Michael D. Ekstrand, Ph.D

Curriculum Vitae

People and Information Research Team (PIReT)
Dept. of Computer Science
Boise State University
1910 University Drive
Boise, ID 83725-2055


https://md.ekstrandom.net
https://piret.info
+1 (208) 426-5761

Education

Ph.D (2014)
Computer Science, University of Minnesota.
Advisers: John T. Riedl and Joseph A. Konstan
B.S. (2007)
Computer Engineering, Iowa State University.

Employment History

2016–present
Assistant Professor, Dept. of Computer Science, Boise State University
Co-director, People and Information Research Team (PIReT)
2014–2016
Assistant Professor, Dept. of Computer Science, Texas State University
2008–2014
Graduate Research Assistant, GroupLens Research, University of Minnesota
Su 2012, F 2013
Instructor, University of Minnesota
Summer 2010
Research Intern, Autodesk Research, Toronto, CA
2007–2008, S 2011
Teaching Assistant, University of Minnesota
2005–2007
Undergrad Research Assistant, Scalable Computing Laboratory, Iowa State University

Teaching

Boise State University

TermCourseTitleCreditsStudents
F21CS 538Recommender Systems311
F20CS 533Intro to Data Science322
S20CS 697Equity and Discrimination33
S20CS 410Databases336
F19CS 533Intro to Data Science328
S19CS 538Recommender Systems312
F18CS 410/510Databases340
Su18CS 310-HUIntro to Databases16
S18CS 410/510Databases322
F17CS 533Intro to Data Science322
S17CS 597Recommender Systems313
F16CS 410/510Databases328

Texas State University

  • CS 4332 (Intro to Database Systems)
  • CS 3320 (Internet Software Development)
  • CS 5369Q/4379Q (Recommender Systems)
  • CS 4350 (Unix Systems Programming)

Coursera

I co-created the Recommender Systems specialization on Coursera, along with its two previous single-class versions, with Joseph A. Konstan.

University of Minnesota

  • Instructor for CS 5980-1 (Intro to Recommender Systems)
  • Summer instructor for CS 1902 (Structure of Computer Programming II)
  • TA for CSCI 5125 (Collaborative and Social Computing) and CSCI 1902

Teaching Professional Development

  • Boise State University Ten for Teaching program.
  • Boise State University Center for Teaching and Learning Course Design Institute, a one-week intensive session in Summer 2017.
  • CTL workshops on service learning, mastery-based grading, and other topics.
  • Texas State University’s Program for Excellence in Teaching and Learning (2014–2015).
  • Preparing Future Faculty at the University of Minnesota.

Publications

Author formatting key:

  • myself, advised student, other Boise State student
  • †presenter
  • ‡equal first authors.
  • Citation counts from Semantic Scholar.

Book Chapters

RSHB3E
2021

Michael D. Ekstrand, Anubrata Das, Robin Burke, and Fernando Diaz. 2021. Fairness in Recommender Systems. To appear in Recommender Systems Handbook (3rd edition). Francesco Ricci, Lior Roach, and Bracha Shapira, eds. Springer-Verlag.

SocAcc
2018

Daniel Kluver, Michael D. Ekstrand, and Joseph A. Konstan. 2018. Rating-Based Collaborative Filtering: Algorithms and Evaluation. In Social Information Access. Peter Brusilovsky and Daqing He, eds. Springer-Verlag, Lecture Notes in Computer Science vol. 10100, pp. 344–390. DOI 10.1007/978-3-319-90092-6_10. ISBN 978-3-319-90091-9. Cited 54 times.

Journal Publications

FnT21
2021

Michael D. Ekstrand, Anubrata Das, Robin Burke, and Fernando Diaz. 2021. Fairness and Discrimination in Information Access Systems. Foundations and Trends® in Information Retrieval (to appear), 92 pp. arXiv:2105.05779 [cs.IR]. Impact factor: 8. Cited 1 time.

UMUAI21
2021

Michael D. Ekstrand and Daniel Kluver. 2021. Exploring Author Gender in Book Rating and Recommendation. User Modeling and User-Adapted Interaction 31(3) (February 2021), 377–420. DOI 10.1007/s11257-020-09284-2. NSF PAR 10218853. Impact factor: 4.412.

AJIM20
2020

Michael D. Ekstrand, Katherine Landau Wright, and Maria Soledad Pera. 2020. Enhancing Classroom Instruction with Online News. Aslib Journal of Information Management 72(5) (June 2020), 725–744. DOI 10.1108/AJIM-11-2019-0309. Impact factor: 1.903. Cited 3 times.

