Content Based Movie Recommendation System

Content Based Movie Recommendation System

Prof. N.R. Zinzurke, Pranav Chobhe, Sonali Jogdand, Sakshi Karle

Dept. Computer Engineering JSPM’s JSCOE Pune, India

Abstract— In the era of digital streaming platforms, the sheer volume of available content can overwhelm users, making the efficient discovery of relevant and enjoyable content a critical challenge. Many individuals spend hours and hours of their productive time in searching for the content that best fits their needs and interests. To address this challenge, movie recommendation system has emerged as essential tool. The adoptionof computer-assisted approaches is essential to help in overcoming these constraints. With the advancement of data science, machine learning (ML) models are being used in recommending a type of content to user based on his/her individual interests. In this study, a vectorization and cosine similarity base model is fine-tuned efficiently to study user interest in order to recommend the content. In this paper, we present a movie recommendation engine following a content- based recommendation approach. We use publicly available features such as movie plot, movie genre and ratings to find similarity between the movies and generate a recommendation list. By providing recommendations which are consistent with user interest, the aim is to make the platform personalized.

Keywords—Movie            recommendation,            content-based, vectorization, cosine similarity, movies, recommender system, TMDb.