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Building Recommender Systems for Fashion

Industry talk at RecSys 2017 by Nick Landia from Dressipi

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Building Recommender Systems for Fashion

  1. 1. Building Recommender Systems for Fashion Nick Landia Dressipi @dressipi
  2. 2. Dressipi • Fashion Recommendations & Style Advice • Going for 5 years • 25 employees • B2B
  3. 3. In this talk • Fashion Domain Characteristics • Advice & Recommendation Reasons
  4. 4. Fashion Domain Items: “Fast Fashion” • Short lifetime of items: a couple of months on average • Large retailers release new garments daily
  5. 5. Fashion Domain Users • Taste changes over time • Seasons complicate things • Trends can drastically change user preferences very quickly!
  6. 6. Fashion Domain What this means for recommenders • Item catalogue changes rapidly • High sparsity • Use content data • We need recommenders that can recognise changes in user preference and respond quickly
  7. 7. Advice & Recommendation Reasons Why do we need an advice component • Users are looking for guidance and validation that something will or will not work for them • Users might not know what fits them best • Fashion confidence is a big factor – out of comfort zone recommendations
  8. 8. Advice & Recommendation Reasons Challenges • Can’t blindly trust historical interaction data • Need additional information on users and garments • Need expert knowledge • Need to communicate in a user-understandable way
  9. 9. Advice & Recommendation Reasons How we do this • Users fill out questionnaire • We label garments • Stylists encode fashion rules
  10. 10. Style Rules
  11. 11. “Your key shapes: The key to dressing your slim silhouette is to disguise your broad shoulders. Clothes that add flattering volume to your lower half help to balance out your proportions. Look for lower necklines that soften you shoulder line. Avoid fussy shoulder details that add bulk to this area.”
  12. 12. Advice & Recommendation Reasons • Recommendation reasons have to make sense to the user • The point of reasons is not to justify “why are you showing me this”, it is to answer “why is this item good for me” • Give useful information to the user
  13. 13. Summary • Item catalogue and user preferences change over time, faster than in other domains • It’s not just about product recommendations, users want advice • Make recommendation reasons useful to the user • User-Item interaction data alone is not enough
  14. 14. Learnings • Start from domain rather than algorithm • Human-understandable features are great! • allow for applications that are useful to the user, i.e. advice and recommendation reasons • allow for more involvement of domain experts and more rapid iteration on algorithm development
  15. 15. Thanks! • We are hiring! • Open for collaboration with academia! •