Boise State University

Fall 2018

CS 410/510 (Databases)

Summer 2018

CS 310-HU (Databases Hatchery); 6 students

Spring 2018

CS 410/510 (Databases)

Fall 2017

CS 533 (Introduction to Data Science); 22 students

Spring 2017

CS 597 (Recommender Systems); 13 students

Fall 2016

CS 410/CS 510 (Databases); 28 students

Texas State University

Spring 2016

CS 3320 (Internet Software Development); 48 students

CS 5369Q/4379Q (Recommender Systems); 26 students

Fall 2015

CS 4332 (Introduction to Database Systems); 39 students

Spring 2015

CS 5369Q/4379Q (Recommender Systems); 28 students

CS 4350 (Unix Systems Programming); 32 students

Fall 2014

CS 4332 (Introduction to Database Systems); 50 students

In addition, I have supervised several independent study students.

Coursera

I co-created the Recommender Systems specialization on Coursera, along with its two previous single-class versions, with Joseph A. Konstan.

University of Minnesota

Fall 2013

CSCI 5980-1 (Introduction to Recommender Systems), co-taught with Joseph A. Konstan; also offered as a MOOC on Coursera.

Summer 2012

CSCI 1902 (Structure of Computer Programming II)

Spring 2011

CSCI 5125 (Collaborative and Social Computing), as teaching assistant

2007–2008

CSCI 1902 (Structure of Computer Programming II), as teaching assistant (3 terms)

M.S. Students Supervised

• Mucun Tian (M.S. expected 2019)
• Vaibhav Mahant (M.S. 2016, Texas State University; thesis: Improving Top-N Evaluation of Recommender Systems)
• Sushma Channamsetty (M.S. 2016, Texas State University; thesis: Recommender Response to User Profile Diversity and Popularity Bias)
• Mohammed Imran R Kazi (M.S. 2016, Texas State University; thesis: Exploring Potentially Discriminatory Biases in Book Recommendation)
• Shuvabrata Saha (M.S. 2016, Texas State University; co-advised with Dr. Apan Qasem; thesis: A Multi-objective Autotuning Framework For The Java Virtual Machine)

Book Chapters

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. ISBN 978-3-319-90091-9. DOI 10.1007/978-3-319-90092-6_10. Cited 7 times.

Journal Publications

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.2016.15.5.a1. Cited 1 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. Cited 13 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), 81–173. DOI 10.1561/1100000009. Cited 701 times.

Conference Publications

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

Michael D. Ekstrand, Mucun Tian, Mohammed R. Imran Kazi, Hoda Mehrpouyan, and Daniel Kluver. 2018. Exploring Author Gender in Book Rating and Recommendation. To appear in Proceedings of the 12th ACM Conference on Recommender Systems (RecSys ’18). ACM. DOI 10.1145/3240323.3240373. arXiv:1808.07586v1 [cs.IR]. Acceptance rate: 17.5%.

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%.

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 81:172–186. 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 30th International Florida Artificial Intelligence Research Society Conference. AAAI, pp. 639–644.

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. AAAI, pp. 657–660.

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. DOI 10.1145/2792838.2800195. Acceptance rate: 21%. Cited 29 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. DOI 10.1145/2645710.2645737. Acceptance rate: 23%. Cited 81 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 (L@S ’14). ACM. DOI 10.1145/2556325.2566244. Acceptance rate: 37%. Cited 23 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 7th ACM Conference on Recommender Systems (RecSys ’13). ACM. DOI 10.1145/2507157.2507188. Acceptance rate: 24%. Cited 24 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 17 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. DOI 10.1145/2365952.2366002. Acceptance rate: 32%. Cited 24 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. 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, pp. 133–140. DOI 10.1145/2043932.2043958. Acceptance rate: 27% (20% for oral presentation, which this received). Cited 151 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. DOI 10.1145/2047196.2047220. Acceptance rate: 25%. Cited 24 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. DOI 10.1145/1864708.1864740. Acceptance rate: 19%. Cited 72 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. DOI 10.1145/1641309.1641317. Acceptance rate: 36% (Selected as Best Paper). Cited 23 times.

Workshops, Seminars, Posters, Etc.

