Abstract: With the continuous growth of the gaming industry, players often face difficulty discovering new games that match their interests. The vast collection of games on digital platforms such as Steam can overwhelm users, making personalized recommendations crucial for enhancing user experience. This paper proposes PlaySmart, a content-based recommender system that leverages machine learning techniques to suggest games based on their similarity to titles previously enjoyed by users. Using the Steam dataset, game metadata such as genres, developers, tags, and descriptions are processed using the TF-IDF (Term Frequency–Inverse Document Frequency) vectorization technique and cosine similarity to compute recommendations. The proposed model provides relevant, accurate, and personalized results while maintaining simplicity and interpretability.The proposed system demonstrates how content-based filtering can effectively personalize recommendations while maintaining simplicity, scalability, and transparency key factors in modern recommender systems for digital entertainment platforms.

Keywords: Machine Learning, Recommender System, Content-Based Filtering, TF-IDF, Steam Dataset, Cosine Similarity

Downloads: | DOI: 10.17148/IJIREEICE.2025.131028