DESIGN AND IMPLEMENTATION OF A RELATIONAL DATA MODEL FOR ANIME RECOMMENDATION

Abstract

The Anime Recommendation System is a sophisticated application focused on improving the anime viewing experience through personalized recommendations that align with each user's unique tastes. In contrast to existing systems, which often depend on basic user ratings or simple genre classifications, this project utilizes advanced collaborative filtering techniques both user-based and item- based as well as content-based approaches. By harnessing an extensive dataset that includes various anime attributes, user ratings, and behavioral data, this system creates detailed recommendations that consider the complex interactions between users and different anime. The collaborative filtering element finds similarities among users and recommends titles based on the preferences of individuals with similar tastes. Meanwhile, the content-based approach examines genres and synopses to suggest anime akin to those a user has previously enjoyed. This dual-method system provides a more comprehensive recommendation process by addressing the shortcomings of traditional systems that often fail to capture the complexity of user preferences. Additionally, this system is built to be scalable, allowing for future improvements like hybrid recommendation models, real-time updates, and sentiment analysis. By incorporating user feedback and leveraging advanced machine learning techniques, this project aspires to deliver a more precise, engaging, and personalized anime discovery experience that sets a new benchmark for anime the anime industry