Dennis Torres
2025-02-08
Explainable Machine Learning Models for Predicting Player Retention Patterns
Thanks to Dennis Torres for contributing the article "Explainable Machine Learning Models for Predicting Player Retention Patterns".
This research explores the use of adaptive learning algorithms and machine learning techniques in mobile games to personalize player experiences. The study examines how machine learning models can analyze player behavior and dynamically adjust game content, difficulty levels, and in-game rewards to optimize player engagement. By integrating concepts from reinforcement learning and predictive modeling, the paper investigates the potential of personalized game experiences in increasing player retention and satisfaction. The research also considers the ethical implications of data collection and algorithmic bias, emphasizing the importance of transparent data practices and fair personalization mechanisms in ensuring a positive player experience.
This research examines the convergence of mobile gaming and virtual reality (VR) technologies, focusing on how the integration of VR into mobile games can create immersive, interactive experiences for players. The study explores the technical challenges of VR gaming on mobile devices, including hardware limitations, motion tracking, and user comfort, as well as the design principles that enable seamless interaction between virtual environments and physical spaces. The paper investigates the cognitive and emotional effects of VR gaming, particularly in relation to presence, immersion, and player agency. It also addresses the potential for VR to revolutionize mobile gaming experiences, creating new opportunities for storytelling, social interaction, and entertainment.
The gaming industry's commercial landscape is fiercely competitive, with companies employing diverse monetization strategies such as microtransactions, downloadable content (DLC), and subscription models to sustain and grow their player bases. Balancing player engagement with revenue generation is a delicate dance that requires thoughtful design and consideration of player feedback.
This paper explores the integration of artificial intelligence (AI) in mobile game design to enhance player experience through adaptive gameplay systems. The study focuses on how AI-driven algorithms adjust game difficulty, narrative progression, and player interaction based on individual player behavior, preferences, and skill levels. Drawing on theories of personalized learning, machine learning, and human-computer interaction, the research investigates the potential for AI to create more immersive and personalized gaming experiences. The paper also examines the ethical considerations of AI in games, particularly concerning data privacy, algorithmic bias, and the manipulation of player behavior.
This study applies social network analysis (SNA) to investigate the role of social influence and network dynamics in mobile gaming communities. It examines how social relationships, information flow, and peer-to-peer interactions within these communities shape player behavior, preferences, and engagement patterns. The research builds upon social learning theory and network theory to model the spread of gaming behaviors, including game adoption, in-game purchases, and the sharing of strategies and achievements. The study also explores how mobile games leverage social influence mechanisms, such as multiplayer collaboration and social rewards, to enhance player retention and lifetime value.
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