Combining Sequential and Aggregated Data for Churn Prediction inCasual Freemium Games
Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Research › peer-review
Within this scenario, it is extremely important to be able to detect promptly when a player is about to quit playing (churn) in order to react and attempt to retain the player within the game, thus prolonging his or her game lifetime.
In this article we investigate how to improve the current state-of-the-art in churn prediction by combining sequential and aggregate data using different neural network architectures.
The results of the comparative analysis show that the combination of the two data types grants an improvement in the prediction accuracy over predictors based on either purely sequential or purely aggregated data.
|Title of host publication||Proceedings of the IEEE Conference on Games|
|Number of pages||8|
|Publication date||30 Jul 2019|
|Publication status||Published - 30 Jul 2019|
|Event||IEEE Conference on Games - Queen Mary University of London, London, United Kingdom|
Duration: 20 Aug 2019 → 23 Aug 2019
|Conference||IEEE Conference on Games|
|Location||Queen Mary University of London|
|Periode||20/08/2019 → 23/08/2019|