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Recommendation challenges at Amazon Airstream use case Houssam Nassif AISTATS’15
11

Airstream use case Houssam Nassif AISTATS’15.

Dec 27, 2015

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Jasmine Conley
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Page 1: Airstream use case Houssam Nassif AISTATS’15.

Recommendation challenges at Amazon

Airstream use case

Houssam NassifAISTATS’15

Page 2: Airstream use case Houssam Nassif AISTATS’15.

Mission Statement

To be Earth’s Most Customer-Centric Company Where People Can Find and Discover Anything

They Want to Buy Online

Page 3: Airstream use case Houssam Nassif AISTATS’15.

Airstream

Page 4: Airstream use case Houssam Nassif AISTATS’15.

Visual Browse Experience

• How to define and measure?• Image parameters? Clicks? Hand curated?

Beautiful

• How to engage users?• Optimize for discovery or repeatability?• What products to show?

Engaging

Page 5: Airstream use case Houssam Nassif AISTATS’15.

Optimize for Users

• 244MM active accounts• 14 countries

Customers

• Personalized• What metric to optimize?• Milliseconds latency

Recommendation at scale

Page 6: Airstream use case Houssam Nassif AISTATS’15.

Temporal Variations

Time (in hours)

Val

ue (

clic

ks,

purc

hase

s)

Page 7: Airstream use case Houssam Nassif AISTATS’15.

Zero-Inflated DistributionC

usto

mer

s

Engagement

Page 8: Airstream use case Houssam Nassif AISTATS’15.

How to Recommend?

Multi-armed bandits

Collaborative filtering

Deep learning

Regression analysis …

Page 9: Airstream use case Houssam Nassif AISTATS’15.

Need for Diversity

Page 10: Airstream use case Houssam Nassif AISTATS’15.

How to Diversify?

Challenges• How to learn ideal mix?• How to balance between diversity and

metric of interest?

Possible solutions• Determinantal Point Processes • Submodular Functions

Page 11: Airstream use case Houssam Nassif AISTATS’15.

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