@ARTICLE{Bilski_Piotr_Prediction_2024,
 author={Bilski, Piotr and Łabędzki, Rafał and Bilski, Adrian},
 volume={vol. 70},
 number={No 4},
 journal={International Journal of Electronics and Telecommunications},
 pages={1081-1087},
 howpublished={online},
 year={2024},
 publisher={Polish Academy of Sciences Committee of Electronics and Telecommunications},
 abstract={The paper presents the methodology for evaluating mobile game players retention, which is the basis for generating economic income for its creators. The system for collecting and processing the in-game data (events related to the players’ actions) exploiting the Big Data cloud platform is described. The player profile with the crucial features allowing for the retention analysis is introduced. Datasets generated for the My Spa Resort mobile game by CherryPick company are described. The retention prediction approach based on the similarity estimation between the analyzed and already inactive players is presented. Results of the prediction using the k Nearest Neighbors (kNN) classifier are discussed.},
 type={Article},
 title={Prediction of the mobile game players’ payments-related retention from the Big Data perspective},
 URL={http://czasopisma.pan.pl/Content/133237/PDF-MASTER/37-4785-Bilski-sk-new.pdf},
 doi={10.24425/ijet.2024.152510},
 keywords={retention, data science, prediction, mobile game},
}