The goal of the recommender systems (RS) reading group is to gain deeper understanding both of seminal work as well as emerging ideas in the field. Papers will include research on RS algorithm development and evaluation; user-centered design and user studies for RS; fairness, accountability, and explainability in recommendations; and societal impacts of RS.
The group will meet biweekly for one hour to discuss a selected paper. A rotating discussion leader will engage the group in whatever format they feel is best for the paper (e.g., presentation, guided discussion, free form discussion).
Upcoming papers include:
Zhao, Q., Harper, F. M., Adomavicius, G., & Konstan, J. A. (2018, April). Explicit or implicit feedback? Engagement or satisfaction? A field experiment on machine-learning-based recommender systems. In Proceedings of the 33rd Annual ACM Symposium on Applied Computing (pp. 1331-1340).
Hu, Y., Koren, Y., & Volinsky, C. (2008, December). Collaborative filtering for implicit feedback datasets. In 2008 Eighth IEEE International Conference on Data Mining (pp. 263-272). Ieee.
Dacrema, M. F., Cremonesi, P., & Jannach, D. (2019, September). Are we really making much progress? A worrying analysis of recent neural recommendation approaches. In Proceedings of the 13th ACM Conference on Recommender Systems (pp. 101-109).
Knijnenburg, B. P., Bostandjiev, S., O’Donovan, J., & Kobsa, A. (2012, September). Inspectability and control in social recommenders. In Proceedings of the sixth ACM conference on Recommender systems (pp. 43-50).
Jannach, D., & Adomavicius, G. (2016, September). Recommendations with a purpose. In Proceedings of the 10th ACM conference on recommender systems (pp. 7-10).
Cremonesi, P., Koren, Y., & Turrin, R. (2010, September). Performance of recommender algorithms on top-n recommendation tasks. In Proceedings of the fourth ACM conference on Recommender systems (pp. 39-46).
For additional information please contact Amy Winecoff at .