Date post: | 15-Apr-2017 |
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Technology |
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Machine Learning (ML) for eCommerce and Retail
Dr. Andrei Lopatenko Director of Engineering,
Recruit Institute of Technology Recruit Holdings
former Walmart Labs, Google (twice), Apple (twice) [email protected]
ML for eCommerce
• Search, Browse, for commerce sites and application
• Help users to find and discover items they will purchase
• Maximize revenue/profit per user session
Search
Search - ranking
ranking
Search - LHN
Left Hand
Navigation
Search spell correction
Search type ahead
Browse
Search data size
• Catalogue items • 8 M items now compare ~ 400 M
Amazon / eBay • X 10 in near future • 2 K text description per item + images • Several hundreds of structured attributes
per catalog
Search – user searches
• Tens of millions per day • Tens billions session per year • Online sales 13.2 B per year (http://
fortune.com/2015/11/17/walmart-ecommerce/)
• 500B per year sales offline stories (8% USA economy) in ~ 11K stores
• The number of transactions ~ 10B (public data)
ML addressable problems
• Learning to rank • Given a query, what’s the list of items
with the highest probability of conversion (purchase), ATC (add to card), page view
ML addressable problems
• Typeahead • Given a sequence of characters types by
user, what’s most probably competitions, what are most probable items users wants to buy
ML addressable problems
• Spell correction • Given a user query, what’s the query user
actually wanted to type
ML addressable problems
• Cold start • Given a new items with it’s set of
attributes and no history of sales or exposure on site, predict items sales and item sales per query
ML addressable problems
• Prediction of LHN • Given a user query, what’s the best set of
facet and facet values, which gives higher probability of users interacting with them and finally buying an item
ML addressable problems
• Query understanding • Given a query, build a semantic parse of
query, tag tokens with attributes: blue tshirts for teenagers -> blue:color tshirts:type for:opt teenagers:agerestriction10-20
• Classification: blue tshirts for teenagers: -> type:apparel, price preference: 10-30, releaseyearpreference: 2014-2016
ML addressable problems
• Related searches • Given a query, what are queries which are
either semantically close to this one, or represent coincidental users interests
• Nike shoes -> adidas shoes, sport shoes, • Coffee mugs -> travel mugs, photo coffee
mugs, cappuccino cups
ML addressable problems
• product discovery • help users to explore product assortment, • drive users to diverse products • reduce risk of selecting irrelevant items • help to find price,quality,brand etc
alternatives • reduce pigeonhole risk • provide relevant data to make a decision
ML addressable problems
• Image similarity • Given images of the items, give other
items such that images of those are visually appealing to the users which like the original item (appealing by shape? Color? Texture?) -> causing high conversion in recommendation
ML addressable problems
• Voice search • Given voice input, reply with a list of the
best items • “what are the cheapest samsung tvs in the
store” • “what is best deal on queen bed today?”
ML addressable problems
• extraction of item attributes • Given an item: what are item attributes:
brand, color, size (wheel, screen, height, S/M/XL, Queen/Twin/King/Full), Gender, Pattern, Shape, Features
ML addressable problems
• Representations of users : actions on websites/apps -> searches, clicks, browsing behaviour, product -> purchase preferences, reviews, ratings, return rates
ML addressable problems
• title generation: how to generate the title which will cause maximum conversion rate
• which product attributes select for the title?
What makes a good title?
What makes a good title?
Limits
• Most models should be served in production
• 50ms on prediction • Part of big system, memory limits ~ 10G
Retail
Retail
• Key directions which require machine learning:
• discounting tools • coupons and rewards • loyalty • inventory management
Inventory management
• Customer want to buy products • Customers have diverse needs • Products should be in stock, ideally in
warehouses close to customers • but it’s expensive to store products • Problem: How many products of each type
should be stored, when product supply should be refilled?