User, Agent, Subject, Spy

I gave this talk on November 26, 2019 on a visit to the University of Texas at Austin.

My Research

These papers provide more details on the research I presented. Many of them have accompanying code to reproduce the experiments and results.

RecSys16
2016

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. DOI 10.1145/2959100.2959179. Acceptance rate: 36%. Cited 63 times.

ComplexRec18
2018

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 4 times.

KidRec18
2018

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. Cited 7 times.

FLAIRS17-s
2017

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. No acceptance rate reported. Cited 5 times.

Reveal18-mc
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. Cited 1 time.

FAT18-ck
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 99 times.

RecSys18
2018

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. DOI 10.1145/3240323.3240373. arXiv:1808.07586v1 [cs.IR]. Acceptance rate: 17.5%. Cited 82 times.

FAT18-fp
2018

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 52 times.

Projects

Funding

Other Work Cited

  • ACM Code of Ethics
  • Crawford, K. 2017. The Trouble with Bias. NIPS 2017 Keynote.
  • Robyn Speer. 2017. ConceptNet Numberbatch 17.04: better, less-stereotyped word vectors.
  • Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. 2012. Fairness Through Awareness. In (Proceedings of the 3rd Innovations in Theoretical Computer Science Conference (pp. 214–226). New York, NY, USA: ACM. DOI 10.1145/2090236.2090255
  • Friedler, S. A., Scheidegger, C., & Venkatasubramanian, S. 2016. On the (im)possibility of fairness. arXiv:1609.07236 [cs, Stat]. Retrieved from http://arxiv.org/abs/1609.07236
  • Chouldechova, A. 2016. Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. arXiv [stat.AP]. Retrieved from http://arxiv.org/abs/1610.07524
  • Kleinberg, J., Mullainathan, S., & Raghavan, M. 2016. Inherent Trade-Offs in the Fair Determination of Risk Scores. arXiv [cs.LG]. Retrieved from http://arxiv.org/abs/1609.05807
  • Lipton, Z. C., Chouldechova, A., & McAuley, J. 2017. Does mitigating ML’s disparate impact require disparate treatment? arXiv [stat.ML]. Retrieved from http://arxiv.org/abs/1711.07076
  • Burke, R. 2017. Multisided Fairness for Recommendation. arXiv [cs.CY]. Retrieved from http://arxiv.org/abs/1707.00093
  • Neil Hunt. 2014. 🎞 Quantifying the Value of Better Recommendations.
  • Bart P Knijnenburg, Saadhika Sivakumar, and Daricia Wilkinson. 2016. Recommender Systems for Self-Actualization. In Proc. RecSys ’16, 11–14. DOI:https://doi.org/10.1145/2959100.2959189
  • Sabina Tomkins, Steven Isley, Ben London, and Lise Getoor. 2018. Sustainability at Scale: Towards Bridging the Intention-Behavior Gap with Sustainable Recommendations. In Proceedings of the 12th ACM Conference on Recommender Systems, 214–218. DOI:https://doi.org/10.1145/3240323.3240411
  • Cremonesi, P., Koren, Y., & Turrin, R. 2010. Performance of Recommender Algorithms on Top-n Recommendation Tasks. In Proceedings of the Fourth ACM Conference on Recommender Systems (RecSys 2010) (pp. 39–46). New York, NY, USA: ACM.
  • Steck, H. 2018. Calibrated Recommendations. In Proceedings of the 12th ACM Conference on Recommender Systems (RecSys 2018).
  • Sturgeon, T. 1958. ON HAND: A Book. Venture Science Fiction, 2(2), 66. March 1958.