Edu-Videos | Building Recommender Systems by Netflix Research/Eng Director Xavier Amatriain

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. 

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