Daniel Hall
2025-02-05
Explainable Machine Learning Models for Predicting Player Retention Patterns
Thanks to Daniel Hall for contributing the article "Explainable Machine Learning Models for Predicting Player Retention Patterns".
The fusion of gaming and storytelling has birthed narrative-driven masterpieces that transport players on epic journeys filled with rich characters, moral dilemmas, and immersive worlds. Role-playing games (RPGs), interactive dramas, and story-driven adventures weave intricate narratives that resonate with players on emotional, intellectual, and narrative levels, blurring the line between gaming and literature.
This paper explores the use of artificial intelligence (AI) in predicting player behavior in mobile games. It focuses on how AI algorithms can analyze player data to forecast actions such as in-game purchases, playtime, and engagement. The research examines the potential of AI to enhance personalized gaming experiences, improve game design, and increase player retention rates.
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