The Movie Recommendation Engine is a system that leverages k-nearest neighbor search to provide personalized movie recommendations based on user ratings. It efficiently analyzes user preferences, identifies similar users, and suggests movies that align with the user's taste.
Given a user and a number r of recommendations desired, this will efficiently return an ordered list of the top r movies recommended for that user from my movies dataset.
This project is for the purpose of "Build to Understand", specifically exploring how k-nearest neighbor, collaborative filtering can be used in real life to build movie recommender systems like Netflix.
Data is provided by professor Brandon Fain at Duke University. The dataset consists of roughly 1,000 users have rated a total of roughly 1,700 movies. However, most users have only rated a small subset of the movies. Rather than representing the input as a 1,000 by 1,700 matrix of ratings, we just have a list of ratings of the form: user id, movie id, rating, timestamp.