Assigning integer id's to movies

Let's do the same thing to the movies. Then let's join the new user IDs and movie IDs into one dataframe.

Cet exercice fait partie du cours

Building Recommendation Engines with PySpark

Afficher le cours

Instructions

Use the .select() and the .distinct() methods to extract all unique Movies from the ratings dataframe.
Repartition the movies dataframe to one partition using coalesce().
Complete the partial code provided to assign unique integer IDs to each movie. Name the new column movieId and call the .persist() method on the resulting dataframe.
Join the ratings dataframe to the users dataframe and subsequently to the movies dataframe. Call the result movie_ratings.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# Extract the distinct movie id's
movies = ratings.select("____").distinct() 

# Repartition the data to have only one partition.
movies = movies.coalesce(____) 

# Create a new column of movieId integers. 
movies = movies.withColumn("____", monotonically_increasing_id()).____() 

# Join the ratings, users and movies dataframes
movie_ratings = ratings.join(____, "User", "left").join(____, "Movie", "left")
movie_ratings.show()

Modifier et exécuter le code

Cet exercice fait partie du cours

Building Recommendation Engines with PySpark

AvancéNiveau de compétence

4.8+

Commencer le cours gratuitement

This chapter will show you how powerful recommendations engines can be, and provide important distinctions between collaborative-filtering engines and content-based engines as well as the different types of implicit and explicit data that recommendation engines can use. You will also learn a very powerful way to uncover hidden features (latent features) that you may not even know exist in customer datasets.

Exercise 1: Why learn how to build recommendation engines?Exercise 2: See the power of a recommendation engine Exercise 3: Power of recommendation engines Exercise 4: Recommendation engine types and data types Exercise 5: Collaborative vs content-based filtering Exercise 6: Collaborative vs content based filtering part II Exercise 7: Implicit vs explicit data Exercise 8: Ratings data types Exercise 9: Uses for recommendation engines Exercise 10: Alternate uses of recommendation engines.Exercise 11: Confirm understanding of latent features

In this chapter you will review basic concepts of matrix multiplication and matrix factorization, and dive into how the Alternating Least Squares algorithm works and what arguments and hyperparameters it uses to return the best recommendations possible. You will also learn important techniques for properly preparing your data for ALS in Spark.

Exercise 1: Overview of matrix multiplication Exercise 2: Matrix multiplication Exercise 3: Matrix multiplication part II Exercise 4: Overview of matrix factorization Exercise 5: Matrix factorization Exercise 6: Non-negative matrix factorization Exercise 7: How ALS alternates to generate predictions Exercise 8: Estimating recommendations Exercise 9: RMSE as ALS alternates Exercise 10: Data preparation for Spark ALS Exercise 11: Correct format and distinct users Exercise 12: Assigning integer id's to movies

Exercice en cours

Exercise 13: ALS parameters and hyperparameters Exercise 14: Build out an ALS model Exercise 15: Build RMSE evaluator Exercise 16: Get RMSE

In this chapter you will be introduced to the MovieLens dataset. You will walk through how to assess it's use for ALS, build out a full cross-validated ALS model on it, and learn how to evaluate it's performance. This will be the foundation for all subsequent ALS models you build using Pyspark.

Exercise 1: Introduction to the MovieLens dataset Exercise 2: Viewing the MovieLens Data Exercise 3: Calculate sparsity Exercise 4: The GroupBy and Filter methods Exercise 5: MovieLens Summary Statistics Exercise 6: View Schema Exercise 7: ALS model buildout on MovieLens Data Exercise 8: Create test/train splits and build your ALS model Exercise 9: Tell Spark how to tune your ALS model Exercise 10: Build your cross validation pipeline Exercise 11: Best Model and Best Model Parameters Exercise 12: Model Performance Evaluation Exercise 13: Generate predictions and calculate RMSE Exercise 14: Interpreting the RMSE Exercise 15: Do recommendations make sense

In most real-life situations, you won't not have "perfect" customer data available to build an ALS model. This chapter will teach you how to use your customer behavior data to "infer" customer ratings and use those inferred ratings to build an ALS recommendation engine. Using the Million Songs Dataset as well as another version of the MovieLens dataset, this chapter will show you how to use the data available to you to build a recommendation engine using ALS and evaluate it's performance.

Exercise 1: Introduction to the Million Songs Dataset Exercise 2: Confirm understanding of implicit rating concepts Exercise 3: MSD summary statistics Exercise 4: Grouped summary statistics Exercise 5: Add zeros Exercise 6: Evaluating implicit ratings models Exercise 7: Specify ALS hyperparameters Exercise 8: Build implicit models Exercise 9: Running a cross-validated implicit ALS model Exercise 10: Extracting parameters Exercise 11: Overview of binary, implicit ratings Exercise 12: Binary model performance Exercise 13: Recommendations from binary data Exercise 14: Course recap