How to Build a Movie Recommendation Engine?

The audience loves to watch movies irrespective of their age, gender, race, color, or location. The movie is an amazing medium that keeps people connected in a way. The most interesting fact is how unique our choices and combinations are in terms of movie preferences. Some individuals prefer to watch thriller movies while others like to watch romance or sci-fi movies. It would be unfair to generalize a movie and say everybody likes it. So, here comes the movie recommendation engine into the play and analyzes all the behavioral patterns of audiences and refines as well as suggests preferred movies to them. So, waiting further, let’s jump into the basics of a movie recommender system and the steps to build a movie recommendation engine.

What is a Movie Recommendation Engine?

A movie recommendation engine is a tool that filters and suggests movies to audiences according to their preferences. The main objective of a recommender system is to predict the user’s preferred content. It analyzes user data and recommends personalized movies in real-time. It includes a class of techniques as well as algorithms that can suggest relevant items to the end-user.

Why do we need a movie recommendation engine?

We are presently in the “era of abundance”. There are thousands of movies to choose from and a movie recommendation engine is the only tool that can save users a lot of time and help them find something they like. From a business point of view, the more relevant movies a user/subscriber finds on the platform, the higher will be the engagement. Various sources have articulated that about 35% to 40% of revenue comes from recommendations to the users.

Building a Movie Recommendation Engine

First of all, we will import libraries that we will be using in our movie recommendation system, and also, we will import the data set by adding the path of the CSV files. After adding the data, we must look at the file using the data frame.head() command to print the first 5 rows of the data set. The movie data set has:

• MovieId – once the recommendation is done, we get a list of all similar movield and get the title for each movie from this dataset.
• Genres – Once the analyzation is done, it will classify movies into different genres

 The rating data set includes:

• UserId – User Id is unique for each user
• MovieId – using this feature we take the title of the movie from the movie data set
• Rating – it includes the ratings by each user to all movies and using this we can predict the top 10 similar movies

Implementation

To find the likeness between movies for the content-based method, we can use a cosine similarity function and for the collaborative method, we can use the matrix factorization technique.

The three steps involved in the implementation of a recommendation engine are:

• Building a matrix factorization-based model
• Creating hand-crafted features
• Implement the final model

Step 1 – Matrix Factorization-based algorithm

It is a class of collaborative filtering algorithms used in a recommendation engine. It became popular during the Netflix prize challenge due to how effective it was. It works by decomposing the user-movie interaction matrix into the product of two lower dimensionality rectangular matrices.

Step 2 – Creating Handcrafted Features

The next step is to convert the data frame format into a user-movie interaction matrix. Matrices used in this kind of problem are usually sparse because there is a high possibility user may only rate a few movies.

Advantages of sparse matrix format of data, also called CSR format are:

• Efficient arithmetic operations
• Efficient row slicing
• Fast matrix-vector products

Spicy.sparse.csr_matrix is a utility function that effectively converts the data frame into a sparse matrix and ‘train_sparse_matrix’ is the sparse matrix representation of the train_data data frame.

Step 3 – Creating a final model for our movie recommendation engine

To create the final model, you can use XGBoost as an optimized distributed gradient boosting library.

XG Boost is defined as an optimized distributed gradient boosting library designed to be highly efficient, flexible as well as portable. Under the gradient boosting library, a machine learning algorithm is implemented. It offers parallel tree boosting that solves several data science problems in a fast and precise method.

Performance metrics 

There are two major methods to evaluate a recommendation engine’s performance:

• Root Mean Squared Error
•  Mean Absolute Percentage Error

Root mean squared error method measures squared loss while Mean absolute percentage error measures absolute loss. Lower values denote lower error rates and thus enhanced performance. Both of them are helpful and good because they allow for lower error rates and enhanced performance.

Conclusion

In this blog, we learned what a movie recommender system is, how important a recommendation engine is and how to build and implement a recommender system. However, we at Muvi have a pre-build recommender system – Alie which allows you to start working on your projects by just taking the subscription. You don’t need a team of coders to develop a recommender system for your business anymore! Alie integrates with your website and applications to provide real-time recommendations. Its unique machine learning algorithm is designed to analyze user data and recommend personalized content in real-time with impeccable accuracy. Start a 14-days free trial to explore how Alie can help your movie streaming platform boost user engagement without a team of backend developers.