What if your search engine could automatically tune its own domain-specific relevancy model? What if it could learn the important phrases and topics within your domain, automatically identify alternate spellings (synonyms, acronyms, and related phrases) and disambiguate multiple meanings of those phrases, learn the conceptual relationships embedded within your documents, and even use machine-learned ranking to discover the relative importance of different features and then automatically optimize its own ranking algorithms for your domain?
In this presentation, youll learn you how to do just that - to evolving Lucene/Solr implementations into self-learning data systems which are able to accept user queries, deliver relevance-ranked results, and automatically learn from your users subsequent interactions to continually deliver a more relevant experience for each keyword, category, and group of users.
Such a self-learning system leverages reflected intelligence to consistently improve its understanding of the content (documents and queries), the context of specific users, and the relevance signals present in the collective feedback from every prior user interaction with the system. Come learn how to move beyond manual relevancy tuning and toward a closed-loop system leveraging both the embedded meaning within your content and the wisdom of the crowds to automatically generate search relevancy algorithms optimized for your domain.
Trey is the SVP of Engineering at Lucidworks, where he leads their engineering efforts around both Apache Lucene/Solr, as well as Lucidworks commercial product offerings. Trey is also the co-author of the book Solr in Action, as well as a published researcher and frequent public speaker on topics related to search, analytics, recommendation systems, and natural language processing. Trey previously served as Director of Engineering at CareerBuilder, developing search, recommendations, and data analytics products powering billions of searches a month across billions of documents. Trey holds degrees from Georgia Tech (MBA in Management of Technology), Furman University (Bachelors in CS, Business, Philosophy), and has also completed Masters-level work in Information Retrieval and Web Search from Stanford University.
