May 26, 2020
Trey Grainger, Chief Algorithms Officer at Lucidworks, author, and speaker, discusses search engines, artificial intelligence, and AI-powered searches.
Grainger is an experienced engineering and data science executive with specific expertise in search and information retrieval, as well as recommendation systems, and data analytics spaces. Grainger discusses his background and his work at Lucidworks, the successful San Francisco, California-based enterprise search technology company.
As Grainger explains, Lucidworks
provides its expertise in the area of AI-powered search technology
to hundreds of Fortune 1000 clients. Accessing data and finding
relevant results is the name of the game, and Lucidworks is
exceptional in this critical area of business development. He
discusses chatbots and analytics use cases, and how companies can
Lucidworks assists their broad base of clients by helping them build intelligent search applications that will allow them to fully expose their products to customers and/or provide internal knowledge to all their existing employees. Grainger goes on to explain how search engines are utilized by nearly every website, but many simply don’t get the job done. Lucidworks powers search engine technology that digs deeper and provides relevant results that are useful.
Grainger talks about the
importance of ‘bringing back’ results that best match the intent of
the user/searcher. Intelligent search technology must be specific,
focusing on the content dimension, and user-understanding
dimension, etc. For example, sophisticated
search engines should be able to pick up on signals, learning
what people want to ‘see’ in their content, based upon their clicks
and behaviors, so the engine can ‘tune’ itself to find better
answers for future user/searchers.
He delves into the subject of domain understanding, and discusses how it drills down to what the content is really about. For the engine to understand the nuanced meaning of searches and search words is important. The context of the user is important, for example, if a user searches for the word ‘driver’ while at the airport, the search engine should be able to discern that they’re probably looking for an Uber or taxi driver, and probably not a device driver for their computer’s OS. Context is crucial in order to provide the appropriate results.
Continuing, Grainger discusses
the specifics of queries, and different experiences that searches
can provide. He talks about the direct correlation between
improving relevance in searches to increased bottom lines. He talks
about commerce use cases versus enterprise use cases, their
similarities, and the benefits. Wrapping up, Grainger talks about
natural language processing and the future of searches.
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