Creating Movie Recommendations with Deep Learning

Creating Movie Recommendations with Deep Learning

Table of Contents:

I. Introduction II. Collaborative Filtering III. MovieLens Dataset IV. Building the Model V. Encoding Users and Movies VI. Training the Model VII. Saving the Model and Encoders VIII. Conclusion

I. Introduction

In today's video, we will be discussing how to use deep learning to build a movie recommendation system. We will be using collaborative filtering, which is the most basic way of building a recommendation system. Collaborative filtering involves different users rating different items, and then predicting the missing ratings for every user-item pair. We will be learning two vectors, one for the user and one for the item. The same idea can be applied to a movie recommendation system, where users rate movies between one to five or one to ten.

II. Collaborative Filtering

Collaborative filtering is a technique used in recommendation systems to predict the preferences of a user by collecting information from many users. It is Based on the idea that people who have similar preferences in the past will have similar preferences in the future. Collaborative filtering can be done in two ways: user-based and item-based. In user-based collaborative filtering, the system recommends items to a user based on the preferences of other users who are similar to that user. In item-based collaborative filtering, the system recommends items to a user based on the preferences of other users who have rated the same items.

III. MovieLens Dataset

To build our movie recommendation system, we will be using the MovieLens dataset. This is a large dataset that is freely available for download from movielens.org or from Kaggle. The dataset contains different movies and users who have rated the movies from one to five. We will be using a subset of this dataset for our tutorial.

IV. Building the Model

To build our movie recommendation system, we will be using the TASE library. We will be defining a class called MovieDataSet, which will have three attributes: users, movies, and ratings. We will also be defining a class called RexModel, which will inherit from TASE's Model class. The RexModel class will have two attributes: user_embeds and movie_embeds. We will be concatenating these two embeddings and passing them through a linear layer to get the output.

V. Encoding Users and Movies

Before we can train our model, we need to encode the users and movies. We will be using the LabelEncoder class from scikit-learn to encode the users and movies.

VI. Training the Model

We will be using the fit method of the RexModel class to train our model. We will be using a holdout-based validation for our tutorial. We will also be using the mean squared error (MSE) loss function and the root mean squared error (RMSE) metric to monitor the performance of our model.

VII. Saving the Model and Encoders

Once we have trained our model, we need to save it along with the label encoders for the users and movies. We will be using the pickle library to save these objects.

VIII. Conclusion

In this tutorial, we have learned how to use deep learning to build a movie recommendation system. We have used collaborative filtering to predict the preferences of a user based on the preferences of other users. We have also learned how to encode the users and movies and how to train our model using the MovieLens dataset. Finally, we have learned how to save our model and label encoders for future use.