Movie Recommendation Using Cosine Similarity

A Movie Recommendation system used to recommendation movie on the basis of the previous movie watched by a user.

Introduction

A recommendation systems is used to recommendation a movie,music,video etc on the basis of previous one.

All Social-Media Industry used it to provide the content that the users are most interest. They just track their data to recommendation those things.

Type on Recommender System

1) content-based systems

2) collaborative filtering systems

content-based systems

This are very simple and old method. In this system they will search the similar content that the user saw in last video or movie. May be of same artist or same director etc.

collaborative filtering systems

It is a new method of recommender system. Most of the social-media use this method to recommender things to their user.

Here they make connect user on the basis of what they watch and like, for example: if i like to watch hindi movie and another person also like it such that our choose is much similar so they connect us so next time when i watched a movie and give it a like then the algorithem knows that the another person would all interested to watch it.

This method is also used all by shoping site.

# This Project is based on content-based systems

Implementation of content-based systems

All data are first converted in to vector using "CountVectorizer." After that by using cosine similarity the model has been train on a data of 5000 movies. All the Movies name are available in "Movies_title.txt"

you just try it by you own :

1) Clone the repo.

2) start 'Movie Recomentation.py'

3) open 'Movies_title.txt'

4) just copy the name of a movies of whose similar movies you want then just paste it in 'Movie Recomentation.py' Input line

5) Top 5 similar movies available in database will show up.

Screenshots

Authors

• @Somnath Dash