Recommendation-Systems

Movie Recommendation System

What is a recommendation system?

A recommendation system generates a compiled list of items in which a user might be interested, in the reciprocity of their current selection of item(s).

Every major tech giant of the FAANG(Facebook,Amazon,Apple,Netflix and Google) makes use of this system.

For instance, the Netflix recommendation system offers recommendations by matching and searching similar users' habits and suggesting movies that share characteristics with films that users have rated highly.

Amazon uses it to suggest products to customers, YouTube uses it to decide which video to play next on autoplay, and Facebook uses it to recommend pages to like and people to follow.

Problem Statement

We want to create a baseline rcommendation system with the TMDB data set.

Approach

1. Demographic Based Generic Recommendation system

The basic idea behind this system is that movies that are more popular and critically acclaimed will have a higher probability of being liked by the average audience.

Data Description

tmdb_5000_credits data

Features	Description
movie_id	A unique identifier for each movie
cast	The name of lead and supporting actors
crew	The name of Director, Editor, Composer, Writer etc

tmdb_5000_movies data

Features	Description
budget	The budget in which the movie was made
genre	The genre of the movie, Action, Comedy ,Thriller etc
homepage	A link to the homepage of the movie
id	This is infact the movie_id as in the first dataset
keywords	The keywords or tags related to the movie
original_language	The language in which the movie was made
original_title	The title of the movie before translation or adaptation
overview	A brief description of the movie
popularity	A numeric quantity specifying the movie popularity
production_companies	The production house of the movie
production_countries	The country in which it was produced
release_date	The date on which it was released
revenue	The worldwide revenue generated by the movie
runtime	The running time of the movie in minutes
status	"Released" or "Rumored"
tagline	Movie's tagline
title	Title of the movie
vote_average	average ratings the movie recieved
vote_count	the count of votes recieved

2. User-Item Based Recommendation system

The item-based system recommends items based on their similarity with the items that the target user rated. Likewise, the similarity can be computed with Pearson Correlation. We will be using Pearson correlation for our purpose

u.data

Features	Description
user_id	A unique identifier for each user
item_id	A unique identifier for each movie
rating	rating given
timestamp	timestamp at which rating was given

Movie_Id_Titles

Features	Description
item_id	A unique identifier for each movie
title	Title of the movie

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