Anime Segmentation Based on User Preferences: Applying Clustering to Identify Groups of Anime with Similar Genres, Themes, and Popularity

The anime industry has experienced significant growth, with an increasing focus on user preferences for content discovery and engagement. This study applies clustering techniques, specifically K-means, to segment anime based on user preferences, genres, themes, and popularity. By analyzing a comprehensive dataset containing attributes such as user ratings, popularity, genres, and themes, the research identifies distinct groups of anime that align with varying viewer tastes. The clustering results reveal that anime can be categorized into several groups, including highly popular but critically less-acclaimed titles, well-regarded but moderately popular anime, and niche, critically acclaimed series that appeal to smaller but dedicated audiences. This segmentation allows streaming platforms to offer more personalized recommendations, enhancing user experience and engagement by matching viewers with content that best fits their preferences. Although clustering techniques provide valuable insights into anime content, the study acknowledges certain limitations, such as overlap between clusters, indicating that some anime may not fit perfectly into a single category. This highlights the need for further improvements in segmentation accuracy. The study suggests exploring hybrid clustering methods, combining K-means with other techniques, and integrating demographic data, such as age, gender, and geographic location, to refine recommendations. Overall, the application of clustering algorithms to better understand user preferences in anime offers a promising approach to developing more effective and personalized recommendation systems. This can ultimately improve user satisfaction and engagement in the rapidly growing and competitive anime streaming market.