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The following Video Lectures on Recommender Systems were given by Netflix Research/Engineering Director, Xavier Amatriain at Machine Learning Summer School 2014 at Carnegie Mellon University in Pittsburgh. 

This is the outline of the lecture (Videos below):
  1. Introduction: What is a Recommender System
  2. “Traditional” Methods
    1. Collaborative Filtering
    2. Content-based Recommendations
  3. “Novel” Methods
    1. Learning to Rank
    2. Context-aware Recommendations
      1. Tensor Factorization
      2. Factorization Machines
    3. Deep Learning
    4. Similarity
    5. Social Recommendations
  4. Hybrid Approaches
  5. A practical example: Netflix 
  6. Conclusions
  7. References

Watch Part 1 (2hrs):

 

http://www.mlss2014.com / See the website for more videos and slides.

Watch Part 2 (2 hrs)

Recommender Systems (Machine Learning Summer School 2014 @ CMU) from Xavier Amatriain

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