JOT16
2016

Michael D. Ekstrand and Michael Ludwig. 2016. Dependency Injection with Static Analysis and Context-Aware Policy. Journal of Object Technology 15(1) (February 2016), 1:1–31. DOI 10.5381/jot.2016.15.1.a1. Cited 1 time.

TOCHI15
2015

Joseph A. Konstan, J.D. Walker, D. Christopher Brooks, Keith Brown, and Michael D. Ekstrand. 2015. Teaching Recommender Systems at Large Scale: Evaluation and Lessons Learned from a Hybrid MOOC. Transactions on Computer-Human Interaction 22(2) (April 2015). DOI 10.1145/2728171. Impact factor: 1.293. Cited 20 times.

VLDB11
2011

Justin J. Levandoski, Michael D. Ekstrand, Michael J. Ludwig, Ahmad Eldawy, Mohamed F. Mokbel, and John T. Riedl. 2011. RecBench: Benchmarks for Evaluating Performance of Recommender System Architectures. Proceedings of the VLDB Endowment 4(11) (August 2011), 911–920. Acceptance rate: 18%. Cited 10 times.

FnT11
2011

Michael D. Ekstrand, John T. Riedl, and Joseph A. Konstan. 2011. Collaborative Filtering Recommender Systems. Foundations and Trends® in Human-Computer Interaction 4(2) (February 2011), 81–173. DOI 10.1561/1100000009. Cited 605 times.

Conference Publications

These papers have been published in peer-reviewed conference proceedings.

UMAP21
2021

A. K. M. Nuhil Mehdy, Michael D. Ekstrand, Bart Knijnenburg, and Hoda Mehrpouyan. 2021. Privacy as a Planned Behavior: Effects of Situational Factors on Privacy Perceptions and Plans. In Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization (UMAP ’21). ACM. DOI 10.1145/3450613.3456829. arXiv:2104.11847 [cs.SI]. NSF PAR 10223377. Acceptance rate: 23%. Cited 2 times.

WWW21
2021

Ömer Kırnap, Fernando Diaz, Asia J. Biega, Michael D. Ekstrand, Ben Carterette, and Emine Yılmaz. 2021. Estimation of Fair Ranking Metrics with Incomplete Judgments. In Proceedings of The Web Conference 2021 (TheWebConf 2021). ACM. DOI 10.1145/3442381.3450080. arXiv:2108.05152. NSF PAR 10237411. Acceptance rate: 21%.

CIKM20-lk
2020

Michael D. Ekstrand. 2020. LensKit for Python: Next-Generation Software for Recommender Systems Experiments. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM ’20, Resource track). ACM, pp. 2999–3006. DOI 10.1145/3340531.3412778. arXiv:1809.03125 [cs.IR]. NSF PAR 10199450. No acceptance rate reported. Cited 7 times.

CIKM20-ee
2020

Fernando Diaz, Bhaskar Mitra, Michael D. Ekstrand, Asia J. Biega, and Ben Carterette. 2020. Evaluating Stochastic Rankings with Expected Exposure. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM ’20). ACM, pp. 275–284. DOI 10.1145/3340531.3411962. arXiv:2004.13157 [cs.IR]. NSF PAR 10199451. Acceptance rate: 20%. Nominated for Best Long Paper. Cited 29 times.

CHIIR20
2020

Mucun Tian and Michael D. Ekstrand. 2020. Estimating Error and Bias in Offline Evaluation Results. Short paper in Proceedings of the 2020 Conference on Human Information Interaction and Retrieval (CHIIR ’20). ACM, 5 pp. DOI 10.1145/3343413.3378004. arXiv:2001.09455 [cs.IR]. NSF PAR 10146883. Acceptance rate: 47%. Cited 4 times.

RecSys18
2018

Michael D. Ekstrand, Mucun Tian, Mohammed R. Imran Kazi, Hoda Mehrpouyan, and Daniel Kluver. 2018. Exploring Author Gender in Book Rating and Recommendation. In Proceedings of the 12th ACM Conference on Recommender Systems (RecSys ’18). ACM, pp. 242–250. DOI 10.1145/3240323.3240373. arXiv:1808.07586v1 [cs.IR]. Acceptance rate: 17.5%. Cited 61 times.

FAT18-ck
2018

Michael D. Ekstrand, Mucun Tian, Ion Madrazo Azpiazu, Jennifer D. Ekstrand, Oghenemaro Anuyah, David McNeill, and Maria Soledad Pera. 2018. All The Cool Kids, How Do They Fit In?: Popularity and Demographic Biases in Recommender Evaluation and Effectiveness. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency (FAT* 2018). PMLR, Proceedings of Machine Learning Research 81:172–186. Acceptance rate: 24%. Cited 74 times.

