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[Nearly] Instant Recommendations

(Nearly) instant recommendations

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Page 1: (Nearly) instant recommendations

[Nearly] Instant

Recommendations

Page 2: (Nearly) instant recommendations

Great Recommendation is Hard

Page 3: (Nearly) instant recommendations

Simple “Recommendation” is Easy

Page 4: (Nearly) instant recommendations

And Why Does This Matter Again?

Page 5: (Nearly) instant recommendations

“Personalizing” Our Recommendations

Page 6: (Nearly) instant recommendations

Examining Our Algorithm

Facebook

ESPN.com

NBA.com

Page 7: (Nearly) instant recommendations

What About Lift?

Page 8: (Nearly) instant recommendations

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

Page 9: (Nearly) instant recommendations

A Quick Peek Inside Looker

Page 10: (Nearly) instant recommendations

Finding a Sweet Spot

Popularity

(common stuff)

Discovery

(rare stuff)

Good

Recommendation

Page 11: (Nearly) instant recommendations

Our Quick Model

Page 12: (Nearly) instant recommendations

Applying Looker

Page 13: (Nearly) instant recommendations

Extending the Model

Page 14: (Nearly) instant recommendations

The Result

● Different Groupings

○ Users

○ Orders

● Different Actions

○ Purchase

○ View

● Different Items

○ Product / SKU

○ Brand

○ Department

Page 15: (Nearly) instant recommendations

Want More?

[email protected]

@lookerdata