Dressipi - Personalised recommendation engine for fashion consumers
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Transcript of Dressipi - Personalised recommendation engine for fashion consumers
Dressipi● Online Style Advice Service● B2B● Enables personalization throughout partner site
Examples● Product Recommendations
Examples● Outfit Recommendations
Examples● Personalised Emails
Examples● Style Advice
CompetitorsVarious to different degrees:● B2B recommendation plugins: Rich Relevance, Peerius, Monetate, …● B2C Online Styling Service: StitchFix, Outfittery, ...● Internal Teams: ASOS, Zalando, Amazon Fashion, …
Recommender SystemHottest domains in Recommender Systems
What makes Fashion domain different?New Item Problem is very pronounced
● Short lifetime of items: most garments are only available to buy for a couple of months
● New garments arrive frequently● Trends are quick and have big influence
What makes Fashion domain different?Expert Knowledge is required
● There are “objective” do’s and don’ts around colours, body shape, fit etc
● Customers don’t always know what fits them - can’t blindly trust behavioral data
● Customers sometimes want a style change - this means they want the opposite of what we would traditionally learn from their behavioural data!
Data available to us● User Features● Garment Features● Interaction Data
Data - User Features● Colours● Body Shape● Personality
Data - Garment Features
Data - How it fits together
How do we build recommenders?● We develop recommender algorithms for the fashion domain● Real stylists● Links to academia● Rigorous Evaluation
Outfits - what and why● A set of products that all go together & is “complete”● Needs to take into account single product recommendations, item
level preferences
Outfits - it’s complicated● Not just frequently bought together / also bought this● Relationship between products is more complex● “When cardigan is style: Bolero/Shrug, only style with dresses and
tops with sleeve length: sleeveless or short sleeve”
Outfits ● MVP is much harder than single product● Requires expert advice● Many open questions & directions for future exploration
What’s next?
● Omni-channel● More retailers● Beyond purchases
What makes Dressipi awesome?● ML for Fashion is a new and exciting field● Wide open for innovation● We develop algorithms from scratch!● We compete with big established companies!● We measure improvement through A/B tests!
Questions?
