Anime Based on Content-Based Filtering

Abstract

Anime comes from the word "Animation", nowadays it is very popular among people, mostly kids and teenagers. They are being streamed on large platforms such as Netflix and Amazon Prime. Millions of people watch anime nowadays regularly. It has a huge fan base. Due to this increment in the popularity of anime, there is a requirement for a recommendation system that can suggest user watch the best-suited anime for the user. The recommendation system is used to predict the preference of the user based on user interest. In this paper, we have proposed an anime recommendation system. It is based on a content-based filtering approach that makes use of the information provided by users, analyzes them and then recommends the different anime that is best suited to the user. This method helps users to find anime that they are interested in and can watch without concern about the content of the show. They don't have to ask other people for suggestions about the anime and they can have a lot of options at the same place. The recommended anime list is sorted according to the ratings given to these animations by the user that is stored in the dataset and it uses various algorithms such as content-based filtering and sectorization for this purpose. It doesn't allow users to waste time because of its efficient and effective technique. This system has been developed in Jupyter Notebook with the help of python and machine learning algorithms as the backend and for the front end python idle is used with various libraries.