Electronic Thesis and Dissertation Repository


Master of Science


Computer Science

Collaborative Specialization

Artificial Intelligence


Katchabaw, Michael J.


We demonstrate the applicability and practicality of recurrent neural networks (RNNs), a machine learning methodology suited for sequential data, on player data from the mobile video game My Singing Monsters. Since this data comes in as a stream of events, RNNs are a natural solution for analyzing this data with minimal preprocessing. We apply RNNs to monitor and forecast game metrics, predict player conversion, estimate lifetime player value, and cluster player behaviours. In each case, we discuss why the results are interesting, how the trained models can be applied in a business setting, and how the preliminary work can serve as a foundation for future research. Finally, as data on video game players is typically proprietary and confidential and results of research often go unpublished, this thesis serves to contribute to the literature on game user research.