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 10 times. Cited 10 times.

Daniel Kluver, Michael D. Ekstrand, and Joseph A. Konstan. 2018. Rating-Based Collaborative Filtering: Algorithms and Evaluation. In Social Information Access. Peter Brusilovsky and Daqing He, eds. Springer-Verlag, Lecture Notes in Computer Science vol. 10100, pp. 344–390. DOI 10.1007/978-3-319-90092-6_10. ISBN 978-3-319-90091-9. Cited 93 times. Cited 120 times.

Michael D. Ekstrand, Ben Carterette, and Fernando Diaz. 2023. Distributionally-Informed Recommender System Evaluation. Transactions on Recommender Systems (August 2023). TORS (August 2023). DOI 10.1145/3613455. arXiv:2309.05892. NSF PAR 10461937.

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 2022), 1–177. FnT IR 16(1–2) (July 2022). DOI 10.1561/1500000079. arXiv:2105.05779. NSF PAR 10347630. Impact factor: 8. Cited 47 times. Cited 72 times.

Michael D. Ekstrand and Daniel Kluver. 2021. Exploring Author Gender in Book Rating and Recommendation. User Modeling and User-Adapted Interaction 31(3) (February 2021), 377–420. UMUAI 31(3) (February 2021). DOI 10.1007/s11257-020-09284-2. NSF PAR 10218853. Impact factor: 4.412. Cited 135^* times.

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 2020), 725–744. AJIM 72(5) (June 2020). DOI 10.1108/AJIM-11-2019-0309. Impact factor: 1.903. Cited 9 times. Cited 12 times.

Michael D. Ekstrand and Michael Ludwig. 2016. Dependency Injection with Static Analysis and Context-Aware Policy. Journal of Object Technology 15(1) (February 2016), 1:1–31. DOI 10.5381/jot.2016.15.1.a1. Cited 14 times.

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 2015). DOI 10.1145/2728171. Impact factor: 1.293. Cited 24 times. Cited 106^* times.

Justin J. Levandoski, Michael D. Ekstrand, Michael J. Ludwig, Ahmad Eldawy, Mohamed F. Mokbel, and John T. Riedl. 2011. RecBench: Benchmarks for Evaluating Performance of Recommender System Architectures. Proceedings of the VLDB Endowment 4(11) (August 2011), 911–920. Acceptance rate: 18%. Cited 9 times. Cited 21 times.

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 2011), 81–173. FnT HCI 4(2) (February 2011). DOI 10.1561/1100000009. Cited 632 times. Cited 1530 times.

Ngozi Ihemelandu and Michael D. Ekstrand. 2023. Candidate Set Sampling for Evaluating Top-N Recommendation. To appear in Proceedings of the 22nd IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT ’23). Proc. WI-IAT ’23. Acceptance rate: 28%.

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). Proc. SIGIR ’23. DOI 10.1145/3539618.3592004. arXiv:2305.02461. NSF PAR 10423691.

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). Proc. SIGIR ’23. DOI 10.1145/3539618.3592034. arXiv:2304.13129. NSF PAR 10423689.

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 1 time.

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 17 times. Cited 15 times.

A. K. M. Nuhil Mehdy, Michael D. Ekstrand, Bart Knijnenburg, and Hoda Mehrpouyan. 2021. Privacy as a Planned Behavior: Effects of Situational Factors on Privacy Perceptions and Plans. In Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization (UMAP ’21). ACM. Proc. UMAP ’21. DOI 10.1145/3450613.3456829. arXiv:2104.11847. NSF PAR 10223377. Acceptance rate: 23%. Cited 8 times. Cited 9 times.

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 26 times. Cited 28 times.

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. NSF PAR 10199450. No acceptance rate reported. Cited 45 times. Cited 63^* times.

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. NSF PAR 10199451. Acceptance rate: 20%. Nominated for Best Long Paper. Cited 121 times. Cited 115 times.

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, 5 pp. Proc. CHIIR ’20. DOI 10.1145/3343413.3378004. arXiv:2001.09455. NSF PAR 10146883. Acceptance rate: 47%. Cited 8 times. Cited 7 times.

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. Proc. RecSys ’18. DOI 10.1145/3240323.3240373. arXiv:1808.07586v1. Acceptance rate: 17.5%. Citations reported under UMUAI21. Citations reported under UMUAI21.

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 170 times. Cited 190 times.

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 66 times. Cited 74 times.

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. (Recommender Systems track). No acceptance rate reported. Cited 9 times. Cited 13 times.

