- Ph.D (2014)
- Computer Science, University of Minnesota, Minneapolis, MN. Advisers: John T. Riedl and Joseph A. Konstan
- B.S. (2007)
- Computer Engineering (With Distinction), Iowa State University, Ames, IA.
- Assistant Professor, Dept. of Computer Science, Boise State University
- Co-founder, People and Information Research Team (PIReT)
- Assistant Professor, Dept. of Computer Science, Texas State University
- Graduate Research Assistant, GroupLens Research, Dept. of Computer Science, University of Minnesota
- Summer 2010
- Research Intern, Autodesk Research, Toronto, CA
- CS 533 (Introduction to Data Science)
- CS 597 (Recommender Systems)
- CS 410 / CS 510 (Databases)
- Recommender Systems specialization on Coursera
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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 1 times., , , , and .
2018. The LKPY Package for Recommender Systems Experiments: Next-Generation Tools and Lessons Learned from the LensKit Project. Computer Science Faculty Publications and Presentations 147. Boise State University. Presented at the REVEAL 2018 Workshop on Offline Evaluation for Recommender Systems, a workshop at RecSys 2018. . . Cited 1 times..
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 3 times., , , , , , 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%. Cited 3 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. AAAI, pp. 639–644.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). Cited 9 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 33 times., , , and .
2016. Dependency Injection with Static Analysis and Context-Aware Policy. Journal of Object Technology 15(1) (February 2016), 1:1–31. . Cited 1 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 56 times., , , and .
2015. Teaching Recommender Systems at Large Scale: Evaluation and Lessons Learned from a Hybrid MOOC. Transactions on Computer-Human Interaction 22(2) (April 2015). . Cited 13 times., , , , and .
2013. Rating Support Interfaces to Improve User Experience and Recommender Accuracy. In Proceedings of the 7th ACM Conference on Recommender Systems (RecSys ’13). ACM. . Acceptance rate: 24%. Cited 26 times., , , , , , and .
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 99 times., , , and .
2011. Searching for Software Learning Resources Using Application Context. In Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology (UIST ’11). ACM, pp. 195–204. . Acceptance rate: 25%. Cited 25 times., , , , and .
- 2018–2023: NSF award 1751278, $482,081: CAREER: User-Based Simulation Methods for Quantifying Sources of Error and Bias in Recommender Systems
- 2017: $19K Boise State College of Education Empathy Grant LITERATE: Locating Informational Texts for Engaging Readers And Teaching Equitably (co-PI; with PI Katherine Wright & co-PI Sole Pera)
- 2014: Texas State University Research Enhancement Program (competitive internal research grant), $8000: Temporal Analysis of Recommender Systems.
- Co-organizer, TREC 2019 Track on Fairness in Information Retrieval
- ACM Conference on Recommender Systems (General Co-chair 2018, Steering Committee & PC member, Publicity 2016, Demos 2012)
- Conference on Fairness, Accountability, and Transparency (Steering Committee, PR & Publicity co-char 2019, Systems Track co-chair 2018)
- Distinguished Reviewer, ACM Transactions on Interactive Intelligent Systems (2017–present)
- Organizer, FATREC Workshop on Responsible Recommendation at RecSys 2017/2018
- External advisor, CrowdRec (EU Framework Programme collaborative project, 2014–2016)
- PC member and/or reviewer for numerous conferences, including WWW (Track on Behavior Analysis and Personalization), FLAIRS Special Track on Recommender Systems, CHI, CSCW, IUI, SAC REcommender Systems track, UIST, WikiSym/OpenSym, ICWSM
- Reviewer for multiple journals, includin TOIS, TWEB, TKDD, TIIS, TDSC, TKDE, PLOS ONE, and UMUAI
- Proceedings co-chair, ACM CHI 2012–2013