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[ELKR11]

Michael Ekstrand, Michael Ludwig, Joseph A. Konstan, and John Riedl. Rethinking the Recommender Research Ecosystem: Reproducibility, Openness, and LensKit. In Proceedings of the 5th ACM Conference on Recommender Systems, 133–140. ACM, 2011. doi:10.1145/2043932.2043958.

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[EK19]

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[EM17]

Michael D Ekstrand and Vaibhav Mahant. Sturgeon and the Cool Kids: Problems with Top-N Recommender Evaluation. In Proceedings of the 30th Florida Artificial Intelligence Research Society Conference. AAAI Press, May 2017. URL: https://aaai.org/ocs/index.php/FLAIRS/FLAIRS17/paper/viewPaper/15534.

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[KV97]

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[TDV21]

Yan-Martin Tamm, Rinchin Damdinov, and Alexey Vasilev. Quality Metrics in Recommender Systems: Do We Calculate Metrics Consistently? In RecSys '21, 708–713. New York, NY, USA, September 2021. Association for Computing Machinery. doi:10.1145/3460231.3478848.

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Yunhong Zhou, Dennis Wilkinson, Robert Schreiber, and Rong Pan. Large-Scale Parallel Collaborative Filtering for the Netflix Prize. In Algorithmic Aspects in Information and Management, 337–348. Springer Berlin Heidelberg, 2008. doi:10.1007/978-3-540-68880-8_32.