Create Your Own Movie Recommendation System Using Python

Do you wonder how Netflix suggests movies that align your interests so much? Or maybe you want to build a system that can make such suggestions to its users too?

If your answer was yes, then you’ve come to the right place as this article will teach you how to build a movie recommendation system by using Python. 

However, before we start discussing the ‘How’ we must be familiar with the ‘What.’

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Recommendation System: What is It?

Recommendation systems have become a very integral part of our daily lives. From online retailers like Amazon and Flipkart to social media platforms like YouTube and Facebook, every major digital company uses recommendation systems to provide a personalized user experience to their clients.

Some examples of recommendation systems in your everyday life include:

• The suggestions you get from Amazon when you buy products are a result of a recommender system.
• YouTube uses a recommender system to suggest videos suited for your taste.
• Netflix has a famous recommendation system for suggesting shows and movies according to your interests. 

A recommender system suggests users products by using data. This data could be about the user’s entered interests, history, etc. If you’re studying machine learning and AI, then it’s a must to study recommender systems as they are becoming increasingly popular and advanced. 

Types of Recommendation Systems

There are two types of recommendation systems:

1. Collaborative Recommendation Systems

A collaborative recommendation system suggests items according to the likeness of similar users for that item. It groups users with similar interests and tastes and suggests their products accordingly. 

For example, suppose you and one other user liked Sholay. Now, after watching Sholay and liking it, the other user liked Golmaal. Because you and the other user have similar interests, the recommender system would suggest you watch Golmaal based on this data. This is collaborative filtering. 

2. Content-Based Recommendation Systems

A content-based recommender system suggests items based on the data it receives from a user. It could be based on explicit data (‘Likes’, ‘Shares’, etc.) or implicit data (watch history). The recommendation system would use this data to create a user-specific profile and would suggest items based on that profile. 

Building a Basic Movie Recommendation System

Now that we have covered the basics of recommender systems, let’s get started on building a movie recommendation system. 

We can start building a movie recommendation system Python-based by using the full MovieLens dataset. This dataset contains more than 26 million ratings, 750,000 tag applications that are applied to over 45,000 movies. The tag genome data present in this dataset with more than 12 million relevance scores. 

We are using the full dataset for creating a basic movie recommendation system. However, you’re free to use a smaller dataset for this project. First, we’ll have to import all the required libraries:

A basic movie recommendation system Python-based would suggest movies according to the movie’s popularity and genre. This system works based on the notion that popular movies with critical acclamation will have a high probability of getting liked by the general audience. Keep in mind that such a movie recommendation system doesn’t give personalized suggestions. 

To implement it, we will sort the movies according to their popularity and rating and pass in a genre argument to get a genre’s top movies:

Input

md = pd. read_csv(‘../input/movies_metadata.csv’)

md.head()

Output

Input

md[‘genres’] = md[‘genres’].fillna(‘[]’).apply(literal_eval).apply(lambda x: [i[‘name’] for i in x] if isinstance(x, list) else [])

The Formula for Our Chart

For creating our chart of top movies, we used the TMDB ratings. We will use IMDB’s weighted rating formula to create our chart, which is as follows:

Weighted Rating (WR) = (iaouaouaouaouaou)

Here, v stands for the number of votes a movie got, m is the minimum number of votes a movie should have to get on the chart, R stands for the average rating of the movie, and C is the mean vote for the entire report. 

Building the Charts

Now that we have the dataset and the formula in place, we can start building the chart. We’ll only add those movies to our charts that have a minimum of 95% votes. We’ll begin with creating a top 250 chart. 

Input

vote_counts = md[md[‘vote_count’].notnull()][‘vote_count’].astype(‘int’)

vote_averages = md[md[‘vote_average’].notnull()][‘vote_average’].astype(‘int’)

C = vote_averages.mean()

C

Output

5.244896612406511

Input

m = vote_counts.quantile(0.95)

m

Output

434.0

Input

md[‘year’] = pd.to_datetime(md[‘release_date’], errors=’coerce’).apply(lambda x: str(x).split(‘-‘)[0] if x != np.nan else np.nan)

Input

qualified = md[(md[‘vote_count’] >= m) & (md[‘vote_count’].notnull()) & (md[‘vote_average’].notnull())][[‘title’, ‘year’, ‘vote_count’, ‘vote_average’, ‘popularity’, ‘genres’]]

qualified[‘vote_count’] = qualified[‘vote_count’].astype(‘int’)

qualified[‘vote_average’] = qualified[‘vote_average’].astype(‘int’)

qualified.shape

Output

(2274, 6)

As you can see, to get a place on our chart a movie must have a minimum of 434 votes. You may have noticed that the average rating a movie must have to enter our chart is 5.24. 

