Publications

This page lists my research publications, organized by type and date. See my research page for a topical view of my research.

I also have research profiles elsewhere:

Citation counts from Microsoft Academic. With this data, my h-index is 12 and my i10-index is 13. Google Scholar records somewhat higher citation counts which can affect these metrics.

Journal Papers

Michael D. Ekstrand and Michael Ludwig. 2016. Dependency Injection with Static Analysis and Context-Aware Policy. Journal of Object Technology 15, 1 (February 2016), pp 1:1–31. DOI:10.5381/jot.2016.15.5.a1.

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, Article 10 (April 2015), 23 pages. DOI:10.1145/2728171. Cited 12 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 6 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), pp 81–173. DOI:10.1561/1100000009. Cited 308 times (634 est.).

Refereed Conference Papers

These are full papers which have been published in peer-reviewed conference proceedings.

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 Conference on Fairness, Accountability and Transparency. PMLR 81:172–186. Acceptance rate: 24%.

Michael D. Ekstrand, Rezvan Joshaghani, and Hoda Mehrpouyan. 2018. Privacy for All: Ensuring Fair and Equitable Privacy Protections. In Proceedings of the Conference on Fairness, Accountability and Transparency. PMLR 81:35–47. Acceptance rate: 24%.

Michael D. Ekstrand and Vaibhav Mahant. 2017. Sturgeon and the Cool Kids: Problems with Top-N Recommender Evaluation. In Proceedings of the 30^th International Florida Artificial Intelligence Research Society Conference.

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 Ninth ACM Conference on Recommender Systems (RecSys ’15). ACM. DOI:10.1145/2792838.2800195. Acceptance rate: 21%. Cited 16 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 Eighth ACM Conference on Recommender Systems (RecSys ’14). ACM. DOI:10.1145/2645710.2645737. Acceptance rate: 23%. Cited 34 times (70 est.).

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 (ACM L@S ’14). ACM. DOI:10.1145/2556325.2566244. Acceptance rate: 37%. Cited 19 times.

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 Seventh ACM Conference on Recommender Systems (RecSys ’13). ACM. DOI:10.1145/2507157.2507188. Acceptance rate: 24%. Cited 20 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. DOI:10.1145/2365952.2365974. Acceptance rate: 20%. Cited 13 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, 86–96. DOI:10.1145/2247596.2247608. Acceptance rate: 23%. Cited 9 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, 133–140. DOI:10.1145/2043932.2043958. Acceptance rate: 27% (20% for oral presentation, which this received). Cited 72 times (134 est.).

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, 195–204. DOI:10.1145/2047196.2047220. Acceptance rate: 25%. Cited 21 times.

Michael D. Ekstrand, Praveen Kannan, James A. Stemper, John T. Butler, Joseph A. Konstan, and John T. Riedl. 2010. Automatically Building Research Reading Lists. In Proceedings of the Fourth ACM Conference on Recommender Systems (RecSys ’10). ACM, 159–166. DOI:10.1145/1864708.1864740. Acceptance rate: 19%. Cited 47 times (68 est.).

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. DOI:10.1145/1641309.1641317. Acceptance rate: 36% (Selected as Best Paper). Cited 24 times.

Short Papers

These are short research papers published in peer-reviewed conference proceedings.

Sushma Channamsetty and Michael D. Ekstrand. 2017. Recommender Response to Diversity and Popularity Bias in User Profiles. Short paper in Proceedings of the 30^th International Florida Artificial Intelligence Research Society Conference.

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, 233–236. DOI:10.1145/2365952.2366002. Acceptance rate: 32%. Cited 19 times.

Workshops, Seminars, Posters, Etc.

These have undergone some form of peer review, but are not fully-reviewed scientific publications.

Rezvan Joshaghani, Michael D. Ekstrand, Bart Knijnenburg, and Hoda Mehrpouyan. 2018. Do Different Groups Have Comparable Privacy Tradeoffs?. To appear in Proceedings of the CHI 2018 Workshop on Moving Beyond a ‘One-Size Fits All’ Approach: Exploring Individual Differences in Privacy.

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.

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

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). ACM. DOI:10.1145/2959100.2959179. Acceptance rate: 36% (Past, Present, and Future track). Cited 6 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.

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

Other Publications

Michael D. Ekstrand and Amit Sharma. 2017. The FATREC Workshop on Responsible Recommendation. In Proceedings of the 11th ACM Conference on Recommender Systems.

Michael D. Ekstrand. 2014. Towards Recommender Engineering: Tools and Experiments in Recommender Differences. Ph.D Thesis, University of Minnesota. Cited 1 times.

Martijn Willemsen, Dirk Bollen, and Michael Ekstrand. 2011. UCERSTI 2: Second Workshop on User-Centric Evaluation of Recommender Systems and Their Interfaces. Workshop at the Fifth ACM Conference on Recommender Systems (RecSys ’11). ACM, 395–396. DOI:10.1145/2043932.2044020. Cited 3 times.

Michael D. Ekstrand, Michael Ludwig, Jack Kolb, and John T. Riedl. 2011. LensKit: a modular recommender framework. Demo presented at the Fifth ACM Conference on Recommender Systems (RecSys ’11). ACM, 349–350. DOI:10.1145/2043932.2044001. Cited 12 times.