(nearly) instant recommendations
TRANSCRIPT
[Nearly] Instant
Recommendations
Great Recommendation is Hard
Simple “Recommendation” is Easy
And Why Does This Matter Again?
“Personalizing” Our Recommendations
Examining Our Algorithm
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