Input

def weighted_rating(x):

    v = x[‘vote_count’]

    R = x[‘vote_average’]

    return (v/(v+m) * R) + (m/(m+v) * C)

Input

qualified[‘wr’] = qualified.apply(weighted_rating, axis=1)

Input

qualified = qualified.sort_values(‘wr’, ascending=False).head(250)

With all of this in place, let’s build the chart:

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Top Movies Overall

Input

qualified.head(15)

Output

Voila, you have created a basic movie recommendation system Python-based! 

We will now narrow down our recommender system’s suggestions to genre-based so it can be more precise. After all, it is not necessary for everyone to like The Godfather equally. 

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Narrowing Down the Genre

So, now we’ll modify our recommender system to be more genre-specific:

Input

s = md.apply(lambda x: pd.Series(x[‘genres’]),axis=1).stack().reset_index(level=1, drop=True)

s.name = ‘genre’

gen_md = md.drop(‘genres’, axis=1).join(s)

Input

def build_chart(genre, percentile=0.85):

    df = gen_md[gen_md[‘genre’] == genre]

    vote_counts = df[df[‘vote_count’].notnull()][‘vote_count’].astype(‘int’)

    vote_averages = df[df[‘vote_average’].notnull()][‘vote_average’].astype(‘int’)

    C = vote_averages.mean()

    m = vote_counts.quantile(percentile)

    qualified = df[(df[‘vote_count’] >= m) & (df[‘vote_count’].notnull()) & (df[‘vote_average’].notnull())][[‘title’, ‘year’, ‘vote_count’, ‘vote_average’, ‘popularity’]]

    qualified[‘vote_count’] = qualified[‘vote_count’].astype(‘int’)

    qualified[‘vote_average’] = qualified[‘vote_average’].astype(‘int’)

    qualified[‘wr’] = qualified.apply(lambda x: (x[‘vote_count’]/(x[‘vote_count’]+m) * x[‘vote_average’]) + (m/(m+x[‘vote_count’]) * C), axis=1)

    qualified = qualified.sort_values(‘wr’, ascending=False).head(250)

        return qualified

We have now created a recommender system that sorts movies in the romance genre and recommends the top ones. We chose the romance genre because it didn’t show up much in our previous chart. 

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Top Movies in Romance

Input

build_chart(‘Romance’).head(15)

Output

Now, you have a movie recommender system that suggests top movies according to a chosen genre. We recommend testing out this recommender system with other genres too such as Action, Drama, Suspense, etc. Share the top three movies in your favourite genre the recommender system suggests in the comment section below

Learn More About a Movie Recommendation System 

As you must have noticed by now, building a movie recommendation system Python-based, is quite simple. All you need is a little knowledge of data science and a little effort to create a fully-functional recommender system. 

However, what if you want to build more advanced recommender systems? What if you want to create a recommender system that a large corporate might consider using? 

If you’re interested in learning more about recommender systems and data science, then we recommend taking a data science course. With a course, you’ll learn all the fundamental and advanced concepts of data science and machine learning. Moreover, you’ll study from industry experts who will guide you throughout the course to help you avoid doubts and confusion.

At upGrad, we offer multiple data science and machine learning courses. You can pick anyone from the following depending on your interests:

• PG Diploma in Data Science
• Master of Science in Data Science
• PG Certification in Data Science
• PG Diploma in Machine Learning and AI
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Apart from these courses, we offer many other courses in data science and machine learning. Be sure to check them out!

Final Thoughts

You now know how to build a movie recommendation system. After you have created the system, be sure to share it with others and show them your progress. Recommender systems have a diverse range of applications so learning about them will surely give you an upper hand in the industry.

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