CCP Games
Session events for a computer game
Description
We study early player behaviour in a live computer game, focusing on the first hours and days of new-player activity, and examine how behavioural trajectories relate to long-term retention. Using detailed event logs, we apply sequential pattern mining methods such as PrefixSpan to extract recurring subsequences that capture onboarding pathways and potential friction loops. The project explores how to represent low-level events as higher-level behavioural primitives, how to incorporate session/time constraints, and how to evaluate pattern stability across player cohorts. Discovered patterns will be tested for predictive value in retention modelling and assessed for interpretability and actionability. The project offers hands-on experience with real-world sequential data, scalable analysis, and applied ML research suitable for an MSc thesis.
How applications work
- Sign in with your University student account and send a short application.
- The assigned supervisor receives your application and contacts you if more information is needed.
- Follow the decision in My applications. You will also receive an email when the status changes.
Response times vary by project. Contact the supervisor for an update; submitting an application does not reserve a place.
Additional information
Qualifications: This project is suitable for Master’s students in computer science at the University of Iceland with a strong foundation in programming and a solid understanding of AI and deep neural networks. Applicants must have completed at least one of the following courses: Machine Learning (REI505M), Introduction to Deep Neural Networks (TÖL506M), or The AI Lifecycle (REI603M). Applicants are expected to have a good understanding of the material covered in the completed course/courses.