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We go over how we extended Solr’s JSON Facet API to return large amounts of data and reports for a client’s use case.
This post will show you precisely how BM25 builds upon TF-IDF, what its parameters do, and why it is so effective.
KMW hosted the Boston Elasticsearch meetup group, and our own Rudi Seitz gave an in-depth talk about how relevancy works in a search engine. In this post learn about when he covered, including: how scoring is computed, query structure and analysis chains, and approaches to tuning relevancy.
Instead of delving into the mathematical definitions of TF-IDF and BM25, we will help you develop an intuitive understanding of these metrics using a series of simple examples.
KMW Founder, Kevin Watters, gives a talk at the Activate Search & AI conference on using Search as the brain for an open sourced, life-sized, 3D printed, humanoid robot. Of particular interest to the Solr community was the section of this talk highlighting training a Neural Network off of a Solr index in real-time.
Customer issues addressed by a KMW Solr Audit Include:
-Cluster instability
-Query throughput and latency issues
-Relevancy issues / poor recall or precision
-Ingestion / index latency concerns
-Planning for a Solr upgrade or major platform update
-Problematic Hardware/cluster sizing and/or scaling issues
-Defunct operational practices
Then: Enterprises spend a lot of time to make their content well-curated so that it can be exposed in a search engine.