FAT18-fp
2018

Michael D. Ekstrand, Rezvan Joshaghani, and Hoda Mehrpouyan. 2018. Privacy for All: Ensuring Fair and Equitable Privacy Protections. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency (FAT* 2018). PMLR, Proceedings of Machine Learning Research 81:35–47. Acceptance rate: 24%. Cited 36 times.

FLAIRS17-s
2017

Michael D. Ekstrand and Vaibhav Mahant. 2017. Sturgeon and the Cool Kids: Problems with Random Decoys for Top-N Recommender Evaluation. In Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference (Recommender Systems track). AAAI, pp. 639–644. No acceptance rate reported. Cited 5 times.

FLAIRS17-rr
2017

Sushma Channamsetty and Michael D. Ekstrand. 2017. Recommender Response to Diversity and Popularity Bias in User Profiles. Short paper in Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference (Recommender Systems track). AAAI, pp. 657–660. No acceptance rate reported. Cited 10 times.

RecSys15
2015

Michael D. Ekstrand, Daniel Kluver, F. Maxwell Harper, and Joseph A. Konstan. 2015. Letting Users Choose Recommender Algorithms: An Experimental Study. In Proceedings of the 9th ACM Conference on Recommender Systems (RecSys ’15). ACM. DOI 10.1145/2792838.2800195. Acceptance rate: 21%. Cited 74 times.

RecSys14
2014

Michael D. Ekstrand, F. Maxwell Harper, Martijn C. Willemsen, and Joseph A. Konstan. 2014. User Perception of Differences in Recommender Algorithms. In Proceedings of the 8th ACM Conference on Recommender Systems (RecSys ’14). ACM. DOI 10.1145/2645710.2645737. Acceptance rate: 23%. Cited 115 times.

L@S14
2014

Joseph A. Konstan, J.D. Walker, D. Christopher Brooks, Keith Brown, and Michael D. Ekstrand. 2014. Teaching Recommender Systems at Large Scale: Evaluation and Lessons Learned from a Hybrid MOOC. In Proceedings of the First ACM Conference on Learning @ Scale (S ’14). ACM. DOI 10.1145/2556325.2566244. Acceptance rate: 37%. Cited 48 times.

RecSys13
2013

Tien T. Nguyen, Daniel Kluver, Ting-Yu Wang, Pik-Mai Hui, Michael D. Ekstrand, Martijn C. Willemsen, and John Riedl. 2013. Rating Support Interfaces to Improve User Experience and Recommender Accuracy. In Proceedings of the 7th ACM Conference on Recommender Systems (RecSys ’13). ACM. DOI 10.1145/2507157.2507188. Acceptance rate: 24%. Cited 34 times.

RecSys12-b
2012

Daniel Kluver, Tien T. Nguyen, Michael Ekstrand, Shilad Sen, and John Riedl. 2012. How Many Bits per Rating?. In Proceedings of the Sixth ACM Conference on Recommender Systems (RecSys ’12). ACM, pp. 99–106. DOI 10.1145/2365952.2365974. Acceptance rate: 20%. Cited 36 times.

RecSys12-f
2012

Michael Ekstrand and John Riedl. 2012. When Recommenders Fail: Predicting Recommender Failure for Algorithm Selection and Combination. Short paper in Proceedings of the Sixth ACM Conference on Recommender Systems (RecSys ’12). ACM, pp. 233–236. DOI 10.1145/2365952.2366002. Acceptance rate: 32%. Cited 54 times.

EDBT12
2012

Justin J. Levandoski, Mohamed Sarwat, Mohamed F. Mokbel, and Michael D. Ekstrand. 2012. RecStore: An Extensible And Adaptive Framework for Online Recommender Queries Inside the Database Engine. In Proceedings of the 15th International Conference on Extending Database Technology (EDBT ’12). ACM, pp. 86–96. DOI 10.1145/2247596.2247608. Acceptance rate: 23%. Cited 14 times.

RecSys11
2011

Michael D. Ekstrand, Michael Ludwig, Joseph A. Konstan, and John T. Riedl. 2011. Rethinking The Recommender Research Ecosystem: Reproducibility, Openness, and LensKit. In Proceedings of the Fifth ACM Conference on Recommender Systems (RecSys ’11). ACM, pp. 133–140. DOI 10.1145/2043932.2043958. Acceptance rate: 27% (20% for oral presentation, which this received). Cited 161 times.

UIST11
2011

Michael Ekstrand, Wei Li, Tovi Grossman, Justin Matejka, and George Fitzmaurice. 2011. Searching for Software Learning Resources Using Application Context. In Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology (UIST ’11). ACM, pp. 195–204. DOI 10.1145/2047196.2047220. Acceptance rate: 25%. Cited 41 times.

