We share our thoughts on the Lucidwork's Activate 2018 conference
I was privileged to participate in Haystack EU 2018 - relevance engineering as a discipline is here to stay, and just about every search & discovery team is scrambling to understand this new discipline.
This search stuff sounds like work - won't AI just solve all these problems soon?
Explore key considerations for leveraging your existing data in conjunction with open third-party content in order to extract valuable insights through methods of analysis and visualization.
In 2012 I first saw OpenNLP, and was both excited by it, but also appalled by the documentation. I wrote this blog post in 2012, but turns out I never actually published it! So here it is, updated for OpenNLP 1.9.0!
Learn to improve search quality from the best experts in the field. From TFIDF, to Semantic Search, to Learning to Rank with Solr
We share our thoughts on the Solr OpenNLP Integrations
Learn to improve search quality from the best experts in the field. From TFIDF, to Semantic Search, to Learning to Rank with Solr and Elasticsearch
We share our attempts to implement partial phrase highlighting in Solr
Your precious data is typically an ugly unpolished gem. Enterprise Data World was all about how to polish it up! Some thoughts from a Relevancy perspective.
Will search relevance be revolutionized by deep learning? Join Doug Turnbull, author of 'Relevant Search', and Tommaso Teofili, author of 'Deep Learning for Search' as we explore the budding field of Neural Search.
I love Solr and Elasticsearch, BUT...
Learning to Rank (LTR) is now available for both Solr and Elasticsearch. Why is this such a hot topic? What does an organization need to leverage a Learning to Rank solution? Liz explains the LTR pipeline in terms of what is available as an off-the-shelf solution and what isn't. She discusses the challenges faced when implementing LTR and some open research areas moving forward.
Exploring a method of search relevance testing that doesnt suffer from drawbacks of clickstreams or expert user judgments
Haystack is the conference for improving search relevance. Haystack is the no-holds-barred conference for organizations where search, matching, and relevance really matters to the bottom line.
A guide on how to implement, test, and deploy a Normalized Discounted Cumulative Gain (NDCG) ranking quality scorer in Quepid.
Want to hear how the next generation of digital solutions are applying AI and Machine Intelligence to create intelligent applications, like Chatbots and Virtual Assistants?
We document some of the behaviors behind Solr's relatively new sow=false strategy for parsing queries and dealing with query-time synonyms
The agenda for Haystack - the search relevance talk - has been announced!
Elastic Cloud users rejoice! Elasticsearch LTR 1.0.1 released with support for XPack Security added.
Optimizing products to build real brand fanatics - beyond just 'conversions'.
Search metrics are crucial, and they're easy to do.
Why are we hosting this crazy Haystack conference? We want to bring together search relevance practitioners to discuss their hardest technical problems.
In my LuceneRevolution talk I neglected to discuss how SynonymQuery impacted index time semantic expansion. I discuss that oversight in detail here, with work arounds
Overview, prerequisites, and implementation details of getting started with Learning To Rank. Why relevancy tuning will be taken over by machine learning, but not yet.
Our first impressions of Vespa compared to Solr or Elasticsearch
We've released Elasticsearch Learning to Rank 1.0 RC1, which brings machine learning capabilities to Elasticsearch relevance ranking. Beginning the March to fully releasing version 1.0.
*The* conference focused on Solr, we've made the pilgrimage since 2010
Don't be fooled by fancy technology: search is fundamentally a business problem; not a technical one. Technologists and domain experts need to establish careful contracts for managing search together.
In this article, I want to explore what makes search distinct from classic machine learning problems by giving you a tour of some different approaches.
Replacing an enterprise search product? Use this three-step process to create actionable requirements for your team before you start.
What caching does Elasticsearch support, and what is the best way to take advantage of it?
The success of a machine learning project will be dependent on the quality and quantity of the data available.
We explore the parallels between search, recommendation systems and microeconomics. Proposing a new way to think of search relevance and recsys as a system of maximizing the utility of multiple buyers. Is search and discovery akin to quantitative finance in its potential for optimization?