nedimcanulusoy / hybrid-recommender-system

Item and User Based Hybrid Recommender System

Hybrid Recommender System

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Problem

It is desired to suggest 10 movies for the user whose ID is given, using the item-based and user-based recommender methods.

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Dataset

The dataset was provided by MovieLens, a movie recommendation service. It contains the rating scores for these movies along with the movies. It contains 2,000,0263 ratings across 27,278 movies. This data set was created on October 17, 2016. Includes 138,493 users and data from 09 January 1995 to 31 March 2015. Users are randomly selected. It is known that all selected users voted for at least 20 movies.

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movie.csv

There are 3 different variables and 27278 observations in the dataset.

movieId : Movie ID
title   : Movie title
genres  : Movie genre

rating.csv

There are 4 different variables and 20000263 observations in the dataset.

userId  : User ID
movieId : Movie ID
rating  : Rating score
timestamp : Time of rating

The data set can be found under the following MovieLens link.

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Installation

1. Clone this repository

https://github.com/nedimcanulusoy/Hybrid-Recommender-System.git

2. Change directory to the cloned repository

cd Hybrid-Recommender-System

3. Open the notebook and run the cells

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