Machine Learningmeets

Web DevelopmentShuhei Iitsuka @tushuhei

ML is just for product engineers?Spam Mail DetectionImage Recognition (OCR)Recommendation EngineQuery SuggestionAuto CompletionImage TaggingWord IndexingFraud Detection

Search EngineVoice RecognitionRobot LocomotionAutomatic DiagnosisVideo Game ExpertAutomated TradingFace RecognitionPattern Recognition

Route SearchTrend ForecastingHandwriting RecognitionComputer VisionTranslationName IdentificationTransaction Data MiningHuman Modeling

Machine Learning is also forWeb designers / developers / marketers.

ML for designers: interactive data visualization.

Shuhei Iitsuka and Yutaka Matsuo: A Product Network Based on Browsing and Purchase Behavior in E-commerce. The institute of electronics information and communication engineers D. 2015.

Wedding Venue A

Wedding Venue B

Images from:http://zexy.net/wedding/c_7770021671/http://zexy.net/wedding/c_7770029193/

User

inquiry inquiry

Competition

ML for designers: interactive data visualization.

http://colah.github.io/posts/2014-10-Visualizing-MNIST/

Example: Visualizing MNIST: An Exploration of Dimensionality Reduction

Visualization of how a machine recognize handwritten digits to classify.

ML for developers: ask users for the best one.

Image from: Autonomous Systems Labs - TU Darmstadt http://www.ausy.tu-darmstadt.de/Research/Research

Website UsersVariation

Clicks

A/B testing

ML for developers: ask users for the best one.

Example: Obama Campaign in 2010

$60M improvementhttp://blog.optimizely.com/2010/11/29/how-obama-raised-60-million-by-running-a-simple-experiment/

Example: A/B testing on BingBEFORE

$10M annual revenue improvementIitsuka, Shuhei, and Yutaka Matsuo. "Website Optimization Problem and Its Solutions." Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2015.

AFTER

ML for marketers: product mapping on preference axis.

item #1item #2・・・

item #10000

user #1, ・・・ , user #m

BUYSKIP

SEEBUY

SEE SKIP

・・・

・・・

・・・

・・・LOGDATA luxury

modern

Axis of preferen

ce

modest

old-fashioned

User behavior

Aggregation(PCA)

item #1 item

#5

item #3item

#2item #4

item #7

item #6

Again,machine learning is not only for product engineers but also for designers / developers / marketers.

Conclusion: