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?