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SciPy and Real-time Big Data for Site Optimization Pyleus Message Processor Bolt Pyleus Event Worker Bolt Pyleus SciPy Optimizer Bolt Pyleus Update Messenger Bolt SciPy Bayesian Bandit Spout Spout Application State Visitors to Bankrate.com Impressions and Clicks Improve User Experience For more info, contact: [email protected] Which Variation to show Bankrate.com Data Science and Engineering Team Example: Pick better story headlines Objective for Site Optimization: Enable fast and cost-efficient ways of testing new designs to improve user experience Algorithmically decide which of two headlines to show user to maximize click-thru-rate (CTR) Computation Framework with Kafka-Storm Simulation Results With more data, algorithm becomes more confident of estimated CTR for each variation Bayesian Multi-Armed Bandit algorithm on Storm Topology decides how often to show each variation by analyzing impressions and clicks Iteration: 100 W1: 56.37% W2: 42.63% Iteration: 1000 W1: 9.82% W2: 90.18% Iteration: 2000 behavior reversal W1: 64.06% W2: 39.94% Iteration: 3000 W1: 94.84% W2: 5.16% Iteration: 4000 W1: 97.12% W2: 2.88%

SciPy 2015: Real-time Big Data for Site Optimization

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SciPy and Real-time Big Data for Site Optimization

Pyleus

Message Processor

Bolt

Pyleus

EventWorker

Bolt

PyleusSciPy

Optimizer

Bolt

Pyleus

UpdateMessenger

Bolt

SciPy

BayesianBandit

Spout

Spout

ApplicationState

Visitors to Bankrate.com

Impressions and Clicks

Improve User Experience

For more info, contact:[email protected]

Which Variation to

show

Bankrate.com Data Science and Engineering Team

Example: Pick better story headlines

Objective for Site Optimization:Enable fast and cost-efficient ways of testing new designs to improve user experience

Algorithmically decide which of two headlines to show user to maximize click-thru-rate (CTR)

Computation Framework with Kafka-Storm

Simulation Results

With more data, algorithm becomes more confident of estimated CTR for each variation

Bayesian Multi-Armed Bandit algorithm on Storm Topology decides how often to show each variation by analyzing impressions and clicks

Iteration: 100

W1: 56.37%W2: 42.63%

Iteration: 1000

W1: 9.82%W2: 90.18%

Iteration: 2000 behavior reversalW1: 64.06%W2: 39.94%

Iteration: 3000

W1: 94.84%W2: 5.16%

Iteration: 4000

W1: 97.12%W2: 2.88%