Improving the Accuracy of Online Game Recommendations: A Content-Based Filtering Approach Utilizing Cosine Similarity Matrix

Online game platforms face the challenge of providing accurate and personalized game recommendations to their users. This research study presents a novel content-based filtering strategy that uses a cosine similarity matrix to enhance the precision of online game recommendations. The study begins by collecting and analysing user preferences and game attributes. This data is used to construct a cosine similarity matrix, which measures the similarity between different games based on their content. The matrix serves as a foundation for the recommendation algorithm. Our recommendation algorithm considers the user's preferences and recommends games that have similar content to their previously enjoyed games. By leveraging the cosine similarity matrix, the algorithm can identify games that align with the user's interests and preferences more accurately. To assess the effectiveness of the approach, a comprehensive set of experiments was conducted using real-world data from an online gaming platform. The results demonstrate that the content-based filtering approach utilizing the cosine similarity matrix significantly improves the accuracy of game recommendations compared to traditional methods. The findings of this study contribute to the field of online game recommendations by introducing a novel approach that effectively utilizes a cosine similarity matrix. By leveraging the content-based filtering technique, the approach provides more accurate and personalized game recommendations, enhancing user satisfaction and engagement. In conclusion, this research highlights the potential of utilizing a cosine similarity matrix in the context of online game recommendations. By incorporating the content-based filtering approach, game platforms can enhance their recommendation systems and provide users with a more enjoyable and tailored gaming experience.