RecSys10
2010

Michael D. Ekstrand, Praveen Kannan, James A. Stempter, John T. Butler, Joseph A. Konstan, and John T. Riedl. 2010. Automatically Building Research Reading Lists. In Proceedings of the 4th ACM Conference on Recommender Systems (RecSys ’10). ACM, pp. 159–166. DOI 10.1145/1864708.1864740. Acceptance rate: 19%. Cited 94 times.

WikiSym09
2009

Michael D. Ekstrand and John T. Riedl. 2009. rv you’re dumb: Identifying Discarded Work in Wiki Article History. In Proceedings of the 5th International Symposium on Wikis and Open Collaboration (WikiSym ’09). ACM, 10 pp. DOI 10.1145/1641309.1641317. Acceptance rate: 36%. Selected as Best Paper. Cited 31 times.

Workshops, Seminars, Posters, Etc.

These papers have been peer-reviewed for workshops, poster proceedings, and similar venues.

RSPE21-inf
2021

Ngozi Ihemelandu and Michael D. Ekstrand. 2021. Statistical Inference: The Missing Piece of RecSys Experiment Reliability Discourse. To appear in Proceedings of the RecSys 2021 Workshop on Perspectives on the Evaluation of Recommender Systems (RecSys ’21). arXiv:2109.06424.

RSLBR21
2021

Lawrence Spear, Ashlee Milton, Garrett Allen, Amifa Raj, Michael Green, Michael D. Ekstrand, and Maria Soledad Pera. 2021. Baby Shark to Barracuda: Analyzing Children’s Music Listening Behavior. To appear in RecSys 2021 Late-Breaking Results (RecSys ’21).

KidRec21
2021

Amifa Raj, Ashlee Milton, and Michael D. Ekstrand. 2021. Pink for Princesses, Blue for Superheroes: The Need to Examine Gender Stereotypes in Kids’ Products in Search and Recommendations. In Proceedings of the 5th International and Interdisciplinary Workshop on Children & Recommender Systems (KidRec ’21), at IDC 2021. arXiv:2105.09296.

FAcctRec20
2020

Amifa Raj, Connor Wood, Ananda Montoly, and Michael D. Ekstrand. 2020. Comparing Fair Ranking Metrics. Presented at the 3rd FAccTrec Workshop on Responsible Recommendation (peer-reviewed but not archived). arXiv:2009.01311 [cs.IR]. Cited 6 times.

RSDemos19
2019

Ashlee Milton, Michael Green, Adam Keener, Joshua Ames, Michael D. Ekstrand, and Maria Soledad Pera. 2019. StoryTime: Eliciting Preferences from Children for Book Recommendations. Demo recorded in Proceedings of the 13th ACM Conference on Recommender Systems (RecSys ’19). 2 pp. DOI 10.1145/3298689.3347048. NSF PAR 10133610. Cited 3 times.

ComplexRec18
2018

Michael D. Ekstrand, Ion Madrazo Azpiazu, Katherine Landau Wright, and Maria Soledad Pera. 2018. Retrieving and Recommending for the Classroom: Stakeholders, Objectives, Resources, and Users. In Proceedings of the ComplexRec 2018 Second Workshop on Recommendation in Complex Scenarios (ComplexRec ’18), at RecSys 2018. Cited 4 times.

Reveal18-mc
2018

Mucun Tian and Michael D. Ekstrand. 2018. Monte Carlo Estimates of Evaluation Metric Error and Bias. Computer Science Faculty Publications and Presentations 148. Boise State University. Presented at the REVEAL 2018 Workshop on Offline Evaluation for Recommender Systems, a workshop at RecSys 2018. DOI 10.18122/cs_facpubs/148/boisestate. NSF PAR 10074452.

Reveal18-lk
2018

Michael D. Ekstrand. 2018. The LKPY Package for Recommender Systems Experiments: Next-Generation Tools and Lessons Learned from the LensKit Project. Computer Science Faculty Publications and Presentations 147. Boise State University. Presented at the REVEAL 2018 Workshop on Offline Evaluation for Recommender Systems, a workshop at RecSys 2018. DOI 10.18122/cs_facpubs/147/boisestate. arXiv:1809.03125v1 [cs.IR]. Cited 17 times.

KidRec18
2018

Maria Soledad Pera, Katherine Wright, and Michael D. Ekstrand. 2018. Recommending Texts to Children with an Expert in the Loop. In Proceedings of the 2nd International Workshop on Children & Recommender Systems (KidRec ’18), at IDC 2018. DOI 10.18122/cs_facpubs/140/boisestate. Cited 4 times.

