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Amazon A9
The Search Engine made by Customer Experience
Concept of Amazon & A9
NPL
Machine Learning
Abstract
- Small improvements in relevance rankingboth positively influence the shopping
experience of customersand significantly impact revenue.
- A number of relevance algorithmsused in Amazon Search.
- A general machine learning frameworkused for ranking within categories, blending
separate rankings in All product Search.
- NLP techniquesused for matching queries and product and algorithmtargeted at unique tasks of
specific categories
e.g., book, fashion.
Ranking Models
- Ranking models are responsible for a function that, given a customer’s query,
returns a sorted list of products in a match set.
- A single ranking model usually covers a combination of a category and a marketplace, e.g., Books in Japan.
- For training the ranking modelswe use labels based on customer’s actions
e.g., purchases, add-to-basket, clicks
- search engine collect the training sets.
- Several times per day, compute the unique set of keywords issued for each context of
interest. - The context can be a combination of marketplace, category, and some user features
- re-issue these queriesin their context requesting feature valuesfor all items in the match set.
Ranking Models
- This feature collection runs regularly, so that the feature vector collected is as close as possible to the one observed when the query was originally
issued by customers.
- construct training, validation, and test sets by collecting data from several days of customer traffic.
positiveexample impression : click, purchase
negativeexample impression : seen,corresponding to items which were displayed to a customer, and unseen, corresponding to
items which matched the query terms but were never shown due to pagination.
- Method of choice in Ranking : Gradient boosted tree, nDCG
https://en.wikipedia.org/wiki/Gradient_boosting https://en.wikipedia.org/wiki/Discounted_cumulative_gain
Behavioral Features
- When training ranking models we use many features
intrinsic properties of products
e.g., sales, reviews
reflect properties of the queries or the context in which the query is issued
e.g., query specificity, customer status
Matching Products and Queries
- Major problem in understanding queries in product search is determining
whether a word or phrase.
e.g., product type
- Probabilistic context-free grammer(PCFG)
- Unsuperviesed manner using variational bayes.
https://en.wikipedia.org/wiki/Variational_Bayesian_methods

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Amazon a9

  • 1. Amazon A9 The Search Engine made by Customer Experience
  • 2. Concept of Amazon & A9 NPL Machine Learning
  • 3. Abstract - Small improvements in relevance rankingboth positively influence the shopping experience of customersand significantly impact revenue. - A number of relevance algorithmsused in Amazon Search. - A general machine learning frameworkused for ranking within categories, blending separate rankings in All product Search. - NLP techniquesused for matching queries and product and algorithmtargeted at unique tasks of specific categories e.g., book, fashion.
  • 4. Ranking Models - Ranking models are responsible for a function that, given a customer’s query, returns a sorted list of products in a match set. - A single ranking model usually covers a combination of a category and a marketplace, e.g., Books in Japan. - For training the ranking modelswe use labels based on customer’s actions e.g., purchases, add-to-basket, clicks - search engine collect the training sets. - Several times per day, compute the unique set of keywords issued for each context of interest. - The context can be a combination of marketplace, category, and some user features - re-issue these queriesin their context requesting feature valuesfor all items in the match set.
  • 5. Ranking Models - This feature collection runs regularly, so that the feature vector collected is as close as possible to the one observed when the query was originally issued by customers. - construct training, validation, and test sets by collecting data from several days of customer traffic. positiveexample impression : click, purchase negativeexample impression : seen,corresponding to items which were displayed to a customer, and unseen, corresponding to items which matched the query terms but were never shown due to pagination. - Method of choice in Ranking : Gradient boosted tree, nDCG https://en.wikipedia.org/wiki/Gradient_boosting https://en.wikipedia.org/wiki/Discounted_cumulative_gain
  • 6. Behavioral Features - When training ranking models we use many features intrinsic properties of products e.g., sales, reviews reflect properties of the queries or the context in which the query is issued e.g., query specificity, customer status
  • 7. Matching Products and Queries - Major problem in understanding queries in product search is determining whether a word or phrase. e.g., product type - Probabilistic context-free grammer(PCFG) - Unsuperviesed manner using variational bayes. https://en.wikipedia.org/wiki/Variational_Bayesian_methods

Editor's Notes

  1. NLP : Natural language processing 자연어 처리
  2. re-issue = publish = feedback
  3. intrinsic properties : 내재적 속성