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Blending Human Computing and Recommender Systems for Personalized Style Recommendations
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Transcript of Blending Human Computing and Recommender Systems for Personalized Style Recommendations
Blending Human Computing and Recommender Systems for Personalized Style Recommendations
Eric Colson | Recsys Conference | Oct 2014
Recommendation Engines
Different Capabilities
Find the Eigenvalues Find the angry dog
Data & Algorithms: our most important assets
• 35% of Amazon sales are driven from recommendations
• 50% of LinkedIn connections are driven by recommendations
• 75% of Netflix videos watched are from recommendations
• 100% of Stitch Fix merchandise is sold by recommendations
Data[c] = (size=’M’,
height=66,
age=31,
isMom=t,
occupation=‘Layer’,
city=‘Austin’,
shoulderFitPreference=’tight’,
hipFitPreference=’loose’,
preferredColorIds={628, 621, 417, 107},
pricePreferenceForDress=[50, 100),
pastPurchases={5008, 808, 11508, 2204, 3553},
profileNotes=‘I am a teacher. My clothes need to be appropriate for the
office administrators as well as for 3rd-graders’,
requestNote=’would love things that I could wear to work and then to date
night after’,
pinterestStylePage='http://pinterest.com/stitchfix/1234',
...
)
Diverse Compute Resources
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Machine Computation
Human Computation
Request Notes
Would love things that I could wear to work and then to date night after.
Stylists Notes
Hi Jillian,
Here is your new Fix! These selections will be great for both work and date night. They will also look great on your frame. The pants have a low rise and are fitted through the thighs. The top fits
1. Unstructured Data
2. Curation
3. Relationship
Leverage more data & processing
Scaling
Expert-Human Judgment – Fashion StylingTypically 3-5 years in retail/fashion/styling. Focus on contemporary and classic styles.
Summary
• Leverage more data & processing with diverse resources– Machines for structured data
– Expert-humans for unstructured data, curation, relationships
• Together they are better
• Together they get better … and better
Q’s?