This page lists my research publications, organized by type and date. See my research page for a topical view of my research.
I also have research profiles elsewhere:
- Boise State SelectedWorks
- ACM Author Profile
- Google Scholar
- Microsoft Academic
- Computer Science Bibliography
- Semantic Scholar
Citation counts from Microsoft Academic. With this data, my h-index is 13 and my i10-index is 13. Google Scholar records somewhat higher citation counts which can affect these metrics.
2018. Rating-Based Collaborative Filtering: Algorithms and Evaluation. In Social Information Access. Peter Brusilovsky, ed. Springer-Verlag. ISBN 978-3-319-90091-9., , and .
2016. Dependency Injection with Static Analysis and Context-Aware Policy. Journal of Object Technology 15, 1 (February 2016), pp 1:1–31. DOI:10.5381/jot.2016.15.5.a1. Cited 1 times.and .
2015. Teaching Recommender Systems at Large Scale: Evaluation and Lessons Learned from a Hybrid MOOC. Transactions on Computer-Human Interaction 22, 2, Article 10 (April 2015), 23 pages. DOI:10.1145/2728171. Cited 13 times., , , , and .
2011. RecBench: Benchmarks for Evaluating Performance of Recommender System Architectures. Proceedings of the VLDB Endowment 4, 11 (August 2011), 911–920. Acceptance rate: 18%. Cited 6 times., , , , , and .
Refereed Conference Papers
These are full papers which have been published in peer-reviewed conference proceedings.
2018. All The Cool Kids, How Do They Fit In?: Popularity and Demographic Biases in Recommender Evaluation and Effectiveness. In Proceedings of the Conference on Fairness, Accountability and Transparency. PMLR 81:172–186. Acceptance rate: 24%., , , , , , and .
2018. Privacy for All: Ensuring Fair and Equitable Privacy Protections. In Proceedings of the Conference on Fairness, Accountability and Transparency. PMLR 81:35–47. Acceptance rate: 24%., , and .
2017. Sturgeon and the Cool Kids: Problems with Top-N Recommender Evaluation. In Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference.and .
2015. Letting Users Choose Recommender Algorithms: An Experimental Study. In Proceedings of the Ninth ACM Conference on Recommender Systems (RecSys ’15). ACM. DOI:10.1145/2792838.2800195. Acceptance rate: 21%. Cited 23 times., , , and .
2014. User Perception of Differences in Recommender Algorithms. In Proceedings of the Eighth ACM Conference on Recommender Systems (RecSys ’14). ACM. DOI:10.1145/2645710.2645737. Acceptance rate: 23%. Cited 70 times., , , and .
2014. Teaching Recommender Systems at Large Scale: Evaluation and Lessons Learned from a Hybrid MOOC. In Proceedings of the First ACM Conference on Learning @ Scale (ACM L@S ’14). ACM. DOI:10.1145/2556325.2566244. Acceptance rate: 37%. Cited 22 times., , , , and .
2013. Rating Support Interfaces to Improve User Experience and Recommender Accuracy. In Proceedings of the Seventh ACM Conference on Recommender Systems (RecSys ’13). ACM. DOI:10.1145/2507157.2507188. Acceptance rate: 24%. Cited 23 times., , , , , , and .
2012. RecStore: An Extensible And Adaptive Framework for Online Recommender Queries Inside the Database Engine. In Proceedings of the 15th International Conference on Extending Database Technology (EDBT ’12). ACM, 86–96. DOI:10.1145/2247596.2247608. Acceptance rate: 23%. Cited 9 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, 133–140. DOI:10.1145/2043932.2043958. Acceptance rate: 27% (20% for oral presentation, which this received). Cited 133 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, 195–204. DOI:10.1145/2047196.2047220. Acceptance rate: 25%. Cited 24 times., , , , and .
2010. Automatically Building Research Reading Lists. In Proceedings of the Fourth ACM Conference on Recommender Systems (RecSys ’10). ACM, 159–166. DOI:10.1145/1864708.1864740. Acceptance rate: 19%. Cited 69 times., , , , , and .
2009. rv you’re dumb: Identifying Discarded Work in Wiki Article History. In Proceedings of the 5th International Symposium on Wikis and Open Collaboration (WikiSym ’09). ACM, 10 pp. DOI:10.1145/1641309.1641317. Acceptance rate: 36% (Selected as Best Paper). Cited 23 times.and .
These are short research papers published in peer-reviewed conference proceedings.
2017. Recommender Response to Diversity and Popularity Bias in User Profiles. Short paper in Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference.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 ’12). ACM, 233–236. DOI:10.1145/2365952.2366002. Acceptance rate: 32%. Cited 23 times.and .
Workshops, Seminars, Posters, Etc.
These have undergone some form of peer review, but are not fully-reviewed scientific publications.
2018. Recommending Texts to Children with an Expert in the Loop. In Proceedings of the 2nd International Workshop on Children & Recommender Systems (KidRec) at IDC 2018., , .
Do Different Groups Have Comparable Privacy Tradeoffs?. At Moving Beyond a ‘One-Size Fits All’ Approach: Exploring Individual Differences in Privacy, a workshop at CHI 2018., , , and . 2018.
2017. The Demographics of Cool: Popularity and Recommender Performance for Different Groups of Users. In RecSys 2017 Poster Proceedings.and .
2017. Challenges in Evaluating Recommendations for Children. In Proceedings of the International Workshop on Children & Recommender Systems (KidRec) at RecSys 2017..
2016. Behaviorism is Not Enough: Better Recommendations through Listening to Users. In Proceedings of the Tenth ACM Conference on Recommender Systems (RecSys ’16). ACM. DOI:10.1145/2959100.2959179. Acceptance rate: 36% (Past, Present, and Future track). Cited 6 times.and .
2016. First Do No Harm: Considering and Minimizing Harm in Recommender Systems Designed for Engendering Health. In Proceedings of the Workshop on Recommender Systems for Health at RecSys ’16.and .
2014. Building Open-Source Tools for Reproducible Research and Education. In Sharing, Re-use and Circulation of Resources in Cooperative Scientific Work, a workshop at ACM CSCW 2014..
2017. The FATREC Workshop on Responsible Recommendation. In Proceedings of the 11th ACM Conference on Recommender Systems.and .
2014. Towards Recommender Engineering: Tools and Experiments in Recommender Differences. Ph.D Thesis, University of Minnesota. Cited 2 times..
2011. UCERSTI 2: Second Workshop on User-Centric Evaluation of Recommender Systems and Their Interfaces. Workshop at the Fifth ACM Conference on Recommender Systems (RecSys ’11). ACM, 395–396. DOI:10.1145/2043932.2044020. Cited 3 times., , and .