Movie Recommendation Using Content-based Filtering.

Authors: Assistant Professor Akhilesh kumar singh, Naman kumar Maurya, Sejal Verma

Abstract: Recommendation System used to predict and suggest movies based on user preferences.The technique applied here tries to predict user preferences using an information filtering technique that improves the user experience through timely and pertinent recommendations. In particular, movie suggestions are vital for enhancing interpersonal relationships since they may provide users with entertainment options based on their tastes or the current popularity of films. Data filtering systems often use these to help people locate content that meet their needs by going through large databases and making recommendations on what to buy or watch. These filtering systems, at times referred to as recommender systems, recommendation engines, or platforms, are designed to predict how a user might rank or favor an item. They are mainly used in the business sector. The primary purpose of this project is to produce a content- based model for film recommendations that involve cosine similarity and vectorization to provide the consumer with general recommendations regarding the popularity of the films.

DOI: http://doi.org/