Michael D. Ekstrand, Ph.D

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

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%.

TORS23
2024

M. D. Ekstrand, B. Carterette, and F. Diaz. 2024. Distributionally-Informed Recommender System Evaluation. TORS 2(1) (March 7th, 2024). DOI 10.1145/3613455. arXiv:2309.05892 [cs.IR]. NSF PAR 10461937. Cited 8 times. Cited 4 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 12 times. Cited 8 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 134 times. Cited 69 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 38 times. Cited 29 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 170 times. Cited 86 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 38 times. Cited 33 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 162 times. Cited 142 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 76 times. Cited 55 times.

AJIM20
2020

M. D. Ekstrand, K. L. Wright, and M. S. Pera. 2020. Enhancing Classroom Instruction with Online News. AJIM 72(5) (June 15th, 2020). DOI 10.1108/AJIM-11-2019-0309. Impact factor: 1.903. Cited 15 times. Cited 10 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 257 times. Cited 195 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 90 times. Cited 68 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 131 times. Cited 89 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 134 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 273 times. Cited 181 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 241 times. Cited 193 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 1641 times. Cited 643 times.

UIST11
2011

M. Ekstrand, W. Li, T. Grossman, J. Matejka, and G. Fitzmaurice. 2011. Searching for Software Learning Resources Using Application Context. Proc. UIST ’11. DOI 10.1145/2047196.2047220. Acceptance rate: 25%. Cited 54 times. Cited 47 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