Movie Recommender System Using K-means Clustering And K-nearest Neighbor

Movie Recommender System Using K-Means Clustering AND K-Nearest Neighbor

Abstarct:

Implementation of Movie Recommender System. Recommender System is a system that seeks to predict or filter preferences according to the user's choices. This model is then used to predict items (or ratings for items) that user may have an interest in. Recommendation engines basically are data filtering tools that make use of algorithms and data to recommend the most relevant items to a particular user. Or in simple terms, they are nothing but an automated form of a “shop counter guy”. Recommender systems have become ubiquitous in our lives. Yet, currently, they are far from optimal. In this project, we attempt to understand the different kinds of recommendation systems and compare their performance on the MovieLens dataset. We attempt to build a scalable model to perform this analysis. We start by preparing and comparing the various models on a smaller datasets.