Students

One of the great parts of my job is working with students on research and software development. This page collects information for and about those students.

If you are interested in doing research with me, particularly as one of my advisees, see my information for prospective students for details and current openings.

Current Ph.D Students

Be my next Ph.D student!

Past Ph.D Students

Ngozi Ihemelandu (Ph.D 2024, Boise State)
Dissertation
Best Practices for Offline Evaluation for Top-N Recommendation: Candidate Set Sampling and Statistical Inference
Papers
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%.

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%.

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.

RSPE21-inf
2021

Ngozi Ihemelandu and Michael D. Ekstrand. 2021. Statistical Inference: The Missing Piece of RecSys Experiment Reliability Discourse. In Proceedings of the Perspectives on the Evaluation of Recommender Systems Workshop 2021 (RecSys ’21). arXiv:2109.06424 [cs.IR]. Cited 7 times. Cited 6 times.

Amifa Raj (Ph.D 2023, Boise State)
Dissertation
Fair Layouts in Information Access Systems: Provider-Side Group Fairness in Ranking Beyond Ranked Lists
Papers
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%.

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.

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.

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 12 times. Cited 8 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 38 times. Cited 29 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 7 times. Cited 5 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.

M.S. Students

Srabanti Guha (MS 2023, Boise State)
Project
Explaining Misallocated Exposure across Multiple Rankings
Carlos Segura Cerna (MS 2020, Boise State)
Project
Recommendation Server for LensKit
Mucun Tian (MS 2019, Boise State)
Thesis
Estimating Error and Bias of Offline Recommender System Evaluation Results
Papers
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.

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.

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 257 times. Cited 195 times.

Sushma Channamsetty (MS 2016, TXST)
Thesis
Recommender Response to User Profile Diversity and Popularity Bias
Papers
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 22 times. Cited 18 times.

Mohammed Imran R Kazi (MS 2016, TXST)
Thesis
Exploring Potentially Discriminatory Biases in Book Recommendation
Papers
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.

Vaibhav Mahant (MS 2016, TXST)
Thesis
Improving Top-N Evaluation of Recommender Systems
Papers
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.

Shuvabrata Saha (MS 2016, TXST)

Co-advised with Dr. Apan Qasem.

Thesis
A Multi-Objective Autotuning Framework For The Java Virtual Machine