Download as PDF, PPTX
Uploaded byAndrei Lopatenko
Machine Learning for retail and ecommerce
This document discusses how machine learning can be applied to ecommerce and retail applications. It outlines several problems that ML can address, including search ranking, typeahead, spell correction, cold start recommendations, left-hand navigation, query understanding, related searches, product discovery, image similarity, voice search, attribute extraction, user modeling, title generation, and inventory management. It also provides context on data sizes, user behaviors, and the need for models to have fast prediction speeds and work within memory constraints in a production setting.
More Related Content
Similar to Machine Learning for retail and ecommerce
![[DSC DACH 24] Can AI make e-commerce and online marketplaces become more huma...](https://cdn.slidesharecdn.com/ss_thumbnails/mladenfernezir-240918230939-c77cef56-thumbnail.jpg?width=640&height=640&fit=bounds)
Machine Learning for retail and ecommerce
- 1. 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) andrei@recruit.ai
- 2. 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
- 3.
- 4.
- 5.
- 6.
- 7.
- 8.
- 9. 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
- 10. Search – usersearches • 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)
- 11. 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
- 12. 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
- 13. ML addressable problems •Spell correction • Given a user query, what’s the query user actually wanted to type
- 14. 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
- 15. 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
- 16. 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
- 17. 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
- 18. 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
- 19. 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
- 20. 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?”
- 21. 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
- 22. ML addressable problems •Representations of users : actions on websites/apps -> searches, clicks, browsing behaviour, product -> purchase preferences, reviews, ratings, return rates
- 23. ML addressable problems •title generation: how to generate the title which will cause maximum conversion rate • which product attributes select for the title?
- 24. What makes agood title?
- 25. What makes agood title?
- 26. Limits • Most modelsshould be served in production • 50ms on prediction • Part of big system, memory limits ~ 10G
- 27.
- 28. Retail • Key directionswhich require machine learning: • discounting tools • coupons and rewards • loyalty • inventory management
- 29. Inventory management • Customerwant 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?
- 30.