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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Mobile games success and failure
T2 - mining the hidden factors
AU - Kerim, Abdulrahman
AU - Genc, Burkay
PY - 2025/1/1
Y1 - 2025/1/1
N2 - Predicting the success of a mobile game is a prime issue in game industry. Thousands of games are being released eachday. However, a few of them succeed while the majority fail. Toward the goal of investigating the potential correlationbetween the success of a mobile game and its specific attributes, this work was conducted. More than 17 thousand gameswere considered for that reason. We show that IAPs (In-App Purchases), genre, number of supported languages, developerprofile, and release month have a clear effect on the success of a mobile game. We also develop a novel success scorereflecting multiple objectives. Furthermore, we show that game icons with certain visual characteristics tend to be asso-ciated with more rating counts. We employ different machine learning models to predict a novel success score metric of amobile game given its attributes. The trained models were able to predict this score, as well as the expected rating averageand rating count for a mobile game with 70% accuracy.
AB - Predicting the success of a mobile game is a prime issue in game industry. Thousands of games are being released eachday. However, a few of them succeed while the majority fail. Toward the goal of investigating the potential correlationbetween the success of a mobile game and its specific attributes, this work was conducted. More than 17 thousand gameswere considered for that reason. We show that IAPs (In-App Purchases), genre, number of supported languages, developerprofile, and release month have a clear effect on the success of a mobile game. We also develop a novel success scorereflecting multiple objectives. Furthermore, we show that game icons with certain visual characteristics tend to be asso-ciated with more rating counts. We employ different machine learning models to predict a novel success score metric of amobile game given its attributes. The trained models were able to predict this score, as well as the expected rating averageand rating count for a mobile game with 70% accuracy.
KW - Data mining
KW - Mobile games
KW - Game features
KW - Machine learning
KW - ANN
U2 - 10.1007/s00521-022-07154-z
DO - 10.1007/s00521-022-07154-z
M3 - Journal article
VL - 37
SP - 543
EP - 557
JO - Neural Computing and Applications
JF - Neural Computing and Applications
SN - 0941-0643
IS - 2
ER -