In this chapter, we will cover the following recipes:
- Modeling preference expressions
- Understanding the data
- Ingesting the movie review data
- Finding the highest-scoring movies
- Improving the movie-rating system
- Measuring the distance between users in the preference space
- Computing the correlation between users
- Finding the best critic for a user
- Predicting movie ratings for users
- Collaboratively filtering item by item
- Building a non-negative matrix factorization model
- Loading the entire dataset into the memory
- Dumping the SVD-based model to the disk
- Training the SVD-based model
- Testing the SVD-based model
