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Detection of fashion trends and seasonal cycles using client feedback

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Slides of my presentation at the Machine Learning meets Fashion workshop of the KDD 2016 conference. https://kddfashion2016.mybluemix.net/

Published in: Data & Analytics
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Detection of fashion trends and seasonal cycles using client feedback

  1. 1. Detection of fashion trends and seasonal cycles through client feedback KDD 2016 WORKSHOP: MACHINE LEARNING MEETS FASHION Roberto Sanchis-Ojeda, Daragh Sibley, Paolo Massimi
  2. 2. Contents 1. Intro 2. The normal approximation 3. Generalized linear mixed effect models 4. Application to Stitch Fix’s style feedback data
  3. 3. Client Ci Style Sj Feedback Yij Positive , Negative 1 , 0
  4. 4. 2. The normal approximation
  5. 5. Simple mathematical law … Sum of Bernoulli = Binomial Positive response ratio Sum of Bernoulli -> Gaussian pi = ∑j Yij / N Large N N = 10 N = 100
  6. 6. … that can be very helpful … p(time) = 0.7 - 0.2 * time p(time) = 0.5
  7. 7. … but certain assumptions break Length of every time interval ● Poor temporal resolution ● p no longer constant ● Few interactions, normal approximation breaks ● Slower computation Large Small
  8. 8. 3. Generalized linear mixed effect models
  9. 9. Categorical aggregation Bernoulli Feedback Yij 0 or 1 Binomial (N0 , N1 ) (34, 27) logit(p) ~ time + style_color + style_group Group by each feature to make sure that p is approximately constant within Binomial draw. Now time can be aggregated to an arbitrarily small time scale
  10. 10. Statistical methods with Bernoulli variables ● Pros: ○ Simple, flexible ○ Well studied technique ● Cons: ○ Large dataset ○ Large number of features ○ Scalability problems ● Pros: ○ Smaller dataset ○ Faster computation ○ Natural regularization that helps with non-uniform data ● Cons: ○ Requires a more complex ETL and analysis process. Logistic Regression Models Generalized Linear Mixed Models
  11. 11. Simulating linear fashion trends 1000 random styles Si in inventory Interacting with a large uniform set of clients 3 interactions per day for two years with probability pi pi = pi,o + mi * time pi,o ~ N(0.6, 0.1) mi ~ U(-0.1, 0.1)
  12. 12. A GLMM linear trend classifier logit(p) ~ X + Z + X and Z have an offset and time as features There is a slope per style id, with 95% CI Out of fashion CI all negative Trending CI all positive
  13. 13. The results
  14. 14. The results 1 2 3
  15. 15. Simulating cyclical seasonal trends 1000 random styles Si in inventory Interacting with a large uniform set of clients 3 interactions per day for two years with probability pi pi = pi,o + Ai * cos(2 (time - t0 )) pi,o ~ N(0.6, 0.1) Ai ~ U(0, 0.1) t0 ~ U(0, 1)
  16. 16. The results
  17. 17. The results 1 2
  18. 18. 4. Application to Stitch Fix’s style feedback data
  19. 19. Discovering cyclical seasonal trends Thousands of real styles Si in inventory Interacting with a large uniform set of clients Use the style feedback as a probe for seasonality
  20. 20. One great example of seasonal style Jan Apr July Oct Dec
  21. 21. Conclusions ● Defining client feedback as a binary variable simplifies the statistical analysis of trends ● The normal approximation is a useful tool but lacks the right level of flexibility, and its assumptions are easily broken. ● Binomial data can be fit with generalized linear mixed effect models, and the random effect coefficients can be used to classify trends on styles. ● Our application to Stitch Fix data proves that the method has real business applications.
  22. 22. Examples of binarized feedback ● Website feedback: ○ No Click on Picture = Negative = 0 ○ Click on Picture = Positive = 1 ● Style feedback: ○ (Hate it, Just ok) = Negative = 0 ○ (Like it, Love it) = Positive = 1 ● Numerical feedback 1, … , N: ○ 1, … , N/2 = Negative = 0 ○ N/2, … , N = Positive = 1
  23. 23. Linearizing the cosine term pi = pi,o + Ai * cos( 2 ( time - t0 ) ) cos( - ) = cos( ) * cos( ) + sin( ) * sin( ) pi = pi,o + Bi * cos( 2 * time ) + Ci * sin( 2 * time )
  24. 24. A GLMM seasonal trend classifier logit(p) ~ X + Z + X and Z have an offset and cosine and sine of 2 by time as features There are two temporal coefficients per style id, with 95% CI Non-seasonal CI all comp. with 0 Any other case Seasonal

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