Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/CIG.2019.8848074 | 2019 IEEE Conference on Games (CoG) |
Keywords | Field | DocType |
player profiling,survival analysis,machine learning,online games,user behavior,deep learning,LSTM neural networks | Customer lifetime value,Computer science,Profiling (computer programming),Login,Artificial intelligence,Deep learning,Machine learning | Conference |
ISSN | ISBN | Citations |
2325-4270 | 978-1-7281-1885-7 | 1 |
PageRank | References | Authors |
0.35 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ana Fernández del Río | 1 | 1 | 0.35 |
| Pei Pei Chen | 2 | 1 | 0.35 |
| África Periáñez | 3 | 1 | 0.35 |