Recommendation Engine for Predicting Best Rated Movies

Abstract

Recommendation systems are a subclass of information filtering system that seek to predict the "rating" or "preference" that a user would give to an item. Recommendation systems have become extremely common in recent years, and are utilized in a variety of areas: some popular applications include movies, music, news, books, research articles, search queries, social tags, and products in general. For this study, we are considering movie recommendation system based on content from a movie dataset. Generally, two types of filtering methods used in recommendation system are content based filtering and collaborative based filtering. For this study, we are using these two methods not only on single but on different contents of movie information like rating, genre. Our recommendation engine would consider previously stored ratings and genre of the movie selected by user, to train the system and project movie name list that the user may like. In this study, for building the recommendation engine we have used content based algorithms and collaborative filtering algorithms available in GraphLab package in python. Finally, these two methods are compared to show which recommendation engine algorithm works better compared to other for most of the time.