Research on two popular recommendation algorithms for anime
Zhefei Meng · Applied and Computational Engineering · 2023
Anime is a popular eastern art form whose audience is mainly young people. In recent years, many people choose to watch anime on the websites. The recommendation system plays a very important part in improving the user experience and saving time. This paper focuses on two basic recommendation algorithms based on machine learning and deep learning methods, including the content-based and collaborative filtering method. The data used in this paper was downloaded from a public dataset on Kaggle. This paper shows how the two methods perform in the anime dataset and compares the results of the two methods. However, two methods have their own advantages in different conditions. The content-based method works when the user wants some related contents. The collaborative filtering method works better while considering more factors other than contents. These two methods can be combined under a new algorithm to form an even more reliable and reasonable recommendation system in the future studies.