CHIPriv18
2018

Rezvan Joshaghani, Michael D. Ekstrand, Bart Knijnenburg, and Hoda Mehrpouyan. 2018. Do Different Groups Have Comparable Privacy Tradeoffs?. At Moving Beyond a ‘One-Size Fits All’ Approach: Exploring Individual Differences in Privacy, a workshop at CHI 2018. NSF PAR 10222636. Cited 1 time.

RSPosters17
2017

Michael D. Ekstrand and Maria Soledad Pera. 2017. The Demographics of Cool: Popularity and Recommender Performance for Different Groups of Users. In RecSys 2017 Poster Proceedings. CEUR, Workshop Proceedings 1905. Cited 4 times.

KidRec17
2017

Michael D. Ekstrand. 2017. Challenges in Evaluating Recommendations for Children. In Proceedings of the International Workshop on Children & Recommender Systems (KidRec), at RecSys 2017.

RecSys16
2016

Michael D. Ekstrand and Martijn C. Willemsen. 2016. Behaviorism is Not Enough: Better Recommendations through Listening to Users. In Proceedings of the Tenth ACM Conference on Recommender Systems (RecSys ’16, Past, Present, and Future track). ACM. DOI 10.1145/2959100.2959179. Acceptance rate: 36%. Cited 50 times.

HealthRec16
2016

Jennifer D. Ekstrand and Michael D. Ekstrand. 2016. First Do No Harm: Considering and Minimizing Harm in Recommender Systems Designed for Engendering Health. In Proceedings of the Workshop on Recommender Systems for Health at RecSys ’16. Cited 10 times.

CSW14
2014

Michael D. Ekstrand. 2014. Building Open-Source Tools for Reproducible Research and Education. At Sharing, Re-use, and Circulation of Resources in Cooperative Scientific Work, a workshop at CSCW 2014.

RSDemos11
2011

Michael D. Ekstrand, Michael Ludwig, Jack Kolb, and John T. Riedl. 2011. LensKit: A Modular Recommender Framework. Demo recorded in Proceedings of the 5th ACM Conference on Recommender Systems (RecSys ’11). ACM, pp. 349-350. DOI 10.1145/2043932.2044001. Cited 29 times.

Tutorials

⸘2019‽
2019

Michael D. Ekstrand, Fernando Diaz, and Robin Burke. 2019. Fairness and Discrimination in Recommendation and Retrieval. Tutorial presented at Proceedings of the 13th ACM Conference on Recommender Systems (RecSys ’19). 2 pp. DOI 10.1145/3298689.3346964.

⸘2019‽
2019

Michael D. Ekstrand, Fernando Diaz, and Robin Burke. 2019. Fairness and Discrimination in Retrieval and Recommendation. Tutorial presented at Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’19). 2 pp. DOI 10.1145/3331184.3331380.

Other Publications and Presentations

These publications are unreviewed reports, preprints, abstract-only presentations, etc.

RSPE21-dist
2021

Michael D. Ekstrand, Ben Carterette, and Fernando Diaz. 2021. Evaluating Recommenders with Distributions. At Proceedings of the RecSys 2021 Workshop on Perspectives on the Evaluation of Recommender Systems (RecSys ’21).

AIMAG21
2021

Nasim Sonboli, Robin Burke, Michael Ekstrand, and Rishabh Mehrotra. 2021. The Multisided Complexity of Fairness in Recommender Systems. AI Magazine (to appear).

⸘2020‽
2020

Michael D. Ekstrand, Pierre-Nicolas Schwab, Jean Garcia-Gathright, Toshihiro Kamishima, and Nasim Sonboli. 2020. 3rd FATREC Workshop: Responsible Recommendation. In Proceedings of the 14th ACM Conference on Recommender Systems (RecSys ’20). ACM. DOI 10.1145/3383313.3411538. Cited 2 times.

⸘2020‽
2020

Bamshad Mobasher, Stylani Kleanthous, Michael D. Ekstrand, Bettina Berendt, Janna Otterbacher, and Avital Schulner Tal. 2020. FairUMAP 2020: The 3rd Workshop on Fairness in User Modeling, Adaptation and Personalization. In Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization (UMAP ’20). ACM. DOI 10.1145/3340631.3398671. Cited 1 time.

⸘2020‽
2020

Asia J. Biega, Fernando Diaz, Michael D. Ekstrand, and Sebastian Kohlmeier. 2020. Overview of the TREC 2019 Fair Ranking Track. In The Twenty-Eighth Text REtrieval Conference (TREC 2019) Proceedings (TREC 2019). arXiv:2003.11650. Cited 8 times.

