(Nearly) instant recommendations

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[Nearly] Instant

Recommendations

Great Recommendation is Hard

Simple “Recommendation” is Easy

And Why Does This Matter Again?

“Personalizing” Our Recommendations

Examining Our Algorithm

Facebook

ESPN.com

NBA.com

What About Lift?

Weaknesses Here Too

ESPN.com5,000 / 100,000

(5%)

NBA.com5,000 / 100,000

(5%)

ESPNBoston.com10 / 100,000

(0.01%)

ESPN.com5,000 / 5,000

(100%)

NBA.com1,000 / 5,000

(20%)

ESPNBoston.com10 / 5,000

(0.2%)

Among ESPN Visitors:Overall Frequency:

20x

4x

A Quick Peek Inside Looker

Finding a Sweet Spot

Popularity

(common stuff)

Discovery

(rare stuff)

Good

Recommendation

Our Quick Model

Applying Looker

Extending the Model

The Result

● Different Groupings

○ Users

○ Orders

● Different Actions

○ Purchase

○ View

● Different Items

○ Product / SKU

○ Brand

○ Department

Want More?

colin@looker.com

@lookerdata

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