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. RecommenderSystems and the Human Factor Mark Graus Netherlands Machine Learning Meetup 2016/03/16
- 3. I’ve been working with ‘recommender systems’ since 2009 Movie Recommender Systems Website Personalization App Personalization 200920112016
- 4. Content WhatAre Recommender Systems Why Machine Learning is not Enough
- 7. 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
- 8. How I see Recommender Algorithms Implicit Feedback Explicit Feedback Collaborative Content-Based
- 9. Distinction 1: Implicit versus Explicit Feedback Implicit My actual behavior watching skipping/stopping Explicit The feedback I give star rating
- 10. 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
- 11. 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 ?
- 13. Matrix Factorization butalso FunkSVD, SVD+ UsualSuspects Titanic DieHard TheGodfather Jack Dylan Olivia Mark ? ? ? ? ? ? ? Dimensionality Reduction
- 15. Content-Based versus Collaborative Considerations Metadata availability Need for explaining
- 16. MyApproach Start with Open Source Software Lenskit (Java) MyMediaLite (C#) Mahout (Python) Learn about Recommender Systems and User Base Scale Up Cassandra Akka
- 17. State-of-the- Art We can do predictions really well Challenges Cold Start Problem Context-Aware Recommendations Social Recommendations “Merged accounts”
- 18. Why Machine Learning is Not Enough
- 20. 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?
- 21. We need to do A/B testing andUX measurement System A System B
- 22. 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
- 23. Take Home Message The Machine Learning is just the beginning of Recommender Systems
- 24. 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