Content Based Recommendation System on Movies
D. Phani Kumar, Animesh Kumar Singh, Sai Neha Arepu, Manideep Sarvasuddi, Erragatla Gowtham, Yalamanchili Sanjana · Advances in engineering research/Advances in Engineering Research · 2023
Today's competitive environment makes it necessary for suggestive advice to be made to the user for them to continue using the services they currently find enjoyable.There, the recommender system's function assumes a key role.Every service in today's world has a recommendation system for movies, music, e-commerce, etc.The Netflix recommender system is essential for increasing the customer experience when watching movies on the service.This research proposes a machine learning-based content-based recommender system for movie recommendations.Examining the movie-enabling recommendations using data from the Tmdb, movies dataset from Kaggle.We use algorithms like Count Vectorizer, Porter Stemmer, and Cosine Similarity to generate five similar movies closely related to the type of content the target movie has and how well our machine-learning approach is working.