The Machine Learning Guide to Fine Dining

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Can insights from machine learning guide us in human decision-making? We explore this question in the context of fine dining. We will illustrate selected ML algorithms by applying them to real-life problems such as how to choose a restaurant, whether to trust server recommendations, and when to go with a favorite dish or try something new. Rani Nelken is Director of Research at Outbrain where he works on the advanced algorithms behind the company's recommendation technology. Prior to that he was a research fellow at Harvard University. He has worked at IBM Research and several startups. He received his PhD in Computer Science from the Technion in 2001.

Technology Education

The Machine Learning Guide to
Fine Dining
Rani Nelken
1

Motivation
Human
decision
making
Machine
Learning
2

How to pick the best
restaurant?
Trust your server’s
recommendations?
Stick with a favorite
or try a new dish?
3

Best restaurant for a group of friends?
4

Ensemble methods and rank
aggregation
Group
restaurant
choice
Elections
Meta-search
Combining
classifiers
5

Borda count: consensus over majority
Candidate John Paul George
1st Choice A A C
2nd Choice B C A
3rd Choice C B B
A: ASTA
B: Grill 23 & Bar
C: Craigie on Main
6

Borda count
Restaurant John Paul George Score
1st Choice A A C 3
2nd Choice B C A 2
3rd Choice C B B 1
Candidate Score
A 3+3+2=8
C 1+2+3=6
B 2+1+1=4
7

Beyond Borda Count
• Partial lists
• Uneven comparisons
• Enhanced Heuristics
• Efficiency
8

More holistic view: Markov Chains
AB C
Edges represent preference
Self-edges
Nodes represent
restaurants
Find stationary distribution using power method
9

How to pick the best
restaurant?
Stick with a favorite
or try a new dish?
Trust your server’s
recommendations?
10

Bayesian classification
Trust
server’s
recs?
Document
classification
11

Flip the problem using Bayes’ rule
• Instead of
estimate
• Reminder:
p(good | rec)
p(rec | good)
p(good | rec) =
p(rec | good)p(good)
p(rec)
12

Naïve Bayes
• How to estimate ?
•
• Reduce to liking individual ingredients
p(rec | good)
p(rec | good) » p( 1I | good)p( 2I | good)...
13

What about unknown ingredients?
p(rec | good) » p( 1I | good)p( 2I | good)... = 0
14

Solution: Laplace smoothing
15
http://www.youtube.com/watch?v=iGPldwfodd
w

How to pick the best
restaurant?
Trust your server’s
recommendations?
Stick with a favorite
or try a new dish?
16

Multi-arm Bandits
When to
choose a
new dish?
Website
optimization,
Clinical trials
17

ε-Greedy
Exploit
Favorite
Explore
New
New dish
1
New dish
2
18

How to choose between unknown
dishes?Information used
Sophistication
ε-greedy
SoftmaxDishes’
previous
success ratio
Dishes’ #tries
UCB
GLM UCB
Model of
unseen dishes
Contextual bandits
Bayesian sampling
19

Strategy for unknown dishes
• Prior estimate for
based on ingredients
• Optimistic correction
p(good | rec)
20

Life lessons from bandits
• Optimism in the face of uncertainty
• Minimize regret relative to other strategies
21

Summary
Human
decision
making
Machine
Learning
22

Further reading
• Rank
Aggregation:http://www10.org/cdrom/papers/pdf/p577.p
df
• Bayesian classification in Intro to IR:
http://nlp.stanford.edu/IR-book/
• Bandit algorithms for website optimization
http://shop.oreilly.com/product/0636920027393.do
• Contextual Bandits:
http://hunch.net/~exploration_learning/main.pdf
• Bayesian Methods for Hackers
https://github.com/CamDavidsonPilon/Probabilistic-
Programming-and-Bayesian-Methods-for-Hackers
• Ingredient Networks: http://arxiv.org/pdf/1111.3919v3.pdf
23

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The Machine Learning Guide to Fine Dining

1. The Machine Learning Guide to Fine Dining Rani Nelken 1

2. Motivation Human decision making Machine Learning 2

3. How to pick the best restaurant? Trust your server’s recommendations? Stick with a favorite or try a new dish? 3

4. Best restaurant for a group of friends? 4

5. Ensemble methods and rank aggregation Group restaurant choice Elections Meta-search Combining classifiers 5

6. Borda count: consensus over majority Candidate John Paul George 1st Choice A A C 2nd Choice B C A 3rd Choice C B B A: ASTA B: Grill 23 & Bar C: Craigie on Main 6

7. Borda count Restaurant John Paul George Score 1st Choice A A C 3 2nd Choice B C A 2 3rd Choice C B B 1 Candidate Score A 3+3+2=8 C 1+2+3=6 B 2+1+1=4 7

8. Beyond Borda Count • Partial lists • Uneven comparisons • Enhanced Heuristics • Efficiency 8

9. More holistic view: Markov Chains AB C Edges represent preference Self-edges Nodes represent restaurants Find stationary distribution using power method 9

10. How to pick the best restaurant? Stick with a favorite or try a new dish? Trust your server’s recommendations? 10

11. Bayesian classification Trust server’s recs? Document classification 11

12. Flip the problem using Bayes’ rule • Instead of estimate • Reminder: p(good | rec) p(rec | good) p(good | rec) = p(rec | good)p(good) p(rec) 12

13. Naïve Bayes • How to estimate ? • • Reduce to liking individual ingredients p(rec | good) p(rec | good) » p( 1I | good)p( 2I | good)... 13

14. What about unknown ingredients? p(rec | good) » p( 1I | good)p( 2I | good)... = 0 14

15. Solution: Laplace smoothing 15 http://www.youtube.com/watch?v=iGPldwfodd w

16. How to pick the best restaurant? Trust your server’s recommendations? Stick with a favorite or try a new dish? 16

17. Multi-arm Bandits When to choose a new dish? Website optimization, Clinical trials 17

18. ε-Greedy Exploit Favorite Explore New New dish 1 New dish 2 18

19. How to choose between unknown dishes?Information used Sophistication ε-greedy SoftmaxDishes’ previous success ratio Dishes’ #tries UCB GLM UCB Model of unseen dishes Contextual bandits Bayesian sampling 19

20. Strategy for unknown dishes • Prior estimate for based on ingredients • Optimistic correction p(good | rec) 20

21. Life lessons from bandits • Optimism in the face of uncertainty • Minimize regret relative to other strategies 21

22. Summary Human decision making Machine Learning 22

23. Further reading • Rank Aggregation:http://www10.org/cdrom/papers/pdf/p577.p df • Bayesian classification in Intro to IR: http://nlp.stanford.edu/IR-book/ • Bandit algorithms for website optimization http://shop.oreilly.com/product/0636920027393.do • Contextual Bandits: http://hunch.net/~exploration_learning/main.pdf • Bayesian Methods for Hackers https://github.com/CamDavidsonPilon/Probabilistic- Programming-and-Bayesian-Methods-for-Hackers • Ingredient Networks: http://arxiv.org/pdf/1111.3919v3.pdf 23

24. Thank you @RaniNelken Rani Nelken 24

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