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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.
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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
