurice:
Searching for software learning resources using application context. UIST 2011: 195-204
2010
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Michael D. Ekstrand, Praveen Kannan, James A. Stemper, John T. Butler, Joseph A. Konstan, John Riedl:
Automatically building research reading lists. RecSys 2010: 159-166

2000 – 2009

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2009
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Michael D. Ekstrand, John Riedl:
rv you're dumb: identifying discarded work in Wiki article history. Int. Sym. Wikis 2009

Coauthor Index

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