blending human computing and recommender systems for personalized style recommendations
DESCRIPTION
Presented at ACM RecSys 2014 Machine algorithms are great for tasks that require processing of large amounts of objective and structured data. However, they have difficulty with tasks that are relatively simple for skilled humans – For example, interpreting concepts in an image, or discerning tone in language, ..etc. Yet, there is a class of problems that call for precisely the combination of these tasks. This concept of human-assisted algorithmic processing is not new. It is inherent to many processes that we are familiar with. However, there are very few systems that embrace humans and machines as two resources within a single system. Instead, they are often independent and non-collaborating agents. In this talk, we explain how a single task-processing system can be architected to use diverse resources: be they human or machine. Such a system not only better utilizes each resource, but also produces better results and gets better with experience.TRANSCRIPT
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
a2
a3
a4
a5a6
a7
a8
a1
λ1
λ2
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?