Find and Be Found: Information Retrieval at LinkedIn
SIGIR 2013 Industry Track Presentation
LinkedIn has a unique data collection: the 200M+ members who use LinkedIn are also the most valuable entities in our corpus, which consists of people, companies, jobs, and a rich content ecosystem. Our members use LinkedIn to satisfy a diverse set of navigational and exploratory information needs, which we address by leveraging semi-structured and social content to understanding their query intent and deliver a personalized search experience. In this talk, we will discuss some of the unique challenges we face in building the LinkedIn search platform, the solutions we've developed so far, and the open problems we see ahead of us.
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 an 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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