Join us for a two-part talk on personalized job matching using Learning-to-Rank (LTR), a machine learning ranking algorithm. We'll have Doug Turnbull from OpenSource Connections to introduce the concepts of LTR and Jason Kowalewski from Snagajob to talk about using LTR at Snagajob.
6:00pm: meet, mingle, pizza, beer
6:30: The ES Learning to Rank Plugin: Machine Learning + Search => Smarter Search:
Search relevance is hard! Do you struggle to manually tune search results? Do your search results still stink? At OpenSource Connections, we've developed a learning to rank plugin that can work with machine learning models to rank Elasticsearch search results. In this brief talk, I want to introduce learning to rank, it's pros & cons as a technique, share our plugin, elicit community feedback, and engage in a conversation about use cases. Come whether you're new to Elasticsearch, or if you've had years of experience and want to give us feedback!
Doug Turnbull, OpenSource Connections
7:00pm: How Snagajob is using Elasticsearch Learning to Rank to build smart job matching
Jason Kowalewski, Snagajob Engineering
Doug Turnbull is a lead relevance consultant at OpenSource Connections. Author of Relevant Search. Doug makes search smarter, more relevant, better able to anticipate user intent, and satisfy his client's business goals. Doug believes search engines will evolve into general-purpose intent engines, increasingly moving away from just the search bar. Doug loves proving/disproving that idea by developing predictive notifications, image search, semantic search, voice search, chatbots and other cutting edge techniques using Elasticsearch. Doug has contributed numerous plugins & tools for the Elasticsearch communities, including Splainer, Elyzer, Elasticsearch LTR Plugin, and Quepid.
Jason Kowalewski is a Principal Architect at Snagajob focused on building a next generation platform to facilitate better matches between employers and workers in the hourly labor market. Jason brings a wide array of experience building scalable, high performance systems to solve problems in the areas of search, predictive analytics, and advertising technology. As an advocate of open source technologies, he is very excited to be implementing a learning-to-rank model inside elasticsearch and the potential which it presents.
About Data Hackers
RVA Data Hackers meets monthly to hear, present and discuss topics in machine learning & big data. Come on down and help us build Richmond's Data Hacking community.
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