One of the technical deep-dive sessions that will take place at Lucene/Solr Revolution is “Enhancing Relevancy through Personalization and Semantic Search.” This session by Trey Grainger, Search Technology Development Manager at CareerBuilder, will teach you how to implement advanced relevancy techniques that enable your search platform to “learn” from your content and users’ behavior.
Topics will include automatic synonym discovery, latent semantic indexing, payload scoring, document-to-document searching, foreground vs. background corpus analysis for interesting term extraction, collaborative filtering, and mining user behavior to drive geographically and conceptually personalized search results.
During this session, you’ll learn how CareerBuilder has enhanced Solr (also utilizing Hadoop) to dynamically discover relationships between data and behavior, and how you can implement similar techniques to greatly enhance the relevancy of your search platform.
This advanced level session will take place from 10:35-11:50 on Thursday, November 7. Click here for more details.
About the Speaker:
Trey Grainger (@treygrainger) leads the Search Technology Development group at CareerBuilder.com and is the co-author Solr in Action, the new Solr book from Manning Publications. His search experience includes handling multi-lingual content across dozens of markets/languages, machine learning, algorithmic and user group based relevancy tuning, geo-spatial search and validation, and work on customized payload scoring models, data mining, clustering, and recommendations. His team operates a cloud search API handling hundreds of servers, thousands of queries per second, and nearly a billion unique documents. Trey is also the founder of Celiaccess.com, a gluten-free search engine, and is a frequent speaker at Lucene and Solr-related conferences, including multiple prior presentations at Lucene/Solr Revolution.
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Lucene/Solr Revolution is presented by Lucidworks, the commercial entity for Apache Lucene/Solr open source search — the future of search technology.
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