We wrote the book
Good search understands a user's intent, not just what they type.
We wrote Relevant Search to help teams build smarter Elasticsearch and Solr applications.
One of the best and most engaging technical books I've ever read. — Trey Grainger, Lucidworks
Will help you solve real-world search relevance problems for Lucene-based search engines — Dimitrios Kouizes-Loukas, Bloomberg
Pioneers in machine learning
We built Elasticsearch Learning to Rank, which powers search at Yelp, Wikipedia, Snag, and others.
Since deploying learning to rank, we’ve seen a net 32% increase in conversion metrics across our historically lowest performing use-cases. — Jason Kowalewski, Sr Director of Engineering at Snag
Learning to rank can’t stay hidden away in obscure academic journals or multi-million dollar products. We’re making this stuff accessible to all search teams: opening up the black box for everyone to use”. — Doug Turnbull - OpenSource Connections CTO
We have the tools
We built Quepid, a search testing platform that takes the guesswork out of search relevancy.
Do NOT attempt a search project without it. — John Bickerstaff
Just want to understand why results are showing up in your search?
We support the community
We host a Slack Channel where practitioners support each other and share solutions.
The blog and other happenings
A relevant search result helps users take further steps, make good decisions, and supports their ability to formulate better, more informed, queries.
In which we deal with learning that sometimes you don't get to use the latest version of Tesseract...
In this post, we unwrap the mystery behind two popular search relevance metrics, and discuss their pros and cons. Our subjects for this exercise are Normalized Discounted Cumulative Gain, and Expected Reciprocal Rank, commonly acronymified as nDCG and ERR. We'll start with some refresher background, visualize what these metrics actually look like, and paint a picture of how each can be either helpful or misleading, depending on the situation. Afterwards, you'll have a better understanding of their behavior and which ones to use when (and why).
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