Retaining Customers
In product meetings, when I advocate for customer retention – regular, repeat business from the customer over months/years – I sometimes get blank stares. Teams want to do everything…
Roll your own search analytics
I recently completed a fairly straight-forward search project in which we were replacing a legacysystem with Solr. The goal for the first release was to just make sure that…
Why We’re Doing a Search Relevance Conference
Our mission is to empower the world’s best search teams. Search teams ultimately generate value for their organizations through better, smarter search. That is: relevance. Sadly relevance remains maddening!…
Solr Synonyms and Taxonomies: Mea Culpa
I screwed up! Mea Culpa. Let he who is without synonyms throw the first rock! In my talk Taxonomical Semantical Magical Search, I presented an index-time hypernym/hyponym expansion solution….
Vespa vs Lucene: First Impressions
As we learn more about Vespa, we wanted to give our initial impressions when comparing to Lucene-based search (Solr/Elasticsearch). This is based on initial passes with Vespa and our…
Elasticsearch Learning to Rank 1.0 RC1 Released
We reached a major milestone over the weekend! We released the first release candidate for version 1.0 of Elasticsearch Learning to Rank. While our partners Wikimedia and Snagajob are…
Solving The Search Management Minefield in Open Source Search
A common theme in software development is the tension between: Fixing painful bugs now!! Creating tools & practices that let us fix bugs faster (though at some theoretical time…
How is search different than other machine learning problems?
Learning to Rank turns search into a machine learning problem. In this article, I want to explore what makes search distinct from other machine learning problems. How does one…
Three Steps To Take Before Replacing Your Enterprise Search Solution
Replacing an enterprise search product? Use this three-step process to create actionable requirements for your team. The inherent challenge in replacing existing enterprise products is that their functionality and…
Caching In Elasticsearch
Responding to queries takes CPU time, memory, and in unfortunate cases, wall time as well. Increasing the power of a cluster helps, over-provisioning can be very expensive. Caching is…
Is Your Infrastructure Ready For Learning To Rank?
In previous articles, we introduced Learning to Rank (LTR) for Elasticsearch 1, 2, which uses machine learning to re-score the best documents in a query. On the surface, it…
Optimizing User-Product Matching Marketplaces
The key to really understanding search, recommendations, and other ‘product matching’ systems is microeconomics. Say what!?!? No hear me out. In this article, I want to challenge you to…