FORUM19
2019

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 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 Sayed, Maria Panteli, Mark Sanderson, Matthew Lease, Preethi Lahoti, and Toshihiro Kamishima. 2019. FACTS-IR: Fairness, Accountability, Confidentiality, Transparency, and Safety in Information Retrieval. SIGIR Forum 53(2) (December 2019), 20–43. DOI 10.1145/3458553.3458556. Cited 1 time.

⸘2019‽
2019

Bettina Berendt, Veronika Bogina, Robin Burke, Michael D. Ekstrand, Alan Hartman, Stylani Kleanthous, Tsvi Kuflik, Bamshad Mobasher, and Janna Otterbacher. 2019. FairUMAP 2019 Chairs’ Welcome Overview. In Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization (UMAP ’19). ACM. DOI 10.1145/3314183.3323842.

⸘2019‽
2019

Alexandra Olteanu, Jean Garcia-Gathright, Maarten de Rijke, and Michael D. Ekstrand. 2019. Workshop on Fairness, Accountability, Confidentiality, Transparency, and Safety in Information Retrieval (FACTS-IR). In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’19). ACM. DOI 10.1145/3331184.3331644. Cited 5 times.

⸘2019‽
2019

Katherine Landau Wright, David McNeill, Michael D. Ekstrand, and Maria Soledad Pera. 2019. Supplementing Classroom Texts with Online Resources. At 2019 American Educational Research Association Conference.

⸘2019‽
2019

Michael D. Ekstrand and Joseph A. Konstan. 2019. Recommender Systems Notation: Proposed Common Notation for Teaching and Research. Computer Science Faculty Publications and Presentations 177. Boise State University. DOI 10.18122/cs_facpubs/177/boisestate. arXiv:1902.01348 [cs.IR]. Cited 3 times.

DAGMAN18
2018

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, and Justin Zobel. 2018. 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) (November 2018), 96–139. DOI 10.4230/DagMan.7.1.96. Cited 10 times.

⸘2018‽
2018

Katherine Landau Wright, Michael D. Ekstrand, and Maria Soledad Pera. 2018. Supplementing Classroom Texts with Online Resources. At 2018 Annual Meeting of the Northwest Rocky Mountain Educational Research Association.

⸘2018‽
2018

Toshihiro Kamishima, Pierre-Nicolas Schwab, and Michael D. Ekstrand. 2018. 2nd FATREC Workshop: Responsible Recommendation. In Proceedings of the 12th ACM Conference on Recommender Systems (RecSys ’18). ACM. DOI 10.1145/3240323.3240335. Cited 6 times.

⸘2018‽
2018

Bamshad Mobasher, Robin Burke, Michael D. Ekstrand, and Bettina Berendt. 2018. UMAP 2018 Fairness in User Modeling, Adaptation and Personalization (FairUMAP 2018) Chairs’ Welcome & Organization. In Adjunct Publication of the 26th Conference on User Modeling, Adaptation, and Personalization (UMAP ’18). ACM. DOI 10.1145/3213586.3226200.

FORUM18
2018

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, and Justin Zobel. 2018. The Dagstuhl Perspectives Workshop on Performance Modeling and Prediction. SIGIR Forum 52(1) (June 2018), 91–101. DOI 10.1145/3274784.3274789. Cited 14 times.

⸘2017‽
2017

Michael D. Ekstrand. 2017. Yak Shaving with Michael Ekstrand. CSR Tales no. 4 (December 2017). PURL https://purl.org/mde/alpaca.

⸘2017‽
2017

Michael D. Ekstrand and Amit Sharma. 2017. The FATREC Workshop on Responsible Recommendation. In Proceedings of the 11th ACM Conference on Recommender Systems (RecSys ’17). ACM. DOI 10.1145/3109859.3109960. Cited 10 times.

⸘2014‽
2014

Michael D. Ekstrand. 2014. Towards Recommender Engineering: Tools and Experiments in Recommender Differences. Ph.D thesis, University of Minnesota. HDL 11299/165307. Cited 5 times.

⸘2011‽
2011

Martijn Willemsen, Dirk Bollen, and Michael Ekstrand. 2011. UCERSTI 2: Second Workshop on User-Centric Evaluation of Recommender Systems and Their Interfaces. In Proceedings of the 5th ACM Conference on Recommender Systems (RecSys ’11). ACM, pp. 395–396. DOI 10.1145/2043932.2044020. Cited 9 times.

