Implementation of Movie Recommender System - Python

Recommender Systems provide personalized suggestions for items that are most relevant to each user by predicting preferences according to user's past choices. They are used in various areas like movies, music, news, search queries, etc. These recommendations are made in two ways: 

• Collaborative filtering: Collaborative filtering makes recommendations based on user's past behavior and similar decisions made by other users to predict items user may be interested in. For example, if User A likes movies X and Y and User B likes Y and Z then system might recommend movie Z to User A.
• Content-based filtering: Content-based filtering recommends items based on the features of the items and the user's past preferences. For example, if a user likes action movies the system will recommend other action movies based on genres, actors or directors.

In this article we’ll build a basic recommender system using Python that recommends movies based on user past preferences.

Step 1: Import Libraries and Load the Data

In this step we will load pandas ,matplotlib and seaborn library. After that we will load user ratings for movies file. Each row in this file shows a user’s rating for a specific movie. To download file click here: .tsv file.

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

column_names = ['user_id', 'item_id', 'rating', 'timestamp']

path = 'file.tsv'

df = pd.read_csv(path, sep='\t', names=column_names)

df.head()

Output:

Step 2: Loading Movie Titles

Now we load another file that matches movie IDs to their titles. This helps us show the actual movie names instead of just numbers. You can download file from here: Movie_Id_Titles.csv.

movie_titles = pd.read_csv('Movie_Id_Titles.csv')
movie_titles.head()

Output:

Step 3: Merging Both Datasets

We now combine the data about user ratings and movie titles and we can see both the movie names and the ratings for each movie.

data = pd.merge(df, movie_titles, on='item_id')
data.head()

Output:

Step 4: Analyzing Movie Ratings

This gives us the average rating for each movie and use to find top-rated movies.

data.groupby('title')['rating'].mean().sort_values(ascending=False).head()

Output:

we can also see how many ratings each movie has. Movies with more ratings are more important when we make recommendations.

data.groupby('title')['rating'].count().sort_values(ascending=False).head()

Output:

Step 5: Creating a Ratings DataFrame

In this step the DataFrame stores the average rating and number of ratings for each movie. We’ll use this later to filter out movies with few ratings.

ratings = pd.DataFrame(data.groupby('title')['rating'].mean()) 

ratings['num of ratings'] = pd.DataFrame(data.groupby('title')['rating'].count())

ratings.head()

Output:

Step 6: Visualizing Ratings

We use matplotlib and seaborn to create visual charts that make the data easier to understand.

sns.set_style('white')
%matplotlib inline

 To Shows how many movies got how many ratings and helps identify popular movies.

plt.figure(figsize =(10, 4))

ratings['num of ratings'].hist(bins = 70)

Output:

 We now show the distribution of average ratings across movies

plt.figure(figsize =(10, 4))

ratings['rating'].hist(bins = 70)

Output:

 Step 7: Creating User-Movie Matrix

We now create a matrix where each row represents a user and each column represents a movie. The values in this matrix are the ratings that users have given to each movie.

moviemat = data.pivot_table(index ='user_id',
              columns ='title', values ='rating')

moviemat.head()

ratings.sort_values('num of ratings', ascending = False).head(10)

Output:

Step 8: Finding Similar Movies

We compare how users rated other movies vs. "Star Wars (1977)". corrwith() calculates correlation with all other movies.

starwars_user_ratings = moviemat['Star Wars (1977)']
liarliar_user_ratings = moviemat['Liar Liar (1997)']

starwars_user_ratings.head()

Output:

 We create a new DataFrame to store these correlations. Drop any NaN values (movies with no overlapping ratings).

similar_to_starwars = moviemat.corrwith(starwars_user_ratings)
similar_to_liarliar = moviemat.corrwith(liarliar_user_ratings)

corr_starwars = pd.DataFrame(similar_to_starwars, columns =['Correlation'])
corr_starwars.dropna(inplace = True)

corr_starwars.head()

 Output:

Step 9: Filter Movies with Enough Ratings

Join the correlation data with our ratings dataframe. Filter movies that have more than 100 ratings for more reliable recommendations.

corr_starwars.sort_values('Correlation', ascending = False).head(10)
corr_starwars = corr_starwars.join(ratings['num of ratings'])

corr_starwars.head()

corr_starwars[corr_starwars['num of ratings']>100].sort_values('Correlation', ascending = False).head()

Output:

Step 10: Trying Another Example

Same process as before now checking for movies similar to “Liar Liar (1997)”.

corr_liarliar = pd.DataFrame(similar_to_liarliar, columns =['Correlation'])
corr_liarliar.dropna(inplace = True)

corr_liarliar = corr_liarliar.join(ratings['num of ratings'])
corr_liarliar[corr_liarliar['num of ratings']>100].sort_values('Correlation', ascending = False).head()

Output:

We can see that our model is working fine giving recommendations based on user preference.

Get the complete notebook link here:

Colab Link : click here.