User, Agent, Subject, Spy
I gave this talk on November 1, 2018 at the Boise State University AI Club.
My Research
These papers provide more details on the research I presented. Many of them have accompanying code to reproduce the experiments and results.
2011. Collaborative Filtering Recommender Systems. Foundations and Trendsยฎ in Human-Computer Interaction 4(2) (February 2011), 81โ173. FnT HCI 4(2) (February 2011). DOI 10.1561/1100000009. Cited 632 times. Cited 1530 times.
, , and .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 โ12). ACM, pp.ย 233โ236. Proc. RecSys โ12. DOI 10.1145/2365952.2366002. Acceptance rate: 32%. Cited 68 times. Cited 76 times.
and .2014. User Perception of Differences in Recommender Algorithms. In Proceedings of the 8th ACM Conference on Recommender Systems (RecSys โ14). ACM. Proc. RecSys โ14. DOI 10.1145/2645710.2645737. Acceptance rate: 23%. Cited 168 times. Cited 234 times.
, , , and .2015. Letting Users Choose Recommender Algorithms: An Experimental Study. In Proceedings of the 9th ACM Conference on Recommender Systems (RecSys โ15). ACM. Proc. RecSys โ15. DOI 10.1145/2792838.2800195. Acceptance rate: 21%. Cited 97 times. Cited 108 times.
, , , and .2016. Behaviorism is Not Enough: Better Recommendations through Listening to Users. In Proceedings of the Tenth ACM Conference on Recommender Systems (RecSys โ16, Past, Present, and Future track). ACM. Proc. RecSys โ16 (Past, Present, and Future track). DOI 10.1145/2959100.2959179. Acceptance rate: 36%. Cited 81 times. Cited 100 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 โ18), at RecSys 2018. Proc. ComplexRec โ18. Cited 4 times. Cited 7 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 โ18), at IDC 2018. Proc. KidRec โ18. DOI 10.18122/cs_facpubs/140/boisestate. Cited 7 times. Cited 6 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.ย 639โ644. (Recommender Systems track). No acceptance rate reported. Cited 9 times. Cited 13 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. DOI 10.18122/cs_facpubs/148/boisestate. NSF PAR 10074452. Cited 1 time. Cited 1 time.
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 81:172โ186. Proc. FAT* 2018. Acceptance rate: 24%. Cited 170 times. Cited 190 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 โ20, Resource track). ACM, pp.ย 2999โ3006. Proc. CIKM โ20 (Resource track). DOI 10.1145/3340531.3412778. arXiv:1809.03125. NSF PAR 10199450. No acceptance rate reported. Cited 45 times. Cited 63* times.
.2011. Rethinking The Recommender Research Ecosystem: Reproducibility, Openness, and LensKit. In Proceedings of the Fifth ACM Conference on Recommender Systems (RecSys โ11). ACM, pp.ย 133โ140. Proc. RecSys โ11. DOI 10.1145/2043932.2043958. Acceptance rate: 27% (20% for oral presentation, which this received). Cited 191 times. Cited 226 times.
, , , and .2018. Exploring Author Gender in Book Rating and Recommendation. In Proceedings of the 12th ACM Conference on Recommender Systems (RecSys โ18). ACM, pp.ย 242โ250. Proc. RecSys โ18. DOI 10.1145/3240323.3240373. arXiv:1808.07586v1. Acceptance rate: 17.5%. Citations reported under UMUAI21. Citations reported under UMUAI21.
, , , , and .Funding
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.ย 39โ46). New York, NY, USA: ACM.
- Neil Hunt. 2014. ๐ Quantifying the Value of Better Recommendations.
- Robyn Speer. 2017. ConceptNet Numberbatch 17.04: better, less-stereotyped word vectors.