The Investigation on Anime-Themed Recommendation Systems

Abstract

In today's digital era, the way we enjoy entertainment has experienced a significant revolution, and this transformation also encompasses the world of anime. With a seemingly endless array of anime series accessible through online streaming services, users frequently grapple with a formidable dilemma: the paradox of abundance. This paper introduces a sophisticated anime recommendation system, carefully crafted to tackle this quandary and enhance the overall anime-watching journey. Drawing upon the capabilities of machine learning and data analysis, the system's primary goal is to provide personalized anime recommendations based on user preferences and behavior. This paper not only outlines the development of an intelligent anime recommendation system but also takes a critical look at the existing body of research in the fields of recommendation systems and anime-related studies. Emphasizing the significance of personalized recommendations, it highlights the crucial role they play in enhancing user engagement and satisfaction within the world of anime streaming. The system itself is a culmination of various techniques and methodologies, employing a hybrid approach that combines collaborative filtering, content-based filtering, and advanced machine learning techniques. Linear models, random forests, and boosting algorithms are skillfully harnessed for prediction purposes, showcasing the system's versatility and adaptability. Preliminary results presented in this paper offer a tantalizing glimpse into the system's potential to deliver tailored recommendations, ultimately enriching the user experience and fostering greater engagement with the captivating universe of anime.