A couple of weeks ago, I gave a 4 hour lecture on Recommender Systems at the 2014 Machine Learning Summer School at CMU. The school was organized by Alex Smola and Zico Kolter and, judging by the attendance and the quality of the speakers, it was a big success. This is the outline of my lecture:

  1. Introduction: What is a Recommender System
  2. “Traditional” Methods
    • Collaborative Filtering
    • Content-based Recommendations
  3. “Novel” Methods
    • Learning to Rank
    • Context-aware Recommendations
    • Tensor Factorization
    • Factorization Machines
  4. Deep Learning
  5. Similarity<
  6. Social Recommendations
  7. Hybrid Approaches
  8. A practical example: Netflix
  9. Conclusions
  10. References

You can access the slides in Slideshare and the videos in Youtube, but I thought it would make sense to gather both here and link them together.

Here are the slides:

Here is the first session (2 hours):

First part

Here is the second session (2 hours):

Second part