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Saturday, January 26 • 5:00pm - 5:25pm
Learning "Learning to Rank"

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Excellent recall is insufficient for useful search; search engines also need to identify the most relevant results in a sea of matches. Learning to Rank algorithms aim to capture the relative utility of search results so as to return useful suggestions quickly and efficiently.
In this introductory talk Sophie will explain some Learning to Rank methods, from standard linear regression, to gradient-boosted decision trees, and apply them to a real search engine. She will compare the methods and discuss the pitfalls she ran into when training a Learning to Rank model.
You will walk away from this talk with an understanding of the problems involved in relevant search, an overview of key techniques, and the knowledge needed to implement Learning to Rank algorithms on your own data set.

Speakers
avatar for Sophie Watson

Sophie Watson

Senior Data Scientist, Red Hat
Sophie Watson is a data scientist at Red Hat, where she helps customers use machine learning to solve business problems in the hybrid cloud. She is a frequent public speaker on topics including machine learning workflows on Kubernetes, recommendation engines, and machine learning... Read More →



Saturday January 26, 2019 5:00pm - 5:25pm CET
D105