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Teaser Lingerie Recommendations with Van de Velde - Data Innovation Summit 2018 - Brussels

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Teaser and some slides for the presentation by Kevin Heyman, Data Scientist at Van de Velde and Nele Verbiest, Data Scientist at Python Predictions at Data Innovation Summit, June 27, 2018, Brussels. In this story, Kevin and Nele explain how they have build a recommendation engine to predict your next lingerie purchase

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Teaser Lingerie Recommendations with Van de Velde - Data Innovation Summit 2018 - Brussels

  1. 1. WE RECOMMEND YOUR NEXT LINGERIE PURCHASE KEVIN HEYMAN NELE VERBIEST JUNE 27th, Brussels di-summit.com
  2. 2. THE STORY
  3. 3. ?the challenge of this project lies in offering valuable product recom- mendations in a digital context
  4. 4. THE PROCESS
  5. 5. RECOMMENDATIONS a critical component of many recommendation systems is a similarity matrix, indicating similarity between items (=products)
  6. 6. PRIMADONNA SUMMER PRIMADONNA MEADOW Two bras are similar if they are bought by the same women. in this matrix, similarity can be defined as the overlap in the women who buy two products: large overlap = similar
  7. 7. Two bras are similar if they are bought by the same women. ANDRES SARDA - NEPTUNE MARIE JO L’AVENTURE - TOM in this matrix, similarity can be defined as the overlap in the women who buy two products: small overlap = unsimilar
  8. 8. using the similarity matrix to generate recommendations
  9. 9. we’ll use the similarity matrix to detect items most similar to each previous purchase, and assign points to each recommendation, so we can recommend the item with the highest total score
  10. 10. 1 2 4 5 6 8 9 7 10 3 OFFLINE EVALUATION this approach showed a significant improvement over recommending the most popular product (graph is accurate but exact figures are confidential)
  11. 11. MAIL TEST INCREASE IN CTO CONTROL TEST also in a real-life mail test, the recommendations proved to increase click-through rate significantly when compared to a control group offering the most popular product (graph is accurate but exact figures are confidential)
  12. 12. RECOMMENDATIONS CURRENT & OLD SEASON the previous approach only works for previous seasons, for which we have purchase data, but what if we don’t?
  13. 13. PRIMADONNA SUMMER PRIMADONNA MEADOW Two bras are similar if they are bought by the same women. ? MARIE JO ERIKA AW 2018 for a new season we don’t know who will purchase so we cannot calculate the overlap
  14. 14. PRIMADONNA DIVINE to solve this, we created a deep neural network to detect similarity between purchased items and the new collection we first trained this network to classify images from the new collection correctly
  15. 15. MARIE JO SAKURA next, we apply the network to previous purchases, so we use the model to predict similar items
  16. 16. MARIE JO - FLEUR MARIE JO L’AVENTURE - MAI example of a detected similarity: old season -> new season
  17. 17. PRIMADONNA TWIST – TOUGH GIRL PRIMADONNA TWIST - CABARET example of a detected similarity: old season -> new season
  18. 18. RECOMMENDATIONS NEW SEASON RECOMMENDATIONS CURRENT & OLD SEASON as a result, we are able to offer recommendations for existing seasons but also new seasons
  19. 19. WE RECOMMEND YOUR NEXT LINGERIE PURCHASE BUT WE DO MORE THAN THAT…

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