Michael D. Ekstrand, Ph.D

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


https://md.ekstrandom.net
https://inertial.science

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

External Funding

Selected Publications

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

ECIR24-g
2024

A. Raj and M. D. Ekstrand. 2024. Towards Optimizing Ranking in Grid-Layout for Provider-side Fairness. Proc. ECIR ’24 (IR for Good track). DOI 10.1007/978-3-031-56069-9_7. NSF PAR 10497109. Acceptance rate: 35.9%. Cited 1 time. Cited 1 time.

TORS24-v
2024

M. D. Ekstrand, B. Carterette, and F. Diaz. 2024. Distributionally-Informed Recommender System Evaluation. TORS 2(1) (March 7th, 2024; online August 4th, 2023). DOI 10.1145/3613455. arXiv:2309.05892 [cs.IR]. NSF PAR 10461937. Cited 15 times. Cited 8 times.

CHIIR23
2023

C. Pinney, A. Raj, A. Hanna, and M. D. Ekstrand. 2023. Much Ado About Gender: Current Practices and Future Recommendations for Appropriate Gender-Aware Information Access. Proc. CHIIR ’23. DOI 10.1145/3576840.3578316. arXiv:2301.04780. NSF PAR 10423693. Acceptance rate: 39.4%. Cited 18 times. Cited 11 times.

FnT22
2022

M. D. Ekstrand, A. Das, R. Burke, and F. Diaz. 2022. Fairness in Information Access Systems. FnT IR 16(1–2) (July 11th, 2022). DOI 10.1561/1500000079. arXiv:2105.05779 [cs.IR]. NSF PAR 10347630. Impact factor: 8. Cited 179 times. Cited 83 times.

SIGIR22
2022

A. Raj and M. D. Ekstrand. 2022. Measuring Fairness in Ranked Results: An Analytical and Empirical Comparison. Proc. SIGIR ’22. DOI 10.1145/3477495.3532018. NSF PAR 10329880. Acceptance rate: 20%. Cited 57 times. Cited 44 times.

UMUAI21
2021

M. D. Ekstrand and D. Kluver. 2021. Exploring Author Gender in Book Rating and Recommendation. UMUAI 31(3) (February 4th, 2021). DOI 10.1007/s11257-020-09284-2. arXiv:1808.07586v2. NSF PAR 10218853. Impact factor: 4.412. Cited 199 times (shared with RecSys18◊). Cited 106 times (shared with RecSys18◊).

WWW21
2021

Ö. Kırnap, F. Diaz, A. J. Biega, M. D. Ekstrand, B. Carterette, and E. Yılmaz. 2021. Estimation of Fair Ranking Metrics with Incomplete Judgments. Proc. TheWebConf 2021. DOI 10.1145/3442381.3450080. arXiv:2108.05152. NSF PAR 10237411. Acceptance rate: 21%. Cited 47 times. Cited 36 times.

CIKM20-ee
2020

F. Diaz, B. Mitra, M. D. Ekstrand, A. J. Biega, and B. Carterette. 2020. Evaluating Stochastic Rankings with Expected Exposure. 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 185 times. Cited 165 times.

CIKM20-lk
2020

M. D. Ekstrand. 2020. LensKit for Python: Next-Generation Software for Recommender Systems Experiments. Proc. CIKM ’20 (Resource track). DOI 10.1145/3340531.3412778. arXiv:1809.03125 [cs.IR]. NSF PAR 10199450. No acceptance rate reported. Cited 99 times. Cited 71 times.

AJIM20
2020

M. D. Ekstrand, K. L. Wright, and M. S. Pera. 2020. Enhancing Classroom Instruction with Online News. AJIM 72(5) (November 17th, 2020; online June 14th, 2020). DOI 10.1108/AJIM-11-2019-0309. Impact factor: 1.903. Cited 19 times. Cited 11 times.

FAT18-ck
2018

M. D. Ekstrand, M. Tian, I. Madrazo Azpiazu, J. D. Ekstrand, O. Anuyah, D. McNeill, and M. S. Pera. 2018. All The Cool Kids, How Do They Fit In?: Popularity and Demographic Biases in Recommender Evaluation and Effectiveness. Proc. FAT* 2018. Acceptance rate: 24%. Cited 284 times. Cited 211 times.

FAT18-fp
2018

M. D. Ekstrand, R. Joshaghani, and H. Mehrpouyan. 2018. Privacy for All: Ensuring Fair and Equitable Privacy Protections. Proc. FAT* 2018. Acceptance rate: 24%. Cited 104 times. Cited 77 times.

RecSys16
2016

M. D. Ekstrand and M. C. Willemsen. 2016. Behaviorism is Not Enough: Better Recommendations through Listening to Users. Proc. RecSys ’16 (Past, Present, and Future track). DOI 10.1145/2959100.2959179. Acceptance rate: 36%. Cited 139 times. Cited 94 times.

RecSys15
2015

M. D. Ekstrand, D. Kluver, F. M. Harper, and J. A. Konstan. 2015. Letting Users Choose Recommender Algorithms: An Experimental Study. Proc. RecSys ’15. DOI 10.1145/2792838.2800195. Acceptance rate: 21%. Cited 136 times. Cited 99 times.

RecSys14
2014

M. D. Ekstrand, F. M. Harper, M. C. Willemsen, and J. A. Konstan. 2014. User Perception of Differences in Recommender Algorithms. Proc. RecSys ’14. DOI 10.1145/2645710.2645737. Acceptance rate: 23%. Cited 282 times. Cited 184 times.

RecSys11
2011

M. D. Ekstrand, M. Ludwig, J. A. Konstan, and J. T. Riedl. 2011. Rethinking The Recommender Research Ecosystem: Reproducibility, Openness, and LensKit. Proc. RecSys ’11. DOI 10.1145/2043932.2043958. Acceptance rate: 27% (20% for oral presentation, which this received). Cited 253 times. Cited 195 times.

FnT11
2011

M. D. Ekstrand, J. T. Riedl, and J. A. Konstan. 2011. Collaborative Filtering Recommender Systems. FnT HCI 4(2) (February 1st, 2011). DOI 10.1561/1100000009. Cited 1723 times. Cited 657 times.

Professional Service & Memberships

  • Senior Member, Association for Computing Machinery
  • Associate editor, ACM Transactions on Recommender Systems
  • 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
  • AC for SIGIR, FAccT; SPC for RecSys; numerous other reviews