Michael Ekstrand

User, Agent, Subject, Spy

I gave this talk on August 20, 2019 on a visit to Microsoft Research Montreal.

My Research

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

RecSys12-f
2012

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 88 times. Cited 73 times.

RecSys14
2014

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 282 times. Cited 184 times.

RecSys15
2015

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 136 times. Cited 99 times.

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 139 times. Cited 94 times.

Complex18
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 7 times. Cited 3 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 6 times. Cited 6 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 16 times. Cited 10 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 at RecSys 2018. DOI 10.18122/cs_facpubs/148/boisestate. NSF PAR 10074452. Cited 1 time. 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 284 times. Cited 211 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%. Citations reported under UMUAI21◊. Citations reported under UMUAI21◊.

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 104 times. Cited 77 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.

Q&A Papers

These papers I mentioned in the Q&A and other comments:

  • Alain Starke, Martijn Willemsen, and Chris Snijders. 2017. Effective User Interface Designs to Increase Energy-efficient Behavior in a Rasch-based Energy Recommender System. In Proc. RecSys ’17, 65–73. DOI:https://doi.org/10.1145/3109859.3109902
  • Ion Madrazo Azpiazu and Maria Soledad Pera. 2019. Multiattentive Recurrent Neural Network Architecture for Multilingual Readability Assessment. Transactions of the Association for Computational Linguistics 7, (March 2019), 421–436. DOI:https://doi.org/10.1162/tacl_a_00278