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Michael Ekstrand : Curriculum Vitae

Overview

I research ways to help people find, filter, track, remember, and make better use of information. Each day, the modern Internet user engages with countless pieces of information. They likely have personally stored additional data and there is much more available with a quick trip to Google. I want to build tools to help users find and keep their way in this sea.

You can read more about my research interests and activity on my research page.

Publications

Ekstrand, Michael D. 2014a. “Building Open-Source Tools for Reproducible Research and Education.” In Proceedings of the Workshop on Sharing, Re-Use and Circulation of Resources in Cooperative Scientific Work at ACM CSCW ’14. http://md.ekstrandom.net/research/pubs/oss-tools/.
———. 2014b. “Towards Recommender Engineering: Tools and Experiments in Recommender Differences.” Ph.D Thesis, Minneapolis, MN: University of Minnesota. http://hdl.handle.net/11299/165307.
Ekstrand, Michael D., 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), 161–68. ACM. doi:10.1145/2645710.2645737.
Ekstrand, Michael D., 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, 11–18. RecSys ’15. New York, NY, USA: ACM. doi:10.1145/2792838.2800195.
Ekstrand, Michael D., and Michael Ludwig. 2016. “Dependency Injection with Static Analysis and Context-Aware Policy.” The Journal of Object Technology 15 (1): 1:1–31. doi:10.5381/jot.2016.15.1.a1.
Ekstrand, Michael D., Michael Ludwig, Jack Kolb, and John T. Riedl. 2011. “LensKit: A Modular Recommender Framework.” In Proceedings of the Fifth ACM Conference on Recommender Systems, 349–50. RecSys ’11. New York, NY, USA: ACM. doi:10.1145/2043932.2044001.
Ekstrand, Michael D., 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), 133–40. ACM. doi:10.1145/2043932.2043958.
Ekstrand, Michael D., and Vaibhav Mahant. 2017. “Sturgeon and the Cool Kids: Problems with Top-N Recommender Evaluation.” In Proceedings of the 30th Florida Artificial Intelligence Research Society Conference. AAAI Press. https://md.ekstrandom.net/research/pubs/sturgeon/.
Ekstrand, Michael D., 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, 1. Orlando, Florida. doi:10.1145/1641309.1641317.
———. 2012. “When Recommenders Fail: Predicting Recommender Failure for Algorithm Selection and Combination.” In Proceedings of the Sixth ACM Conference on Recommender Systems, 233–36. RecSys ’12. New York, NY, USA: ACM. doi:10.1145/2365952.2366002.
Ekstrand, Michael D., and Martijn C. Willemsen. 2016. “Behaviorism Is Not Enough: Better Recommendations Through Listening to Users.” In Proceedings of the 10th ACM Conference on Recommender Systems, 221–24. RecSys ’16. New York, NY, USA: ACM. doi:10.1145/2959100.2959179.
Ekstrand, Michael, Praveen Kannan, James Stemper, John Butler, Joseph A. Konstan, and John Riedl. 2010. “Automatically Building Research Reading Lists.” In RecSys ’10, 159–66. ACM. doi:10.1145/1864708.1864740.
Ekstrand, Michael, 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), 195–204. UIST ’11. ACM. doi:10.1145/2047196.2047220.
Ekstrand, Michael, John Riedl, and Joseph A. Konstan. 2010. “Collaborative Filtering Recommender Systems.” Foundations and Trends® in Human-Computer Interaction 4 (2): 81–173. doi:10.1561/1100000009.
Kluver, Daniel, 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, 99–106. RecSys ’12. New York, NY, USA: ACM. doi:10.1145/2365952.2365974.
Konstan, Joseph A., 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.” ACM Transactions on Computer-Human Interaction 22 (2): 10:1–10:23. doi:10.1145/2728171.
Konstan, Joseph A., 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 Conference, 61–70. L@S ’14. New York, NY, USA: ACM. doi:10.1145/2556325.2566244.
Levandoski, Justin J., Michael D. Ekstrand, Michael Ludwig, Ahmed Eldawy, Mohamed F. Mokbel, and John Riedl. 2011. “RecBench: Benchmarks for Evaluating Performance of Recommender System Architectures.” PVLDB 4 (11): 911–20. http://dblp.uni-trier.de/rec/bibtex/journals/pvldb/LevandoskiELEMR11.
Levandoski, Justin J., 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, 86–96. EDBT ’12. New York, NY, USA: ACM. doi:10.1145/2247596.2247608.
Nguyen, Tien T., 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, 149–56. RecSys ’13. New York, NY, USA: ACM. doi:10.1145/2507157.2507188.

Student Theses

Channamsetty, Sushma. 2016. “Recommender Response to User Profile Diversity and Popularity Bias.” M.S. Thesis. https://digital.library.txstate.edu/handle/10877/6313.
Kazi, Mohammed Imran Rukmoddin. 2016. “Exploring Potentially Discriminatory Biases In Book Recommender System.” M.S. Thesis. https://digital.library.txstate.edu/handle/10877/6306.
Mahant, Vaibhav. 2016. “Improving Top-N Evaluation of Recommender Systems.” M.S. Thesis, San Marcos, TX: Texas State University.
Saha, Shuvabrata. 2016. “A Multi-Objective Autotuning Framework For The Java Virtual Machine.” M.S. Thesis. https://digital.library.txstate.edu/handle/10877/6096.