Haystack is the conference for organizations where search, matching, and relevance really matters to the bottom line. For search managers, developers, relevance engineers & data scientists finding ways to innovate, see past the silver bullets, and share what actually has worked well for their unique problems
Date: 21th May 2020
How to Build your Training Set for a Learning to Rank Project
Presented by Alessandro Benedetti of Sease, Learning to Rank (LTR) is the application of machine learning techniques (typically supervised), in the formulation of ranking models for information retrieval systems.
With LTR becoming more and more popular, organizations struggle with the problem of how to collect and structure relevance signals necessary to train their ranking models.
This talk is a technical guide to explore and master various techniques to generate your training set(s) correctly and efficiently.
Expect to learn how to :
model and collect the necessary feedback from the users (implicit or explicit)
calculate for each training sample a relevance label that is meaningful and not ambiguous (Click Through Rate, Sales Rate …)
transform the raw data collected in an effective training set (in the numerical vector format most of the LTR training libraries expect)