Software and Data

I have built several open-source software packages and data sets in the course of my research and other work. Open-source software distribution and open data are key pieces of my research dissemination strategy. My most significant development efforts are:

  • LensKit, a toolkit for building, researching, and studying recommender systems. As of May 1, 2021, the original Java software (in development 2010–2018; paper RecSys11) is known to be used in 64 papers and theses and was used by over 2500 students to complete programming assignments in the Recommender Systems MOOC. The Python software (2018–, papers CIKM20-lk and Reveal18-lk) is used in at least 12 papers, theses, and educational resources, including the PBS show Crash Course AI, and has been downloaded 6,699 times from the Python Package Index in the last 6 months (according to PyPIStats). The current version is 0.12.3, released on March 6, 2021; it is the 18th release of LensKit for Python.
    https://lenskit.org (current list of known uses: https://lenskit.org/research/)
  • Book Data Tools, software tools to integrate multiple public sources of book and book consumption data into a data set for studying social effects in book publication, reading, and recommendation. Used in UMUAI21 and RecSys18. https://bookdata.piret.info

My work has also produced a number of utility packages to support this software and other efforts, including:

  • seedbank, a Python package for consistently seeding random number generators. https://seedbank.lenskit.org
  • csr, a Python package for managing sparse matrices in CSR format compatible with the Numba JIT for scientific python, and with Intel MKL acceleration for several operations.
    https://csr.lenskit.org
  • binpickle, a Python package for saving scientific data structures (such as machine learning models) to disk in either compressed or memory-mappable format. LensKit uses this package to serialize models for both storage and shared-memory parallelism. https://binpickle.lenskit.org
  • happylog, a Rust package for easily configuring log output for command-line programs.
    https://github.com/mdekstrand/happylog
  • Grapht, a dependency injection framework for Java with novel configuration and static analysis capabilities (paper JOT16). http://grapht.grouplens.org

Students

Research Funding

External Grants

Internal Grants

  • 2017: $19K Boise State College of Education Civility Grant LITERATE: Locating Informational Texts for Engaging Readers And Teaching Equitably (co-PI; with PI Katherine Wright & co-PI Sole Pera)
  • 2014: $8K Texas State University Research Enhancement Program (competitive internal research grant) Temporal Analysis of Recommender Systems (PI)

Invited Talks

  • Oct. 2020: Guest lecture on recommender systems and fairness for Carnegie Mellon University Human-AI Interaction course
  • Apr. 2020: Guest lecture on recommender systems and fairness for Emory University recommender systems course
  • Oct. 2019: ‘Online Recommendation: What? Where? Why? How?’ session at the Idaho Library Association 2019 Conference
  • Aug. 2019: ‘User, Agent, Subject, Spy’ seminar at Microsoft Research Montréal
  • Jul. 2019: ‘User, Agent, Subject, Spy’ seminar at Criteo AI Labs, Paris, France
  • May 2019: ‘Recommendations, Decisions, Feedback Loops, and Maybe Saving the Planet’ at the CRA CCC Visioning Workshop on Economics and Fairness.
  • Dec. 2018: ‘User, Agent, Subject, Spy’ seminar at Clemson University
  • Nov. 2018: ‘User, Agent, Subject, Spy’ seminar at Carnegie Mellon University Human-Computer Interaction Institute
  • Nov. 2018: Guest lecture on recommender systems for Carnegie Mellon University Human-AI Interaction course
  • Nov. 2017: ‘Making Information Systems Good for People’ at Whitman College (Walla Walla, WA)
  • Jun. 2017: ‘Recommending for People’ seminar at RecSysNL at TU Delft
  • Jun. 2017: ‘Recommending for People’ seminar at Jheronimus Academy of Data Science
  • Jun. 2017: ‘Recommending for People’ seminar at UCL Mons
  • Jun. 2017: ‘Responsible Recommendation’ at the Brussels Big Data and Ethics Meetup, the inaugural event of the DigitYser Big Data community
  • Nov. 2016: ‘Recommending for People’ colloquium at the University at Albany Dept. of Computer Science
  • Oct. 2016: ‘Introduction to Recommender Systems’ at the Clearwater Developer Conference
  • Sep. 2015: ‘Challenges in Scaling Recommender Systems Research’ at the Workshop on Large-Scale Recommender Systems at RecSys ’15 in Vienna, Austria
  • Sep. 2015: ‘Levelling Up your Academic Career’ at the Doctoral Symposium at RecSys ’15 in Vienna, Austria
  • Sep. 2012: ‘Flexible Recommender Experiments with LensKit’ at the RecSys Challenge Workshop at RecSys ’12 in Dublin, Ireland
  • Sep. 2012: ‘The MovieLens Data Set’ (invited talk) at the RecSys Challenge Workshop at RecSys ’12 in Dublin, Ireland

