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Recommender Systems and the Human Factor

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The presentation I gave at the Machine Learning Netherlands Meetup March 16, 2017.

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Recommender Systems and the Human Factor

  1. 1. RecommenderSystems and the Human Factor Mark Graus Netherlands Machine Learning Meetup 2016/03/16
  2. 2. I’m 50% Machine Learner 50% Psychologist
  3. 3. I’ve been working with ‘recommender systems’ since 2009  Movie Recommender Systems  Website  Personalization  App  Personalization 200920112016
  4. 4. Content  WhatAre Recommender Systems  Why Machine Learning is not Enough
  5. 5. What are Recommender Systems?
  6. 6. The Machine Learning Behind Recommender Systems  We use historical item-user data to predict unobserved item-user data  Typically big datasets  i.e. billions of observations  millions of users  tons of items  Numerous Specifically Designed Algorithms
  7. 7. How I see Recommender Algorithms Implicit Feedback Explicit Feedback Collaborative Content-Based
  8. 8. Distinction 1: Implicit versus Explicit Feedback Implicit My actual behavior  watching  skipping/stopping Explicit The feedback I give  star rating
  9. 9. Distinction 1: Considerations “Oh no! MyTiVo thinks I’m gay” Jeffrey Zaslow,TheWall Street Journal, December 2002 What I Like versus What I Say I Like Solution: Use a bit of both implicit and explicit
  10. 10. Distinction 2: Content-Based versus Collaborative Filtering  Supervised learning  Features are extracted from ‘metadata’  Target variable is rating (explicit) or whether the movie will be watched (implicit) Genre Director Main Actor Year Rating The Usual Suspects Crime Bryan Singer Kevin Spacey 1995 Titanic Drama James Cameron Leonardo DiCaprio 1997 Die Hard Action John McTiernan Bruce Willis 1988 ?
  11. 11. Distinction 2: Content-Based versus Collaborative Filtering KNN,SlopeOne ? ?
  12. 12. Matrix Factorization butalso FunkSVD, SVD+ UsualSuspects Titanic DieHard TheGodfather Jack Dylan Olivia Mark ? ? ? ? ? ? ?  Dimensionality Reduction
  13. 13. Matrix Factorization butalso FunkSVD, SVD+ Jack Mark Olivia Dylan
  14. 14. Content-Based versus Collaborative Considerations  Metadata availability  Need for explaining
  15. 15. MyApproach  Start with Open Source Software  Lenskit (Java)  MyMediaLite (C#)  Mahout (Python)  Learn about Recommender Systems and User Base  Scale Up  Cassandra  Akka
  16. 16. State-of-the- Art  We can do predictions really well  Challenges  Cold Start Problem  Context-Aware Recommendations  Social Recommendations  “Merged accounts”
  17. 17. Why Machine Learning is Not Enough
  18. 18. Recommender System Data is Observable Behavior Recommendations Behavior Recommender System User Experience
  19. 19. Examples of Things Data CannotTellUs  Do I feel my privacy invaded?  Am I happy to have American Pie 2 recommended?  Why do people react to recommendations the way they do?  Presentation?  Bad Recommendations?  Choice Overload?
  20. 20. We need to do A/B testing andUX measurement System A System B
  21. 21. What did we learn from surveys?  Satisfaction = Recommendation Set Attractiveness - Choice Difficulty  More views != Satisfaction  Diversity influences Satisfaction  Long Lists = Difficult to Choose  Short Lists = Easier to Choose, but not enough choice  Right Balance = Short Lists of Diverse Items
  22. 22. Take Home Message  The Machine Learning is just the beginning of Recommender Systems
  23. 23. Thank you for listening! Some Pointers  Recommender Algorithms  Yehuda Koren, Google  Introduction to Recommender Systems,Coursera/GroupLens  Infrastructure  NetflixTech Blog  A/BTesting  Ron Kohavi, Microsoft Research  User Experience Evaluation in Recommender Systems  Bart Knijnenburg, Clemson University

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