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. (Recommender Systems track). No acceptance rate reported. Cited 15 times. Cited 15 times.

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 96 times. Cited 108 times.

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 167 times. Cited 234 times.

Joseph A. Konstan, J.D. Walker, D. Christopher Brooks, Keith Brown, and Michael D. Ekstrand. 2014. Teaching Recommender Systems at Large Scale: Evaluation and Lessons Learned from a Hybrid MOOC. In Proceedings of the First ACM Conference on Learning @ Scale (S ’14). ACM. Proc. L@S ’14. DOI 10.1145/2556325.2566244. Acceptance rate: 37%. Cited 68 times. Citations reported under TOCHI15^*.

Tien T. Nguyen, Daniel Kluver, Ting-Yu Wang, Pik-Mai Hui, Michael D. Ekstrand, Martijn C. Willemsen, and John Riedl. 2013. Rating Support Interfaces to Improve User Experience and Recommender Accuracy. In Proceedings of the 7th ACM Conference on Recommender Systems (RecSys ’13). ACM. Proc. RecSys ’13. DOI 10.1145/2507157.2507188. Acceptance rate: 24%. Cited 42 times. Cited 56 times.

Daniel Kluver, Tien T. Nguyen, Michael Ekstrand, Shilad Sen, and John Riedl. 2012. How Many Bits per Rating?. In Proceedings of the Sixth ACM Conference on Recommender Systems (RecSys ’12). ACM, pp. 99–106. Proc. RecSys ’12. DOI 10.1145/2365952.2365974. Acceptance rate: 20%. Cited 40 times. Cited 41 times.

Michael Ekstrand and John Riedl. 2012. When Recommenders Fail: Predicting Recommender Failure for Algorithm Selection and Combination. Short paper in Proceedings of the Sixth ACM Conference on Recommender Systems (RecSys ’12). ACM, pp. 233–236. Proc. RecSys ’12. DOI 10.1145/2365952.2366002. Acceptance rate: 32%. Cited 68 times. Cited 76 times.

Justin J. Levandoski, Mohamed Sarwat, Mohamed F. Mokbel, and Michael D. Ekstrand. 2012. RecStore: An Extensible And Adaptive Framework for Online Recommender Queries Inside the Database Engine. In Proceedings of the 15th International Conference on Extending Database Technology (EDBT ’12). ACM, pp. 86–96. Proc. EDBT ’12. DOI 10.1145/2247596.2247608. Acceptance rate: 23%. Cited 15 times. Cited 18 times.

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 191 times. Cited 226 times.

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 49 times. Cited 53 times.

Michael D. Ekstrand, Praveen Kannan, James A. Stempter, John T. Butler, Joseph A. Konstan, and John T. Riedl. 2010. Automatically Building Research Reading Lists. In Proceedings of the 4th ACM Conference on Recommender Systems (RecSys ’10). ACM, pp. 159–166. Proc. RecSys ’10. DOI 10.1145/1864708.1864740. Acceptance rate: 19%. Cited 101 times. Cited 118 times.

Michael D. Ekstrand and John T. Riedl. 2009. rv you’re dumb: Identifying Discarded Work in Wiki Article History. In Proceedings of the 5th International Symposium on Wikis and Open Collaboration (WikiSym ’09). ACM, 10 pp. Proc. WikiSym ’09. DOI 10.1145/1641309.1641317. Acceptance rate: 36%. Selected as Best Paper. Cited 30 times. Cited 34 times.

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). DOI 10.48550/arXiv.2309.10271. arXiv:2309.10271.

Michael D. Ekstrand, Maria Soledad Pera, and Katherine Landau Wright. 2023. Seeking Information with a ‘More Knowledgeable Other’. ACM Interactions 30(1) (January 2023), 70–73. DOI 10.1145/3573364. Cited 1 time. Cited 1 time.

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). DOI 10.48550/arXiv.2209.02662. arXiv:2209.02662.

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. Cited 2 times. Cited 2 times.

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). Proc. PERSPECTIVES @ RecSys ’21. DOI 10.48550/arXiv.2109.06424. arXiv:2109.06424. Cited 5 times. Cited 3 times.

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). Proc. RecSys ’21 LBR. DOI 10.1145/3460231.3478856. NSF PAR 10316668. Cited 3 times. Cited 3 times.

Nasim Sonboli, Robin Burke, Michael Ekstrand, and Rishabh Mehrotra. 2022. The Multisided Complexity of Fairness in Recommender Systems. AI Magazine 43(2) (June 2022), 164–176. DOI 10.1002/aaai.12054. NSF PAR 10334796. Cited 8 times. Cited 6 times.

