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
Location: online
Date: 24th March 2022
our talk
Online Testing Learning to Rank with Solr Interleaving
Learning to Rank (LTR) is the application of machine learning in the training of ranking models for search systems. It has been contributed to Apache Solr in version 6.4.0 back in 2017. Organizations struggle with the problem of how to test online different models to identify the best for their users. Interleaving is an online evaluation approach that compares ranking functions by mixing their results and interpreting the users’ implicit feedback(such as clicks). This talk describes the contribution of the Interleaving capability to the Learning To Rank module in Apache Solr (from 8.8.0) and explains how to use it. You will learn:
- how Interleaving approaches work, with a focus on Team-Draft Interleaving
- how the Apache Solr implementation works
- how to use it to compare ranking models and also the original score
- how to collect and evaluate users’ interactions with the interleaved list to identify the best performing model
Join us as we explore this new Apache Solr feature!
our speaker
Alessandro Benedetti
FOUNDER @ SEASE
APACHE LUCENE/SOLR COMMITTER
APACHE SOLR PMC MEMBER
Senior Search Software Engineer, his focus is on R&D in Information Retrieval, Information Extraction, Natural Language Processing, and Machine Learning.
He firmly believes in Open Source as a way to build a bridge between Academia and Industry and facilitate the progress of applied research.
