Abstract:
In this study, the author introduces "Cpython," a web-based platform that employs a Content-
Based Filtering Algorithm to deliver personalized anime recommendations to users. The
application is strategically designed to provide a solution that aids anime enthusiasts in
navigating the vast array of available anime titles. This research entails a comprehensive
outline and implementation of the application, which includes the incorporation of an
administrative panel feature for seamless management of anime-related data. Furthermore, this
investigation conducts a thorough assessment of the system's performance to ensure that the
generated anime suggestions genuinely resonate with and satisfy users. "Cpython" aspires to
offer an inventive remedy for anime aficionados who frequently grapple with the
overwhelming abundance of anime selections at their disposal. The application's objective is
to elevate the user experience and furnish superior guidance for selecting anime in accordance
with individual preferences. This research also holds potential impact in the development of
content-based recommendation systems applicable to various domains beyond anime. With the
technology utilized and methodologies implemented, "Cpython" serves as a tangible example
of how technology can be employed to address complex challenges in delivering relevant
recommendations.
