2018

2018 in purple neon fireworks letters
Photo by NordWood Themes on Unsplash.

As I've done the last two years, it's time for the annual what-I-did-this-year post! Well, about time; there are a couple more weeks in the year, but I expect their results to be mostly tidying up loose ends of things in this list.

  • Presented two papers at the inaugural Conference on Fairness, Accountability, and Transparency; one with the PIReTs, and another with Hoda Mehrpouyan and Rezvan Joshaghani.

  • Published a CHI workshop paper on fairness in privacy tradeoffs with Bart Knijnenburg, Hoda Mehrpouyan, and Rezvan Joshaghani.

  • Submitted a paper to SIGIR (rejected).

  • Submitted a proposal to NSF CyberLearning (declined).

  • Won an NSF CAREER award.

  • Saw Hamilton (the traveling company in Portland).

  • Book chapter with Daniel Kluver and Joe Konstan went to press.

  • Bought a road bike and began recreational distance riding. I got up to being able to do 30mi rides before winding down for the weather.

  • Co-organized the FairUMAP workshop on fairness in user modeling and personalization with Bamshad Mobasher, Robin Burke, and Bettina Berendt.

  • Oversaw build-out of the LITERATE prototype and carried out user study with fantastic collaborators Sole Pera and Katherine Wright.

  • Ran a very successful RecSys 2018 with Sole Pera and our amazing organizing committee.

  • Published and presented our work on author gender in RecSys 2018.

  • Taught CS 410/510 (Databases) in both fall and spring.

  • Taught CS-HU 310, our one-credit database introduction, in the summer.

  • Substantially improved my response time in grading student work.

  • Published two workshop papers and contributed to a NRMERA conference talk about the LITERATE project.

  • Supervised my M.S. student Mucun Tian to his first first-author paper, a work-in-progress piece for the REVEAL workshop on offline evaluation.

  • Co-organized the second FATERC Workshop on Responsible Recommendation, with more than 50 registered and a full room all day.

  • With Fernando Diaz and Asia Biega, proposed and had accepted a fairness track for TREC 2019.

  • With Michael Veale, organized publicity & outreach for ACM FAT* 2019 as Publicity & PR Co-chair.

  • Rebuilt LensKit in Python (project, paper).

  • Began supervising my first Ph.D student, Amifa Raj.

  • Submitted a proposal to the NSF 2026 IDEA Machine with Sole Pera, Hoda Mehrpouyan, Cathie Olschanowsky, and Elena Sherman.

  • Sat on commmittees for two successful Ph.D proposals (Ion Madrazo Azpiazu and Kimberley Gardner).

  • Gave invited seminar talks at CMU and Clemson.

  • Reviewed a number of papers, though not as many as last year.

  • Redid my academic visual identity with a website refresh and change of standard font.

I did not submit nearly as many grant proposals this year as last, because I received the CAREER early in the year and needed to focus on getting that research going along with RecSys organization.

Teaching

  • Spring — CS 410/510 (Databases)
  • Summer — CS-HU 310 (Intro to Databases)
  • Fall — CS 410/510 (Databases)

Active Grants

Publications

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. DOI10.4230/DagMan.7.1.96.

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.

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. Cited 2 times.

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. DOI10.1145/3240323.3240335. Cited 3 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. DOI10.1145/3240323.3240373. arXiv:1808.07586v1 [cs.IR]. Acceptance rate: 17.5%. Cited 10 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. DOI10.18122/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. DOI10.18122/cs_facpubs/147/boisestate. arXiv:1809.03125 [cs.IR]. Cited 1 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. Cited 2 times.

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. DOI10.18122/cs_facpubs/140/boisestate.

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. DOI10.1145/3274784.3274789. Cited 6 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. DOI10.1145/3213586.3226200.

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. ISBN 978-3-319-90091-9. DOI10.1007/978-3-319-90092-6_10. Cited 21 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.

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 10 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. Acceptance rate: 24%. Cited 10 times.