Presented By
O’Reilly + Cloudera
Make Data Work
29 April–2 May 2019
London, UK

Learning "learning to rank"

Sophie Watson (Red Hat)
11:1511:55 Thursday, 2 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 15/16
Average rating: ****.
(4.10, 10 ratings)

Who is this presentation for?

  • Anyone who wants to learn about or implement learning-to-rank methodologies successfully

Level

Beginner

What you'll learn

  • Explore the tools needed to implement your own learning-to-rank algorithms

Description

Contemporary search engines are often good at finding all results for a given query but less so at identifying which of these are the best results and thus should be returned first. 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 and thereby return useful suggestions quickly and efficiently. Learning to rank can be implemented with machine learning models of varying complexity, from standard linear regression to gradient-boosted decision trees.

Sophie Watson outlines some standard learning-to-rank methods and illustrates them by applying them to a real search engine. She compares off-the-shelf methods and discusses some of the pitfalls she ran into when training a learning-to-rank model and shares simple tips to improve results from the off-the-shelf methods, enabling you to build a robust and effective search engine.

You’ll walk away 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 dataset.

Photo of Sophie Watson

Sophie Watson

Red Hat

Sophie Watson is a Senior Data Scientist in an Emerging Technology Group at Red Hat, where she applies her data science and statistics skills to solving business problems and informing next-generation infrastructure for intelligent application development. She has a background in mathematics and holds a PhD in Bayesian statistics, in which she developed algorithms to estimate intractable quantities quickly and accurately.