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Friday, September 14 • 2:00pm - 2:50pm
OPEN TALK: PANEL: Machine Learning for Smarter Search

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To create "smarter search" in today's search engines, we need to understand a) what our users are looking for, b) what kinds of questions they've generally been interested in the past, c) what previous attempts to satisfy this search question have been, d) what others have been feeling were successful answers to this (and related) questions.  These questions are difficult in general, but with our user's collective behavior to guide us, an "80% solution" can be achieved by doing that most effective "machine learning model": counting.  The next 80% relevance gain can be found in a combination of collaborative filtering recommender systems wired through your search engine, and simple supervised machine learning for query intent classification.  If you really need to get the remaining 80% of relevance gains left, you'll need to roll up your sleeves and do some NLP or knowledge graph mining, but first let's see if you even need to get that far.

avatar for Jake Mannix

Jake Mannix

Chief Data Engineer, Lucidworks
Jake Mannix is the Chief Data Engineer at Lucidworks. Before joining Lucidworks, Jake worked on the Semantic Scholar project at the Allen Institute for Artificial Intelligence, and prior to that was tech lead for Twitter’s data science and data engineering teams, building both the... Read More →

Friday September 14, 2018 2:00pm - 2:50pm
Main Stage

Attendees (41)