User, Agent, Subject, Spy
I gave this talk on November 1, 2018 at the Boise State University AI Club.
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%. Cited 54 times.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%. Cited 107 times., , , 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%. Cited 74 times., , , and .
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. Acceptance rate: 36%. Cited 47 times.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. Cited 4 times., , , 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. Cited 4 times., , 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 (Recommender Systems track). AAAI, pp. 639–644. No acceptance rate reported. Cited 4 times.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%. Cited 64 times., , , , , , and .
2020. LensKit for Python: Next-Generation Software for Recommender Systems Experiments. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM '20, Resource track). ACM, pp. 2999–3006. No acceptance rate reported. Cited 5 times..
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. Acceptance rate: 27% (20% for oral presentation, which this received). Cited 161 times., , , 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%. Cited 57 times., , , , and .
Other Work Cited
- Franklin, Ursula M. 2004. The Real World of Technology. Revised Edition. Toronto, Ont.; Berkeley, CA: House of Anansi Press.
- ACM Code of Ethics
- 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.
- Neil Hunt. 2014. 🎞 Quantifying the Value of Better Recommendations.
- Robyn Speer. 2017. ConceptNet Numberbatch 17.04: better, less-stereotyped word vectors.