Profiling Players with Engagement Predictions
This addresses user profiling for video game companies to identify high-value players, but it is incremental as it applies existing methods to new data.
The paper tackled the problem of profiling high-spending video game users by using player engagement predictions, including survival curves and lifetime value predictions from a deep learning method, and found it to be a promising approach.
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.