Publications
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
- ORCID
- Google Scholar
- Microsoft Academic
- DBLP
- Computer Science Bibliography
- Semantic Scholar
Citation counts from Microsoft Academic. With this data, my h-index is 12 and my i10-index is 13. Google Scholar records somewhat higher citation counts which can affect these metrics.
Journal Papers
2016. Dependency Injection with Static Analysis and Context-Aware Policy. Journal of Object Technology 15, 1 (February 2016), pp 1:1โ31. DOI:10.5381/jot.2016.15.5.a1.
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 12 times.
, , , , and .2011. RecBench: Benchmarks for Evaluating Performance of Recommender System Architectures. Proceedings of the VLDB Endowment 4, 11 (August 2011), 911โ920. Acceptance rate: 18%. Cited 6 times.
, , , , , and .2011. Collaborative Filtering Recommender Systems. Foundations and Trendsยฎ in Human-Computer Interaction 4, 2 (February 2011), pp 81โ173. DOI:10.1561/1100000009. Cited 308 times (634 est.).
, , 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 81:172โ186. 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 81:35โ47. 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 โ15). ACM. DOI:10.1145/2792838.2800195. Acceptance rate: 21%. Cited 16 times.
, , , and .2014. User Perception of Differences in Recommender Algorithms. In Proceedings of the Eighth ACM Conference on Recommender Systems (RecSys โ14). ACM. DOI:10.1145/2645710.2645737. Acceptance rate: 23%. Cited 34 times (70 est.).
, , , 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 โ14). ACM. DOI:10.1145/2556325.2566244. Acceptance rate: 37%. Cited 19 times.
, , , , and .2013. Rating Support Interfaces to Improve User Experience and Recommender Accuracy. In Proceedings of the Seventh ACM Conference on Recommender Systems (RecSys โ13). ACM. DOI:10.1145/2507157.2507188. Acceptance rate: 24%. Cited 20 times.
, , , , , , and .2012. How Many Bits per Rating?. In Proceedings of the Sixth ACM Conference on Recommender Systems (RecSys โ12). ACM, pp 99โ106. DOI:10.1145/2365952.2365974. Acceptance rate: 20%. Cited 13 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 โ12). ACM, 86โ96. 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 โ11). ACM, 133โ140. DOI:10.1145/2043932.2043958. Acceptance rate: 27% (20% for oral presentation, which this received). Cited 72 times (134 est.).
, , , 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 โ11). ACM, 195โ204. DOI:10.1145/2047196.2047220. Acceptance rate: 25%. Cited 21 times.
, , , , and .2010. Automatically Building Research Reading Lists. In Proceedings of the Fourth ACM Conference on Recommender Systems (RecSys โ10). ACM, 159โ166. DOI:10.1145/1864708.1864740. Acceptance rate: 19%. Cited 47 times (68 est.).
, , , , , and .2009. rv youโre dumb: Identifying Discarded Work in Wiki Article History. In Proceedings of the 5th International Symposium on Wikis and Open Collaboration (WikiSym โ09). ACM, 10 pp. DOI:10.1145/1641309.1641317. Acceptance rate: 36% (Selected as Best Paper). Cited 24 times.
and .Short Papers
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 โ12). ACM, 233โ236. DOI:10.1145/2365952.2366002. Acceptance rate: 32%. Cited 19 times.
and .Workshops, Seminars, Posters, Etc.
These have undergone some form of peer review, but are not fully-reviewed scientific publications.
2018. Do Different Groups Have Comparable Privacy Tradeoffs?. To appear in Proceedings of the CHI 2018 Workshop on Moving Beyond a โOne-Size Fits Allโ Approach: Exploring Individual Differences in Privacy.
, , , and .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 โ16). 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 โ16.
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.
.Other Publications
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 1 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 โ11). ACM, 395โ396. DOI:10.1145/2043932.2044020. Cited 3 times.
, , and .2011. LensKit: a modular recommender framework. Demo presented at the Fifth ACM Conference on Recommender Systems (RecSys โ11). ACM, 349โ350. DOI:10.1145/2043932.2044001. Cited 12 times.
, , , and .