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. Proc. KidRec ’21. DOI 10.48550/arXiv.2105.09296. arXiv:2105.09296. NSF PAR 10335669. Cited 4 times. Cited 4 times.

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). DOI 10.48550/arXiv.2009.01311. arXiv:2009.01311. Cited 20 times. Cited 21 times.

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). 2 pp. Proc. RecSys ’19. DOI 10.1145/3298689.3347048. NSF PAR 10133610. Cited 8 times. Cited 12 times.

Michael D. Ekstrand, Ion Madrazo Azpiazu, Katherine Landau Wright, and Maria Soledad Pera. 2018. Retrieving and Recommending for the Classroom: Stakeholders, Objectives, Resources, and Users. In Proceedings of the ComplexRec 2018 Second Workshop on Recommendation in Complex Scenarios (ComplexRec ’18), at RecSys 2018. Proc. ComplexRec ’18. Cited 4 times. Cited 7 times.

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.

Michael D. Ekstrand. 2018. The LKPY Package for Recommender Systems Experiments: Next-Generation Tools and Lessons Learned from the LensKit Project. Computer Science Faculty Publications and Presentations 147. 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/147/boisestate. arXiv:1809.03125v1. Cited 18 times. Citations reported under CIKM20-lk^*.

Maria Soledad Pera, Katherine Wright, and Michael D. Ekstrand. 2018. Recommending Texts to Children with an Expert in the Loop. In Proceedings of the 2nd International Workshop on Children & Recommender Systems (KidRec ’18), at IDC 2018. Proc. KidRec ’18. DOI 10.18122/cs_facpubs/140/boisestate. Cited 7 times. Cited 6 times.

Rezvan Joshaghani, Michael D. Ekstrand, Bart Knijnenburg, and Hoda Mehrpouyan. 2018. Do Different Groups Have Comparable Privacy Tradeoffs?. At Moving Beyond a ‘One-Size Fits All’ Approach: Exploring Individual Differences in Privacy, a workshop at CHI 2018. NSF PAR 10222636. Cited 2 times. Cited 2 times.

Michael D. Ekstrand and Maria Soledad Pera. 2017. The Demographics of Cool: Popularity and Recommender Performance for Different Groups of Users. In RecSys 2017 Poster Proceedings. CEUR, Workshop Proceedings 1905. Cited 5 times. Cited 15 times.

Michael D. Ekstrand. 2017. Challenges in Evaluating Recommendations for Children. In Proceedings of the International Workshop on Children & Recommender Systems (KidRec), at RecSys 2017. Proc. KidRec. Cited 8 times.

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 81 times. Cited 100 times.

Jennifer D. Ekstrand and Michael D. Ekstrand. 2016. First Do No Harm: Considering and Minimizing Harm in Recommender Systems Designed for Engendering Health. In Proceedings of the Workshop on Recommender Systems for Health at RecSys ’16. Cited 10 times. Cited 11 times.

Michael D. Ekstrand. 2014. Building Open-Source Tools for Reproducible Research and Education. At Sharing, Re-use, and Circulation of Resources in Cooperative Scientific Work, a workshop at CSCW 2014.

Michael D. Ekstrand, Michael Ludwig, Jack Kolb, and John T. Riedl. 2011. LensKit: A Modular Recommender Framework. Demo recorded in Proceedings of the 5th ACM Conference on Recommender Systems (RecSys ’11). ACM, pp. 349-350. Proc. RecSys ’11. DOI 10.1145/2043932.2044001. Cited 1 time. Cited 43 times.

Jonathan Stray, Alon Halevy, Parisa Assar, Dylan Hadfield-Menell, Craig Boutilier, Amar Ashar, Lex Beattie, Michael Ekstrand, Claire Leibowicz, Connie Moon Sehat, Sara Johansen, Lianne Kerlin, David Vickrey, Spandana Singh, Sanne Vrijenhoek, Amy Zhang, Mckane Andrus, Natali Helberger, Polina Proutskova, Tanushree Mitra, and Nina Vasan. 2022. Building Human Values into Recommender Systems: An Interdisciplinary Synthesis. DOI 10.48550/arXiv.2207.10192. arXiv:2207.10192. Cited 14 times. Cited 11 times.