These papers have undergone some form of peer review, and are published in workshops, poster proceedings, and similar venues.

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. To appear in Proceedings of the ComplexRec 2018 Second Workshop on Recommendation in Complex Scenarios (ComplexRec ’18), at RecSys 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.

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:1809.03125 [cs.IR].

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. DOI 10.18122/cs_facpubs/140/boisestate.

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.

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.

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 7 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. At Sharing, Re-use, and Circulation of Resources in Cooperative Scientific Work, a workshop at CSCW 2014.

Other Publications and Presentations

NSF Grants

• 2018–2023: NSF award 1751278, $482,081: CAREER: User-Based Simulation Methods for Quantifying Sources of Error and Bias in Recommender Systems (PI)

Internal Grants

• 2017: $19K Boise State College of Education Empathy Grant LITERATE: Locating Informational Texts for Engaging Readers And Teaching Equitably (co-PI; with PI Katherine Wright & co-PI Sole Pera)
• 2014: $8K Texas State University Research Enhancement Program (competitive internal research grant) Temporal Analysis of Recommender Systems (PI)

Professional Service

• PR & Publicity Co-chair, 2nd Confernece on Fairness, Accountability, and Transparency (ACM FAT* 2018)
• General co-chair, ACM RecSys 2018
• Participant in Dagstuhl Perspectives Workshop Towards Cross-Domain Performance Modeling and Prediction: IR/RecSys/NLP
• Organizer, FATREC Workshop on Responsible Recommendation at RecSys 2017 & 2018
• Co-organizer, FairUMAP workshop at UMAP 2018
• Track co-chair, 2018 Conference on Fairness, Accountability, and Transparency Systems track
• Distinguished Reviewer, ACM Transactions on Interactive Intelligent Systems (2017–present)
• Steering committee member, ACM Conference on Recommender Systems, 2017–present
• Steering committee member, Conference on Fairness, Accountability, and Transparency, 2017–present
• Program committee, ACM RecSys (2014–2017) and poster session (2016)
• Program committee, FLAIRS Special Track on Recommender Systems (2015, 2016, 2017)
• Publicity co-chair, ACM RecSys 2016
• Program committee, ACM WWW Track on Behavior Analysis and Personalization (2016, 2017)
• External advisor, CrowdRec (EU Framework Programme collaborative research project, 2014–2016)
• Proceedings co-chair, ACM CHI 2012–2013
• Demos co-chair, ACM RecSys 2012
• Reviewer for numerous conferences and journals, including:
  • ACM conferences CHI (2015, 2013, 2016, 2017), CSCW (2015, 2017), IUI (2017), SAC Recommender Systems track (2013, 2017), UIST (2012, 2016, 2017), WikiSym (2012)
  • Workshops at ACM RecSys: IntRS (2016–2017), LSRS (2016), KidRec (2017)
  • FATML 2017
  • International Journal of Artificial Intelligence Tools (2016)
  • JMLR Open Source (2016)
  • IBM Journal of Research and Development (2016)
  • ICWSM 2012
  • ACM journals TIST, TOIS, TWEB, TKDD, and TIIS
  • IEEE journals TDSC, TKDE
  • Interacting with Computers
  • International Conference on Service-Oriented Computing (2016)
  • PLOS ONE (2016)
  • User Modeling and User-Adapted Interaction
  • Information Retrieval Journal
  • ACM Computing Surveys (2014, 2015)
  • User Modeling
  • Artificial Intelligence Review (Springer)
  • Hindawi journals Advances in Multimedia, Advances in Artificial Intelligence

Department and University Service

• Boise State Ph.D in Computing Steering Committee (Fall 2017)
• Boise State CS Dept. Curriculum Committee (2017–2018)
• Boise State CS Dept. Graduate Recruiting Committee (2017)
• Texas State CS Dept. Undergraduate Committee (2014–2016)
• Texas State CS Dept. Written Comp Exam Grading (2014–2016)
• UMN CS Graduate Student Association secretary (2009–2010)

Community Service

• December 2017 — Boise Public Library panel on preparing for a career in computer science
• Judge, 2015 Travis Elementary School Science Fair