Michael D. Ekstrand, Ph.D

Dept. of Information Science
Drexel University
3675 Market St.
Philadelphia, PA 19104


https://md.ekstrandom.net
https://piret.info

Education

Ph.D (2014)
Computer Science, University of Minnesota, Minneapolis, MN.
B.S. (2007)
Computer Engineering, Iowa State University, Ames, IA.

Appointments

2023–present
Assistant Professor, Dept. of Information Science, Drexel University
2022–2023
Associate Professor, Dept. of Computer Science, Boise State University
2016–2022
Assistant Professor, Dept. of Computer Science, Boise State University
2014–2016
Assistant Professor, Dept. of Computer Science, Texas State University

Research

Funding

  • 2023–2025: NSF 22-32553: Collaborative Research: CCRI: New: A Research News Recommender Infrastructure with Live Users for Algorithm and Interface Experimentation ($1.4M, my share $150K).
  • 2018–2023: NSF 17-51278: CAREER: User-Based Simulation Methods for Quantifying Sources of Error and Bias in Recommender Systems. ($514,081, including REU supplements).
  • 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: Texas State University Research Enhancement Program (competitive internal grant, $8K)

Selected Publications

Author formatting key: myself, advised student, other student.

CHIIR23
2023

Christine Pinney, Amifa Raj, Alex Hanna, and Michael D. Ekstrand. 2023. Much Ado About Gender: Current Practices and Future Recommendations for Appropriate Gender-Aware Information Access. In Proceedings of the 2023 Conference on Human Information Interaction and Retrieval (CHIIR ’23). Proc. CHIIR ’23. DOI 10.1145/3576840.3578316. arXiv:2301.04780. NSF PAR 10423693. Acceptance rate: 39.4%. Cited 3 times. Cited 4 times.

FnT22
2022

Michael D. Ekstrand, Anubrata Das, Robin Burke, and Fernando Diaz. 2022. Fairness in Information Access Systems. Foundations and Trends® in Information Retrieval 16(1–2) (July 11th, 2022), 1–177. FnT IR 16(1–2) (July 11th, 2022). DOI 10.1561/1500000079. arXiv:2105.05779 [cs.IR]. NSF PAR 10347630. Impact factor: 8. Cited 90 times. Cited 51 times.

SIGIR22
2022

Amifa Raj and Michael D. Ekstrand. 2022. Measuring Fairness in Ranked Results: An Analytical and Empirical Comparison. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’22). pp. 726–736. Proc. SIGIR ’22. DOI 10.1145/3477495.3532018. NSF PAR 10329880. Acceptance rate: 20%. Cited 24 times. Cited 20 times.

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 4th, 2021), 377–420. UMUAI 31(3) (February 4th, 2021). DOI 10.1007/s11257-020-09284-2. arXiv:1808.07586v2. NSF PAR 10218853. Impact factor: 4.412. Cited 145 times. Cited 24 times (shared with RecSys18◊).

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. Proc. TheWebConf 2021. DOI 10.1145/3442381.3450080. arXiv:2108.05152. NSF PAR 10237411. Acceptance rate: 21%. Cited 30 times. Cited 27 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. Proc. CIKM ’20. DOI 10.1145/3340531.3411962. arXiv:2004.13157 [cs.IR]. NSF PAR 10199451. Acceptance rate: 20%. Nominated for Best Long Paper. Cited 137 times. Cited 127 times.

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. Proc. CIKM ’20 (Resource track). DOI 10.1145/3340531.3412778. arXiv:1809.03125 [cs.IR]. NSF PAR 10199450. No acceptance rate reported. Cited 72 times. Cited 50 times.

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 15th, 2020), 725–744. AJIM 72(5) (June 15th, 2020). DOI 10.1108/AJIM-11-2019-0309. Impact factor: 1.903. Cited 12 times. Cited 9 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. Proc. FAT* 2018. Acceptance rate: 24%. Cited 216 times. Cited 177 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. Proc. FAT* 2018. Acceptance rate: 24%. Cited 80 times. Cited 67 times.

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. Proc. RecSys ’16 (Past, Present, and Future track). DOI 10.1145/2959100.2959179. Acceptance rate: 36%. Cited 107 times. Cited 86 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. Proc. RecSys ’15. DOI 10.1145/2792838.2800195. Acceptance rate: 21%. Cited 120 times. Cited 97 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. Proc. RecSys ’14. DOI 10.1145/2645710.2645737. Acceptance rate: 23%. Cited 251 times. Cited 171 times.

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 1st, 2015). DOI 10.1145/2728171. Impact factor: 1.293. Cited 110 times (shared with L@S14◊). Cited 23 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. Proc. RecSys ’11. DOI 10.1145/2043932.2043958. Acceptance rate: 27% (20% for oral presentation, which this received). Cited 233 times. Cited 195 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 1st, 2011), 81–173. FnT HCI 4(2) (February 1st, 2011). DOI 10.1561/1100000009. Cited 1574 times. Cited 636 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. Proc. UIST ’11. DOI 10.1145/2047196.2047220. Acceptance rate: 25%. Cited 53 times. Cited 49 times.

Teaching

BSU
Intro to Data Science, recommender systems, databases, ethics
Coursera
Recommender Systems MOOC

Professional Service & Memberships

  • Senior Member, Association for Computing Machinery
  • Editorial board, Foundations and Trends in Information Retrieval
  • Co-organizer, TREC 2019–2021 Track on Fairness in Information Retrieval
  • ACM Conference on Recommender Systems (Program Co-chair 2022, General Co-chair 2018, Steering Committee & SPC member)
  • Conference on Fairness, Accountability, and Transparency (FAccT) (Executive Committee 2020–2023, Steering Committee 2017–2023, Network Co-chair, PC 2017–present)
  • Organizer, FATREC Workshop on Responsible Recommendation at RecSys 2017/2018/2020
  • Senior PC member for RecSys & WWW; regular PC for SIGIR, FAccT, UMAP
  • Reviewer for multiple journals, incl. TOIS, TWEB, TKDD, TIIS, TDSC, TKDE, PLOS ONE, and UMUAI