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The Machine Learning Guide to Fine Dining

Rani Nelken

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Motivation

Human decision making

Machine Learning

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How to pick the best restaurant?

Trust your server’s recommendations?

Stick with a favorite or try a new dish?

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Best restaurant for a group of friends?

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Ensemble methods and rank aggregation

Group restaurant

choice

ElectionsMeta-searchCombining classifiers

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Borda count: consensus over majority

Candidate John Paul George

1st Choice A A C2nd Choice B C A3rd Choice C B B

A: ASTAB: Grill 23 & BarC: Craigie on Main

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Borda count

Restaurant John Paul George Score

1st Choice A A C 32nd Choice B C A 23rd Choice C B B 1

Candidate ScoreA 3+3+2=8C 1+2+3=6B 2+1+1=4

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Beyond Borda Count

• Partial lists

• Uneven comparisons

• Enhanced Heuristics

• Efficiency

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More holistic view: Markov Chains

AB C

Edges represent preference

Self-edges

Nodes representrestaurants

Find stationary distribution using power method

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How to pick the best restaurant?

Stick with a favorite or try a new dish?

Trust your server’s recommendations?

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Bayesian classification

Trust server’s

recs?

Document classification

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Flip the problem using Bayes’ rule

• Instead of

estimate

• Reminder:

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Naïve Bayes

• How to estimate?

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• Reduce to liking individual ingredients

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What about unknown ingredients?

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Solution: Laplace smoothing

http://www.youtube.com/watch?v=iGPldwfoddw

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How to pick the best restaurant?

Trust your server’s recommendations?

Stick with a favorite or try a new dish?

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Multi-arm Bandits

When to choose a

new dish?

Website optimization, Clinical trials

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ε-Greedy

ExploitFavorite

Usually

With low probability

ExploreNew

New dish 1

New dish 2

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How to choose between unknown dishes?Information used

Sophistication

ε-greedy

SoftmaxDishes’ previous success ratio

Dishes’ #triesUCB

GLM UCBModel of unseen dishes

Contextual bandits

Bayesian sampling

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Strategy for unknown dishes

• Prior estimate for based on ingredients

• Optimistic correction

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Life lessons from bandits

• Optimism in the face of uncertainty

• Minimize regret relative to other strategies

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Summary

Human decision making

Machine Learning

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Further reading

• Rank Aggregation:http://www10.org/cdrom/papers/pdf/p577.pdf

• 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

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Thank you

@RaniNelkenRani Nelken