Service

Ongoing Professional Service, Memberships, and Honors

  • Senior Member of the Association for Computing Machinery
  • Executive committee, ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2020–present
  • Co-chair, FAccT Network, 2019–present
  • Steering committee, ACM Conference on Recommender Systems (RecSys), 2017–present
  • Steering committee, ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2017–present

Program Committee and Editorial Service

  • Guest editor, 2021 special issue of User Modeling and User-Adapted Interaction on fairness in user modeling.
  • Distinguished Reviewer, ACM Transactions on Interactive Intelligent Systems (TiiS) (2017–present)
  • ACM Conference on Recommender Systems (Senior PC 2019–2021, PC 2014–2017)
  • ACM Conference on Fairness, Accountability, and Transparency (FAccT) (2018–2021, Area Chair 2018)
  • ACM CIKM (Resource Track PC 2020–2021)
  • ACM SIGIR (PC 2020–2021 Full and Short Papers; 2021 Perspective and Resource papers)
  • NeurIPS Ethical Review panel (2021)
  • TheWebConf Track on Behavior Analysis and Personalization (Senior PC 2021, PC 2016–2020)
  • Track chair, User Modeling and Adaptive Personalization (UMAP) 2021
  • User Modeling and Adaptive Personalization (2019–2020)
  • Workshop on Fairness, Accountability, and Transparency in Machine Learning (FATML) (2017)
  • FLAIRS Special Track on Recommender Systems (2015–2017)
  • SAC Recommender Systems track (2013, 2017)
  • Ad-hoc conference reviews for CHI, CSCW, IUI, UIST, WikiSym, UMAP, ICWSM.
  • Reviewed for Communications of the ACM; ACM journals TDS, TOCHI, TIST, TOIS, TWEB, TKDD, and TIIS; IEEE journals TDSC and TKDE; Interacting with Computers; UMUAI; Information Retrieval Journal; ACM Computing Surveys; Artificial Intelligence Review; and others.
  • Grant proposal reviews for NSF (US 2019, 2020), NWO (NL), FWF & WWTF (AT)

Other Professional Service

  • Co-organizer, SimuRec Workshop on Simulation and Synthetic Data for Recommender Systems at RecSys 2021
  • Sponsorship co-chair, ACM FAccT 2021–2022
  • Organized and moderated panel at RecSys 2019 on responsible recommendation
  • Co-organizer, TREC Track on Fairness in Information Retrieval (2019–2021)
  • PR & Publicity co-chair, 2nd Conference on Fairness, Accountability, and Transparency (ACM FAT* 2019)
  • General co-chair, ACM RecSys 2018
  • Publications working group, FAccT steering committee (2017)
  • Co-organizer, FATREC Workshop on Responsible Recommendation at RecSys 2017, 2018, 2020, 2021
  • Co-organizer, Workshop on Fairness, Accountability, Confidentiality, Transparency, and Safety in Information Retrieval (FACTS-IR) at SIGIR 2019
  • Co-organizer, FairUMAP workshop at UMAP 2018–2020
  • Track co-chair, 2018 Conference on Fairness, Accountability, and Transparency Systems track
  • Participant in Dagstuhl Perspectives Workshop Towards Cross-Domain Performance Modeling and Prediction: IR/RecSys/NLP
  • Publicity co-chair, ACM RecSys 2016
  • External advisor, CrowdRec (EU Framework Programme collaborative research project, 2014–2016)
  • Proceedings co-chair, ACM CHI 2012–2013
  • Demos co-chair, ACM RecSys 2012

Department and University Service

  • 2020–2021 CS Faculty Search Committee
  • COEN SAGE Scholars Program Mentor (2019–2021)
  • Boise State College of Engineering Curriculum Committee (2019–present)
  • Boise State Ph.D in Computing Steering Committee (2017–present)
  • Boise State CS Dept. Curriculum Committee (2017–present)
  • Boise State CS Dept. Graduate Recruiting Committee (2017)
  • Texas State CS Dept. Undergraduate Committee (2014–2016)
  • Texas State CS Dept. Written Comp Exam Grading (2014–2016)
  • UMN CS Graduate Student Association secretary (2009–2010)

Community Service

  • July 2020 — taught continuing education session for Idaho Council for Libraries
  • October 2019 — presented at Idaho Library Association Annual Conference
  • February 2019 — addressed Idaho State House Judiciary Committee on H.B. 118, regulating pretrial risk assessment algorithms
  • December 2017 — Boise Public Library panel on preparing for a career in computer science
  • Judge, 2015 — Travis Elementary School Science Fair

Media Mentions