Education
- Ph.D (2014)
- Computer Science, University of Minnesota, Minneapolis, MN.
- B.S. (2007)
- Computer Engineering, Iowa State University, Ames, IA.
Appointments
- 2023–present
- Assistant Professor, Dept. of Information Science, Drexel University
- 2022–2023
- Associate Professor, Dept. of Computer Science, Boise State University
- 2016–2022
- Assistant Professor, Dept. of Computer Science, Boise State University
- 2014–2016
- Assistant Professor, Dept. of Computer Science, Texas State University
Research
Funding
- 2023–2025: NSF 22-32553: Collaborative Research: CCRI: New: A Research News Recommender Infrastructure with Live Users for Algorithm and Interface Experimentation ($1.4M, my share $150K).
- 2018–2023: NSF 17-51278: CAREER: User-Based Simulation Methods for Quantifying Sources of Error and Bias in Recommender Systems. ($514,081, including REU supplements).
- 2017: $19K Boise State College of Education Civility 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 grant, $8K)
Selected Publications
Author formatting key:
, , .2023. Much Ado About Gender: Current Practices and Future Recommendations for Appropriate Gender-Aware Information Access. In Proceedings of the 2023 Conference on Human Information Interaction and Retrieval (CHIIR ’23). Proc. CHIIR ’23. DOI 10.1145/3576840.3578316. arXiv:2301.04780. NSF PAR 10423693. Acceptance rate: 39.4%. Cited 3 times. Cited 1 time.
, , , and .2022. Fairness in Information Access Systems. Foundations and Trends® in Information Retrieval 16(1–2) (July 2022), 1–177. FnT IR 16(1–2) (July 2022). DOI 10.1561/1500000079. arXiv:2105.05779. NSF PAR 10347630. Impact factor: 8. Cited 47 times. Cited 72 times.
, , , and .2022. Measuring Fairness in Ranked Results: An Analytical and Empirical Comparison. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’22). pp. 726–736. Proc. SIGIR ’22. DOI 10.1145/3477495.3532018. NSF PAR 10329880. Acceptance rate: 20%. Cited 17 times. Cited 15 times.
and .2021. Exploring Author Gender in Book Rating and Recommendation. User Modeling and User-Adapted Interaction 31(3) (February 2021), 377–420. UMUAI 31(3) (February 2021). DOI 10.1007/s11257-020-09284-2. NSF PAR 10218853. Impact factor: 4.412. Cited 135* times.
and .2021. Estimation of Fair Ranking Metrics with Incomplete Judgments. In Proceedings of The Web Conference 2021 (TheWebConf 2021). ACM. Proc. TheWebConf 2021. DOI 10.1145/3442381.3450080. arXiv:2108.05152. NSF PAR 10237411. Acceptance rate: 21%. Cited 26 times. Cited 28 times.
, , , , , and .2020. Evaluating Stochastic Rankings with Expected Exposure. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM ’20). ACM, pp. 275–284. Proc. CIKM ’20. DOI 10.1145/3340531.3411962. arXiv:2004.13157. NSF PAR 10199451. Acceptance rate: 20%. Nominated for Best Long Paper. Cited 121 times. Cited 115 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. Proc. CIKM ’20 (Resource track). DOI 10.1145/3340531.3412778. arXiv:1809.03125. NSF PAR 10199450. No acceptance rate reported. Cited 45 times. Cited 63* times.
.2020. Enhancing Classroom Instruction with Online News. Aslib Journal of Information Management 72(5) (June 2020), 725–744. AJIM 72(5) (June 2020). DOI 10.1108/AJIM-11-2019-0309. Impact factor: 1.903. Cited 9 times. Cited 12 times.
, , 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. Proc. FAT* 2018. Acceptance rate: 24%. Cited 170 times. Cited 190 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. Proc. FAT* 2018. Acceptance rate: 24%. Cited 66 times. 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. Proc. RecSys ’16 (Past, Present, and Future track). DOI 10.1145/2959100.2959179. Acceptance rate: 36%. Cited 81 times. Cited 100 times.
and .2015. Letting Users Choose Recommender Algorithms: An Experimental Study. In Proceedings of the 9th ACM Conference on Recommender Systems (RecSys ’15). ACM. Proc. RecSys ’15. DOI 10.1145/2792838.2800195. Acceptance rate: 21%. Cited 96 times. Cited 108 times.
, , , and .2014. User Perception of Differences in Recommender Algorithms. In Proceedings of the 8th ACM Conference on Recommender Systems (RecSys ’14). ACM. Proc. RecSys ’14. DOI 10.1145/2645710.2645737. Acceptance rate: 23%. Cited 167 times. Cited 234 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). DOI 10.1145/2728171. Impact factor: 1.293. Cited 24 times. Cited 106* 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. Proc. RecSys ’11. DOI 10.1145/2043932.2043958. Acceptance rate: 27% (20% for oral presentation, which this received). Cited 191 times. Cited 226 times.
, , , and .2011. Collaborative Filtering Recommender Systems. Foundations and Trends® in Human-Computer Interaction 4(2) (February 2011), 81–173. FnT HCI 4(2) (February 2011). DOI 10.1561/1100000009. Cited 632 times. Cited 1530 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. Proc. UIST ’11. DOI 10.1145/2047196.2047220. Acceptance rate: 25%. Cited 49 times. Cited 53 times.
, , , , and .Teaching
- BSU
- Intro to Data Science, recommender systems, databases, ethics
- Coursera
- Recommender Systems MOOC
Professional Service & Memberships
- Senior Member, Association for Computing Machinery
- Editorial board, Foundations and Trends in Information Retrieval
- Co-organizer, TREC 2019–2021 Track on Fairness in Information Retrieval
- ACM Conference on Recommender Systems (Program Co-chair 2022, General Co-chair 2018, Steering Committee & SPC member)
- Conference on Fairness, Accountability, and Transparency (FAccT) (Executive Committee 2020–2023, Steering Committee 2017–2023, Network Co-chair, PC 2017–present)
- Organizer, FATREC Workshop on Responsible Recommendation at RecSys 2017/2018/2020
- Senior PC member for RecSys & WWW; regular PC for SIGIR, FAccT, UMAP
- Reviewer for multiple journals, incl. TOIS, TWEB, TKDD, TIIS, TDSC, TKDE, PLOS ONE, and UMUAI