Blending Human Computing and Recommender Systems for Personalized Style Recommendations
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.
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Blending Human Computing and Recommender Systems for Personalized Style Recommendations
- 1. Blending Human Computing and Recommender Systems for Personalized Style Recommendations Eric Colson | Recsys Conference | Oct 2014
- 3. Different Capabilities Find the Eigenvalues Find the angry dog
- 5. 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
- 6. 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', ... )
- 8. Machine Computation a2 a3 a4 a5 a6 a7 a8 a1 λ1 λ2
- 9. Human Computation 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 Request Notes Would love things that I could wear to work and then to date night after. 1. Unstructured Data 2. Curation 3. Relationship
- 10. Leverage more data & processing
- 11. Scaling
- 12. Expert-Human Judgment – Fashion Styling Typically 3-5 years in retail/fashion/styling. Focus on contemporary and classic styles.
- 13. 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
- 14. Q’s?