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Adaptive, Personalized Diversity for
Visual Discovery Daniel Hill Amazon Personalization Sciences Palo Alto, CA I’m going to be talking about Adaptive, Personalized Diversity for Visual discovery. This is a joint work with Houssam, Daniel, Sriram, Mitchell, Vijai and Vishy. Houssam is from Core Machine Learning while the rest of us are from the personalization team.
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Here is what Airstream looks like when you hit the URL or click on the top banner on any Amazon.com retail pages when you are logged in. [start animation] Airstream is a new product discovery experience designed to show customer a large collection of products in an endless beautiful imagery. In this talk, I’ll describe our solutions for overcoming the challenges we faced with.
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Beyond Accuracy: Challenges for visual browsing
Freshness Surface new products without degrading stream quality Diversity Create visual interest Hedge against uncertainty in customer intent Seasonality Adapt quickly to trends in fashion Personalization Increase relevance to customer’s style RecSys 2016
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Beyond Accuracy: Challenges for visual browsing
Freshness Surface new products without degrading stream quality Diversity Create visual interest Hedge against uncertainty in customer intent Seasonality Adapt quickly to trends in fashion Personalization Increase relevance to customer’s style RecSys 2016
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Beyond Accuracy: Challenges for visual browsing
Freshness Surface new products without degrading stream quality Diversity Create visual interest Hedge against uncertainty in customer intent Seasonality Adapt quickly to trends in fashion Personalization Increase relevance to customer’s style RecSys 2016
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Beyond Accuracy: Challenges for visual browsing
Freshness Surface new products without degrading stream quality Diversity Create visual interest Hedge against uncertainty in customer intent Seasonality Adapt quickly to trends in fashion Personalization Increase relevance to customer’s style RecSys 2016
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Explore-exploit in product scoring
Product ID Brand Price Category Jacket Calvin Klein $98 Bayesian Linear Probit Regression Click or No-click Product attributes, x Attribute weight distributions, w Customer actions Thompson Sampling Product Score = F( Σi wi xi ) Wi ~ N(0,1) RecSys 2016
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Ranking by product score alone
RecSys 2016
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Diversification applied to product categories
Category: (Department, Product Type, Price Range) Submodular utility womens-dress-high Stream utility RecSys 2016
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Submodular utility function using adaptive category weights
utility(D) = Σk wk log[1 + c(k, D) ] + s(k,D) Weight for category k Number of products in category k Total score of products in category k RecSys 2016
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Submodular utility function using adaptive category weights
utility(D) = Σk wk log[1 + c(k, D) ] + s(k,D) Weight for category k Number of products in category k Total score of products in category k Category weights are smoothed click-through-rates over a rolling-window wk = (clicksk + αk) / (viewsk + αk + βk) RecSys 2016
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Personalization of category weights from customer actions
Customer clicks cu ~ Multinomial(wu) weights, wu ~ Dirichlet(w) Personalized weights, E[wu] = (cu + w) / |cu + w|1 RecSys 2016
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Diffusion of customer preferences via category correlations
× Customer preference vector Category-category correlations Smoothed customer preference vector RecSys 2016
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Live experimental results
Component Lift in product clicks Submodular vs. proportional diversity +2.2% Adaptive category weights +10.4% Personalized category weights +6.1% RecSys 2016
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Thank you! Daniel Hill Amazon Personalization Sciences Palo Alto, CA
Choon Hui Teo Houssam Nassif Sriram Srinavasan Mitchell Goodman Vijai Mohan S. V. N. Vishwanathan RecSys 2016
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RecSys 2016
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Stream ranker workflow
Event Logs Click Model Updater Seasonal Weights Category Diffusion Matrix Catalog Ranked stream Personalized Weights Diversified stream Rediversifier Browser RecSys 2016
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Category-category Correlations
Diffuse preference for category A to category B based on the correlation between A and B “clicked A also clicked B” RecSys 2016
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