Michael Ekstrand

NSF CAREER award on recommenders, humans, and data

In 2018, I was awarded the NSF CAREER award to study how recommender systems and our evaluations of them respond to the human messiness of their input data. Us computer scientists have long known the principle of ‘garbage in, garbage out’: with bad data, a system will produce bad outputs. But in practice, computing systems can differ a great deal in precisely how they translate such inputs to outputs.

Our goal in this project is to understand that response — to characterize the ‘garbage response curve’ of common recommendation algorithms and surrounding statistical and experimental techniques. For a given type and quantity of garbage (metric/intent mismatch, discriminatory bias, polarized content), we want to understand its impact on recommendations, subsequent human behavior, and the information experiments provide to operators of recommender systems.

Project Abstract

Systems that recommend products, places, and services are an increasingly common part of everyday life and commerce, making it important to understand how recommendation algorithms affect outcomes for both individual users and larger social groups. To do this, the project team will develop novel methods of simulating users’ behavior based on large-scale historical datasets. These methods will be used to better understand vulnerabilities that underlying biases in training datasets pose to commonly-used machine learning-based methods for building and testing recommender systems, as well as characterize the effectiveness of common evaluation metrics such as recommendation accuracy and diversity given different models of how people interact with recommender systems in practice. The team will publicly release its datasets, software, and novel metrics for the benefit of other researchers and developers of recommender systems. The work also will inform the development of course materials about the social impact of data analytics and computing as well as outreach activities for librarians, who are often in the position of helping information seekers understand the way search engines and other recommender systems affect their ability to get what they need.

The work is organized around two main themes. The first will quantify and mitigate the popularity bias and misclassified decoy problems in offline recommender evaluation that tend to lead to popular, known recommendations. To do this, the team will develop simulation-based evaluation models that encode a variety of assumptions about how users select relevant items to buy and rate and use them to quantify the statistical biases these assumptions induce in recommendation quality metrics. They will calibrate these simulations by comparing with existing data sets covering books, research papers, music, and movies. These models and datasets will help drive the second main project around measuring the impact of feature distributions in training data on recommender algorithm accuracy and diversity, while developing bias-resistant algorithms. The team will use data resampling techniques along with the simulation models, extended to model system behavior over time, to evaluate how different algorithms mitigate, propagate, or exacerbate underlying distributional biases through their recommendations, and how those biased recommendations in turn affect future user behavior and experience.

Research Outcomes

Published Papers and Outputs

Per NSF policy, all published papers are deposited in the NSF Public Access Repository, searchable by award ID; see the list associated with this grant.

ECIR24-i
2024

Michael D. Ekstrand, Lex Beattie, Maria Soledad Pera, and Henriette Cramer. 2024. Not Just Algorithms: Strategically Addressing Consumer Impacts in Information Retrieval. In Proceedings of the 46th European Conference on Information Retrieval (ECIR ’24, IR for Good track). Lecture Notes in Computer Science 14611:314–335. DOI 10.1007/978-3-031-56066-8_25. NSF PAR 10497110. Acceptance rate: 35.9%.

ECIR24-g
2024

Amifa Raj and Michael D. Ekstrand. 2024. Towards Optimizing Ranking in Grid-Layout for Provider-side Fairness. In Proceedings of the 46th European Conference on Information Retrieval (ECIR ’24, IR for Good track). Lecture Notes in Computer Science 14612:90–105. DOI 10.1007/978-3-031-56069-9_7. NSF PAR 10497109. Acceptance rate: 35.9%.

ECIR24-m
2024

Ngozi Ihemelandu and Michael D. Ekstrand. 2024. Multiple Testing for IR and Recommendation System Experiments. Short paper in Proceedings of the 46th European Conference on Information Retrieval (ECIR ’24). Lecture Notes in Computer Science 14610:449–457. DOI 10.1007/978-3-031-56063-7_37. NSF PAR 10497108. Acceptance rate: 24.3%.

FAccTRec23
2023

Amifa Raj and Michael D. Ekstrand. 2023. Towards Measuring Fairness in Grid Layout in Recommender Systems. Presented at the 6th FAccTrec Workshop on Responsible Recommendation (peer-reviewed but not archived). arXiv:2309.10271 [cs.IR]. Cited 1 time.

WI23
2023

Ngozi Ihemelandu and Michael D. Ekstrand. 2023. Candidate Set Sampling for Evaluating Top-N Recommendation. In Proceedings of the 22nd IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT ’23). pp. 88-94. DOI 10.1109/WI-IAT59888.2023.00018. arXiv:2309.11723 [cs.IR]. NSF PAR 10487293. Acceptance rate: 28%.

TORS23
2024

Michael D. Ekstrand, Ben Carterette, and Fernando Diaz. 2024. Distributionally-Informed Recommender System Evaluation. Transactions on Recommender Systems 2(1) (March 7th, 2024), 6:1–27. DOI 10.1145/3613455. arXiv:2309.05892 [cs.IR]. NSF PAR 10461937. Cited 7 times. Cited 4 times.

SIGIR23-q
2023

Amifa Raj, Bhaskar Mitra, Michael D. Ekstrand, and Nick Craswell. 2023. Patterns of Gender-Specializing Query Reformulation. Short paper in Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’23). DOI 10.1145/3539618.3592034. arXiv:2304.13129. NSF PAR 10423689. Acceptance rate: 25.12%. Cited 1 time. Cited 1 time.

