Profiling Players with Engagement Predictions

Autor:	del Río, Ana Fernández, Chen, Pei Pei, Periáñez, África
Rok vydání:	2019
Předmět:	Computer Science - Machine Learning Computer Science - Social and Information Networks Statistics - Machine Learning
Zdroj:	2019 IEEE Conference in Games (CoG)
Druh dokumentu:	Working Paper
DOI:	10.1109/CIG.2019.8848074
Popis:	The possibility of using player engagement predictions to profile high spending video game users is explored. In particular, individual-player survival curves in terms of days after first login, game level reached and accumulated playtime are used to classify players into different groups. Lifetime value predictions for each player---generated using a deep learning method based on long short-term memory---are also included in the analysis, and the relations between all these variables are thoroughly investigated. Our results suggest this constitutes a promising approach to user profiling. Comment: Accepted for IEEE Conference on Games (CoG) 2019
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1907.03870 Zobrazit plný text záznamu View this record from Arxiv