Movie Recommendation System built using AutoEncoders.It was trained on MovieLens Dataset.It follows collaborative filtering method. The Collaborative Filtering Recommender is entirely based on the past behavior and not on the context. More specifically, it is based on the similarity in preferences, tastes and choices of two users. It analyses how similar the tastes of one user is to another and makes recommendations on the basis of that.

The dataset that I’m working with is MovieLens, one of the most common datasets that is available on the internet for building a Recommender System. The version of the dataset that I’m working with (1M) contains 1,000,209 anonymous ratings of approximately 3,900 movies made by 6,040 MovieLens users who joined MovieLens in 2000

An autoencoder is a type of artificial neural network used to learn efficient data codings in an unsupervised manner.The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for dimensionality reduction. Recently, the autoencoder concept has become more widely used for learning generative models of data.Some of the most powerful AI in the 2010s have involved sparse autoencoders stacked inside of deep neural networks