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Measuring Player Retention and Monetization Using the Mean Cumulative Function.

, , , , and . IEEE Trans. Games, 12 (1): 101-114 (2020)

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Learning to rank with pairwise regularized least-squares, , , , and . SIGIR 2007 Workshop on Learning to Rank for Information Retrieval, page 27--33. (2007)An efficient algorithm for learning to rank from preference graphs., , , , and . Mach. Learn., 75 (1): 129-165 (2009)Speedy Local Search for Semi-Supervised Regularized Least-Squares., , , and . KI, volume 7006 of Lecture Notes in Computer Science, page 87-98. Springer, (2011)RLScore: Regularized Least-Squares Learners., and . J. Mach. Learn. Res., (2016)Empirical Evaluation of Amplifying Privacy by Subsampling for GANs to Create Differentially Private Synthetic Tabular Data., , and . TKTP, volume 3506 of CEUR Workshop Proceedings, page 72-81. CEUR-WS.org, (2023)A Sparse Regularized Least-Squares Preference Learning Algorithm., , , , and . SCAI, volume 173 of Frontiers in Artificial Intelligence and Applications, page 76-83. IOS Press, (2008)Measuring Player Retention and Monetization Using the Mean Cumulative Function., , , , and . IEEE Trans. Games, 12 (1): 101-114 (2020)User activity decay in mobile games determined by simple differential equations?, , , and . CIG, page 1-8. IEEE, (2016)Parallel Feature Selection for Regularized Least-Squares., , , , and . PARA, volume 7782 of Lecture Notes in Computer Science, page 280-294. Springer, (2012)Fast and parallelized greedy forward selection of genetic variants in Genome-wide association studies., , , , and . GENSiPS, page 214-217. IEEE, (2011)