Michael D. Ekstrand, Graham McDonald, Amifa Raj, and Isaac Johnson. 2022. Overview of the TREC 2021 Fair Ranking Track. In The Thirtieth Text REtrieval Conference (TREC 2021) Proceedings (TREC 2021). Proc. TREC 2021. https://trec.nist.gov/pubs/trec30/papers/Overview-F.pdf. Cited 11 times.

Michael D. Ekstrand. 2021. Multiversal Simulacra: Understanding Hypotheticals and Possible Worlds Through Simulation. DOI 10.48550/arXiv.2110.00811. arXiv:2110.00811. Cited 2 times. Cited 1 time.

Michael D. Ekstrand, Ben Carterette, and Fernando Diaz. 2021. Evaluating Recommenders with Distributions. At Proceedings of the RecSys 2021 Workshop on Perspectives on the Evaluation of Recommender Systems (RecSys ’21). Proc. PERSPECTIVES @ RecSys ’21.

Michael D. Ekstrand, Allison Chaney, Pablo Castells, Robin Burke, David Rohde, and Manel Slokom. 2021. SimuRec: Workshop on Synthetic Data and Simulation Methods for Recommender Systems Research. In Proceedings of the 15th ACM Conference on Recommender Systems (RecSys ’21). ACM. Proc. RecSys ’21. DOI 10.1145/3460231.3470938. Cited 12 times. Cited 12 times.

Michael D. Ekstrand, Pierre-Nicolas Schwab, Toshihiro Kamishima, and Nasim Sonboli. 2021. FAccTRec 2021: The 4th Workshop on Responsible Recommendation. In Proceedings of the 15th ACM Conference on Recommender Systems (RecSys ’21). ACM. Proc. RecSys ’21. DOI 10.1145/3460231.3470932. Cited 1 time.

Asia J. Biega, Fernando Diaz, Michael D. Ekstrand, Sergey Feldman, and Sebastian Kohlmeier. 2021. Overview of the TREC 2020 Fair Ranking Track. In The Twenty-Ninth Text REtrieval Conference (TREC 2020) Proceedings (TREC 2020). Proc. TREC 2020. DOI 10.48550/arXiv.2108.05135. arXiv:2108.05135. Cited 3 times. Cited 8 times.

Robin Burke, Michael D. Ekstrand, Nava Tintarev, and Julita Vassileva. 2021. Preface to the Special Issue on Fair, Accountable, and Transparent Recommender Systems. User Modeling and User-Adapted Interaction 31(3) (July 2021), 371–375. DOI 10.1007/s11257-021-09297-5. Cited 4 times. Cited 4 times.

Michael D. Ekstrand, Pierre-Nicolas Schwab, Jean Garcia-Gathright, Toshihiro Kamishima, and Nasim Sonboli. 2020. 3rd FATREC Workshop: Responsible Recommendation. In Proceedings of the 14th ACM Conference on Recommender Systems (RecSys ’20). ACM. Proc. RecSys ’20. DOI 10.1145/3383313.3411538. Cited 5 times. Cited 4 times.

Bamshad Mobasher, Stylani Kleanthous, Michael D. Ekstrand, Bettina Berendt, Janna Otterbacher, and Avital Schulner Tal. 2020. FairUMAP 2020: The 3rd Workshop on Fairness in User Modeling, Adaptation and Personalization. In Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization (UMAP ’20). ACM. Proc. UMAP ’20. DOI 10.1145/3340631.3398671. Cited 2 times. Cited 2 times.

Asia J. Biega, Fernando Diaz, Michael D. Ekstrand, and Sebastian Kohlmeier. 2020. Overview of the TREC 2019 Fair Ranking Track. In The Twenty-Eighth Text REtrieval Conference (TREC 2019) Proceedings (TREC 2019). Proc. TREC 2019. DOI 10.48550/arXiv.2003.11650. arXiv:2003.11650. Cited 31 times.

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 2019), 20–43. DOI 10.1145/3458553.3458556. Cited 6 times. Cited 32 times.

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). 2 pp. Proc. RecSys ’19. DOI 10.1145/3298689.3346964. Cited 36 times. Cited 37 times.

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). 2 pp. Proc. SIGIR ’19. DOI 10.1145/3331184.3331380. Cited 33 times. Cited 40 times.

Bettina Berendt, Veronika Bogina, Robin Burke, Michael D. Ekstrand, Alan Hartman, Stylani Kleanthous, Tsvi Kuflik, Bamshad Mobasher, and Janna Otterbacher. 2019. FairUMAP 2019 Chairs’ Welcome Overview. In Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization (UMAP ’19). ACM. Proc. UMAP ’19. DOI 10.1145/3314183.3323842. Cited 2 times.

