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Recommender Systems and the Human FactorMark GrausNetherlands Machine Learning Meetup
2016/03/16
I’m 50% Machine Learner 50% Psychologist
I’ve been working with ‘recommender systems’ since 2009
Movie Recommender Systems
Website
Personalization
App
Personalization
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920
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Content What Are Recommender Systems
Why Machine Learning is not Enough
What are Recommender Systems?
The Machine Learning Behind Recommender Systems
We use historical item-user data to predictunobserved item-user data
Typically big datasets i.e. billions of observations
millions of users
tons of items
Numerous Specifically Designed Algorithms
How I see Recommender Algorithms
Implicit Feedback Explicit Feedback
Collaborative
Content-Based
Distinction 1:Implicit versus Explicit Feedback
Implicit
My actual behavior watching
skipping/stopping
Explicit
The feedback I give star rating
Distinction 1:Considerations
“Oh no! My TiVo thinks I’m gay”
Jeffrey Zaslow, The Wall Street Journal, December 2002
What I Like versus What I Say I Like
Solution: Use a bit of both implicit and explicit
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 MainActor
Year Rating
The Usual Suspects
Crime Bryan Singer
Kevin Spacey
1995
Titanic Drama JamesCameron
LeonardoDiCaprio
1997
Die Hard Action John McTiernan
Bruce Willis
1988?
Distinction 2:Content-Based versus Collaborative FilteringKNN, Slope One
?
?
Matrix Factorizationbut also FunkSVD, SVD+
Usu
al S
usp
ect
s
Tit
an
ic
Die
Ha
rd
Th
e G
od
fath
er
Jack
Dylan
Olivia
Mark
?
?
?
? ? ?
?
Dimensionality Reduction
Matrix Factorizationbut also FunkSVD, SVD+
Jack
Mark
Olivia Dylan
Content-Based versus Collaborative
Considerations
Metadata availability
Need for explaining
My Approach
Start with Open Source Software Lenskit (Java)
MyMediaLite (C#)
Mahout (Python)
Learn about Recommender Systems and User Base
Scale Up Cassandra
Akka
State-of-the-Art
We can do predictions really well
Challenges Cold Start Problem
Context-Aware Recommendations
Social Recommendations
“Merged accounts”
Why Machine Learning is Not Enough
Recommender System Data is Observable Behavior
Recommendations
Behavior
Recommender System
User Experience
Examples of Things Data Cannot Tell Us
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?
We need to do A/B testing and UX measurement
System A System B
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
Take Home Message
The Machine Learning is just the beginning of Recommender Systems
Thank you for listening!
Some Pointers
Recommender Algorithms Yehuda Koren, Google
Introduction to Recommender Systems, Coursera/GroupLens
Infrastructure Netflix Tech Blog
A/B Testing Ron Kohavi, Microsoft Research
User Experience Evaluation in Recommender Systems Bart Knijnenburg, Clemson University
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