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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.

avatar for Sophie Watson

Sophie Watson

Software Engineer, Red Hat, Inc
Sophie is a software engineer at Red Hat, where she works in an emerging technology group. She has a background in Mathematics and has recently completed a PhD in Bayesian statistics, in which she developed algorithms to estimate intractable quantities quickly and accurately. Since... Read More →

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

Attendees (36)