7: Collaborative filtering

You interact nearly every day with recommendation systems—algorithms which guess what products and services you might like, based on your past behavior. These systems largely rely on collaborative-filtering, an approach based on linear algebra that fills in the missing values in a matrix. Today we’ll see two ways to do this: one based on a classic linear algebra formulation, and one based on deep learning.

Video

This lesson is based partly on chapter 8 of the book.

Resources

• Notebooks for this lesson:
  • Road to the top: part 3 and part 4
  • Collaborative Filtering Deep Dive
• Spreadsheets for this lesson:
  • Softmax and cross-entropy
  • Collaborative filterings and embeddings
• Things that confused me about cross-entropy by Chris Said
• Label Smoothing Explained using Microsoft Excel by Aman Arora