Understanding what players truly prefer remains one of the major challenges in today’s gaming industry. Developers often rely on intuition or general market trends rather than structured data, leading to inaccurate targeting and missed opportunities. This research presents a database-driven system for gaming preference analytics, designed to collect, organize, and analyze player activity data such as liked games, playtime, and user interactions. The system applies similarity metrics and data analysis techniques to identify relationships among players and their gaming interests, enabling personalized game recommendations and community insights. The project emphasizes conceptual, logical, and physical database design, ensuring efficient data organization and retrieval. While the core of this work focuses on database development, it also establishes a foundation for future integration of artificial intelligence methods, such as clustering and predictive analytics, to enhance personalization and recommendation accuracy. Overall, this research demonstrates how structured data and analytical modeling can bridge the gap between player behavior and informed decision-making, supporting smarter marketing, improved engagement, and sustainable growth in the gaming industry.

Gaming Preference Analytics, Recommendation System, Database Design

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