Streamlit Enabled Adaptive Movie Recommendation Systems Using Content-Based Filtering Techniques

This paper outlines a movie recommendation system that has been proposed and implemented using Python and Streamlit and which relies on content-based filters solely to recommend films that match users' interest and preferred genres. The system takes information from the readily accessible large-scale datasets, preprocess the movie information and then calculate the cosine similarity between films. Through content based filtering, the recommendation engine recommends movies which are of the same genre or similar to those movies the user has expressed an interest in or those that the user has rated highly. The system has an interactive interface made by using Streamlit, more specifically, the program lets users enter the name of a movie and provides recommendations based on this input.