<![CDATA[The explosive growth of the gaming industry has led to a "paradox of choice," where an overwhelming number of titles on platforms like Steam makes it difficult for players to discover games aligned with their personal preferences. This study addresses the critical challenge of game discovery by developing a novel recommendation system that integrates a Content-Based Filtering (CBF) approach with the Support Vector Machine (SVM) algorithm, a combination not extensively explored in the gaming domain. The system provides accurate, attribute-driven recommendations to enhance user experience. Utilizing a dataset of over 55.691 Steam games, we processed textual data such as genre, tags, and categories using TF-IDF before applying the SVM classifier. To validate its effectiveness, the model was benchmarked against K-Nearest Neighbor (KNN) across various training-to-testing ratios. The results demonstrate SVM's consistent superiority, achieving up to 98% accuracy. Notably, the high F1-score of 97.94% in genre-based recommendations signifies a well-balanced model that excels at both minimizing irrelevant suggestions and identifying relevant titles, directly translating to higher user satisfaction. The successfully deployed system, built on the Streamlit framework, was validated through black-box testing, confirming its functionality. This research confirms that the CBF-SVM model offers a highly effective solution to the game discovery problem, with future potential to incorporate hybrid filtering techniques for even greater personalization.]]>
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