[In]formation Retrieval: Search at LinkedIn
By Shakti Sinha & Daniel Tunkelang
Bay Area Search Meetup Presentation
March 27, 2013
LinkedIn has a unique data collection: the 200M+ members who use LinkedIn are also part of the content those same members access using our information retrieval products. In this talk, the speakers will discuss some of the unique challenges we face in building the LinkedIn search platform, particularly around leveraging semi-structured and social content, understanding query intent, and personalizing relevance.
Shakti Sinha heads LinkedIn's search relevance team, and has been making key contributions to LinkedIn's search products since 2010. He previously worked at Google as both a research intern and a software engineer. He has a MS in Computer Science from Stanford, as well as a BS degree from College of Engineering, Pune.
Daniel Tunkelang leads LinkedIn's efforts around query understanding. Before that, he led LinkedIn's product data science team. He previously led a local search quality team at Google and was a founding employee of Endeca (acquired by Oracle in 2011). He has written a textbook on faceted search, and is a recognized advocate of human-computer interaction and information retrieval (HCIR). He has a PhD in Computer Science from CMU, as well as BS and MS degrees from MIT.
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