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. Proc. SIGIR ’19. DOI 10.1145/3331184.3331644. Cited 7 times. Cited 32 times.

Katherine Landau Wright, David McNeill, Michael D. Ekstrand, and Maria Soledad Pera. 2019. Supplementing Classroom Texts with Online Resources. At 2019 American Educational Research Association Conference.

Michael D. Ekstrand and Joseph A. Konstan. 2019. Recommender Systems Notation: Proposed Common Notation for Teaching and Research. Computer Science Faculty Publications and Presentations 177. Boise State University. DOI 10.18122/cs_facpubs/177/boisestate. arXiv:1902.01348. Cited 4 times. Cited 9 times.

Nicola Ferro, Norbert Fuhr, Gregory Grefenstette, Joseph A. Konstan, Pablo Castells, Elizabeth M. Daly, Thierry Declerck, Michael D. Ekstrand, Werner Geyer, Julio Gonzalo, Tsvi Kuflik, Krister Lindén, Bernardo Magnini, Jian-Yun Nie, Raffaele Perego, Bracha Shapira, Ian Soboroff, Nava Tintarev, Karin Verspoor, Martijn C. Willemsen, and Justin Zobel. 2018. From Evaluating to Forecasting Performance: How to Turn Information Retrieval, Natural Language Processing and Recommender Systems into Predictive Sciences (Dagstuhl Perspectives Workshop 17442). Dagstuhl Manifestos 7(1) (November 2018), 96–139. DOI 10.4230/DagMan.7.1.96. Cited 15 times. Cited 18 times.

Katherine Landau Wright, Michael D. Ekstrand, and Maria Soledad Pera. 2018. Supplementing Classroom Texts with Online Resources. At 2018 Annual Meeting of the Northwest Rocky Mountain Educational Research Association.

Toshihiro Kamishima, Pierre-Nicolas Schwab, and Michael D. Ekstrand. 2018. 2nd FATREC Workshop: Responsible Recommendation. In Proceedings of the 12th ACM Conference on Recommender Systems (RecSys ’18). ACM. Proc. RecSys ’18. DOI 10.1145/3240323.3240335. Cited 9 times. Cited 10 times.

Bamshad Mobasher, Robin Burke, Michael D. Ekstrand, and Bettina Berendt. 2018. UMAP 2018 Fairness in User Modeling, Adaptation and Personalization (FairUMAP 2018) Chairs’ Welcome & Organization. In Adjunct Publication of the 26th Conference on User Modeling, Adaptation, and Personalization (UMAP ’18). ACM. Proc. UMAP ’18. DOI 10.1145/3213586.3226200.

Nicola Ferro, Norbert Fuhr, Gregory Grefenstette, Joseph A. Konstan, Pablo Castells, Elizabeth M. Daly, Thierry Declerck, Michael D. Ekstrand, Werner Geyer, Julio Gonzalo, Tsvi Kuflik, Krister Lindén, Bernardo Magnini, Jian-Yun Nie, Raffaele Perego, Bracha Shapira, Ian Soboroff, Nava Tintarev, Karin Verspoor, Martijn C. Willemsen, and Justin Zobel. 2018. The Dagstuhl Perspectives Workshop on Performance Modeling and Prediction. SIGIR Forum 52(1) (June 2018), 91–101. DOI 10.1145/3274784.3274789. Cited 18 times. Cited 15 times.

Michael D. Ekstrand. 2017. Yak Shaving with Michael Ekstrand. CSR Tales no. 4 (December 2017). PURL https://purl.org/mde/alpaca.

Michael D. Ekstrand and Amit Sharma. 2017. The FATREC Workshop on Responsible Recommendation. In Proceedings of the 11th ACM Conference on Recommender Systems (RecSys ’17). ACM. Proc. RecSys ’17. DOI 10.1145/3109859.3109960. Cited 12 times.

Michael D. Ekstrand. 2014. Towards Recommender Engineering: Tools and Experiments in Recommender Differences.

Ph.D thesis, University of Minnesota.

Martijn Willemsen, Dirk Bollen, and Michael Ekstrand. 2011. UCERSTI 2: Second Workshop on User-Centric Evaluation of Recommender Systems and Their Interfaces. In Proceedings of the 5th ACM Conference on Recommender Systems (RecSys ’11). ACM, pp. 395–396. Proc. RecSys ’11. DOI 10.1145/2043932.2044020. Cited 8 times. Cited 7 times.