Recommender Systems

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 RS reading group will not meet during the fall 2021 semester.

Past 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).

This group is open to the Princeton University community.

Please contact Amy Winecoff at for additional information.

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