SIGIR23-i
2023

Ngozi Ihemelandu and Michael D. Ekstrand. 2023. Inference at Scale: Significance Testing for Large Search and Recommendation Experiments. Short paper in Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’23). DOI 10.1145/3539618.3592004. arXiv:2305.02461. NSF PAR 10423691. Acceptance rate: 25.12%. Cited 1 time.

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). DOI 10.1145/3576840.3578316. arXiv:2301.04780. NSF PAR 10423693. Acceptance rate: 39.4%. Cited 10 times. Cited 7 times.

FAccTRec22
2022

Michael D. Ekstrand and Maria Soledad Pera. 2022. Matching Consumer Fairness Objectives & Strategies for RecSys. Presented at the 5th FAccTrec Workshop on Responsible Recommendation (peer-reviewed but not archived). arXiv:2209.02662 [cs.IR]. Cited 1 time. Cited 2 times.

SIGIRec22
2022

Amifa Raj and Michael D. Ekstrand. 2022. Fire Dragon and Unicorn Princess: Gender Stereotypes and Children’s Products in Search Engine Responses. In SIGIR eCom ’22. DOI 10.48550/arXiv.2206.13747. arXiv:2206.13747 [cs.IR]. Cited 5 times. Cited 3 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. DOI 10.1145/3477495.3532018. NSF PAR 10329880. Acceptance rate: 20%. Cited 35 times. Cited 26 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. DOI 10.1561/1500000079. arXiv:2105.05779 [cs.IR]. NSF PAR 10347630. Impact factor: 8. Cited 126 times. Cited 67 times.

AIMAG22
2022

Nasim Sonboli, Robin Burke, Michael Ekstrand, and Rishabh Mehrotra. 2022. The Multisided Complexity of Fairness in Recommender Systems. AI Magazine 43(2) (June 23rd, 2022), 164–176. DOI 10.1002/aaai.12054. NSF PAR 10334796. Cited 22 times. Cited 13 times.

RSHB3E
2022

Michael D. Ekstrand, Anubrata Das, Robin Burke, and Fernando Diaz. 2022. Fairness in Recommender Systems. In Recommender Systems Handbook (3rd edition). Francesco Ricci, Lior Roach, and Bracha Shapira, eds. Springer-Verlag. DOI 10.1007/978-1-0716-2197-4_18. ISBN 978-1-0716-2196-7. Cited 27 times. Cited 15 times.

RSPE21-dist
2021

Michael D. Ekstrand, Ben Carterette, and Fernando Diaz. 2021. Evaluating Recommenders with Distributions. In Proceedings of the RecSys 2021 Workshop on Perspectives on the Evaluation of Recommender Systems (RecSys ’21). Cited 2 times.

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. In RecSys 2021 Late-Breaking Results (RecSys ’21). DOI 10.1145/3460231.3478856. NSF PAR 10316668. Cited 4 times. Cited 3 times.

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. NSF PAR 10335669. Cited 6 times. Cited 4 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%. Cited 36 times. Cited 32 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. DOI 10.1007/s11257-020-09284-2. arXiv:1808.07586v2. NSF PAR 10218853. Impact factor: 4.412. Cited 167 times. Cited 86 times (shared with RecSys18◊).

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 73 times. Cited 53 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 156 times. Cited 137 times.

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 33 times. Cited 25 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, pp. 5. DOI 10.1145/3343413.3378004. arXiv:2001.09455 [cs.IR]. NSF PAR 10146883. Acceptance rate: 47%. Cited 11 times. Cited 9 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). pp. 2. DOI 10.1145/3298689.3347048. NSF PAR 10133610. Cited 12 times. Cited 8 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. Cited 1 time. Cited 1 time.

Recommendation Tutorial

We have given a tutorial on fairness in IR & recommendation in multiple settings.

⸘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). pp. 2. DOI 10.1145/3298689.3346964. Cited 44 times. Cited 37 times.

⸘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). pp. 2. DOI 10.1145/3331184.3331380. Cited 47 times. Cited 36 times.

TREC Track

I am one of the organizers of the TREC Fairness Track; my participation in this is funded by the grant.

FACTS-IR Workshop

I co-organized the Workshop on Fairness, Accountability, Confidentiality, Transparency, and Safety in Information Retrieval.

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 12th, 2019), 20–43. DOI 10.1145/3458553.3458556. Cited 40 times. Cited 20 times.

⸘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 6 times.

Preliminary Work

These papers were written prior to the project period, and establish preliminary results that helped secure the grant.

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%. Citations reported under UMUAI21◊. Citations reported under UMUAI21◊.

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 15 times. Cited 10 times.

Educational Outcomes

I have three planned educational activities as a part of this project:

  1. Work with Don Winiecki and Boise State CS faculty to incorporate material on ethics and the social impact of technology into graduate artificial intelligence and data science classes.
  2. Collaborate with Eric Lindquist from the Boise State School of Public Service to develop and teach Big Data in Public Life, an interdisciplinary undergraduate course on the interaction of big data, ethics, and policy as data-driven algorithmic systems are increasingly deployed in our society in both public and private sectors.
  3. Develop training materials and teach workshops for librarians across Idaho on recommender systems and related technology, so they can make better use of it in working with their communities and provide their patrons with guidance in engaging with recommenders. Meridian Library District will be working with me on the pilot of these workshops.

Library Training

See the Library Training page for details on library training, and on scheduling a training for your library.

I have given the following:

  • Presentation at the Idaho Library Association 2019 Conference

Credits

National Science Foundation logo

This material is based upon work supported by the National Science Foundation under Grant No. IIS 17-51278. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.