User, Agent, Subject, Spy
I gave this talk on August 20, 2019 on a visit to Microsoft Research Montreal.
These papers provide more details on the research I presented. Many of them have accompanying code to reproduce the experiments and results.
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. Acceptance rate: 32%.and .
2014. User Perception of Differences in Recommender Algorithms. In Proceedings of the 8th ACM Conference on Recommender Systems (RecSys '14). ACM. Acceptance rate: 23%., , , and .
2015. Letting Users Choose Recommender Algorithms: An Experimental Study. In Proceedings of the 9th ACM Conference on Recommender Systems (RecSys '15). ACM. Acceptance rate: 21%., , , and .
2016. Behaviorism is Not Enough: Better Recommendations through Listening to Users. In Proceedings of the Tenth ACM Conference on Recommender Systems (RecSys '16). ACM. Acceptance rate: 36% (Past, Present, and Future track).and .
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., , , and .
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., , and .
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. AAAI, pp. 639–644.and .
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.and .
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%., , , , , , and .
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. Acceptance rate: 17.5%., , , , and .
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%., , and .
- NSF CAREER award
- Boise State University College of Education Civility Grant
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.
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