Thursday, June 7, 2012
08:30 AM - 09:30 AM
|Level: ||Case Study|
|Location: ||Grand Ballroom A|
Science fiction has a mixed track record when it comes to anticipating technological innovations. While Jules Verne fared well with with his predictions of submarine and space technology, artificial intelligence hasn't produced anything like Arthur C. Clarke's HAL 9000.
Instead, we've managed to elicit intelligence from machines through unexpected means. Search engines have achieved remarkable success in organizing the world's information by crawling the web, indexing documents, and exploiting link structure to establish authoritativeness. At LinkedIn, we apply large-scale analytics to terabytes of semistructured data to deliver products and insights that serve our 150M+ members. Semantics emerge when we apply the right analytical techniques to a sufficient quality and quantity of data.
In this talk, I will describe how LinkedIn's huge and rich graph of relationship data that powers the products our users love. I believe that the lessons we have learned apply broadly to other semantic applications. While quantity and quality of data are the key challenges to delivering a semantically rich experience, the key is to create the right ecosystem that incents people to give you good data, which then forms the basis for great data products.
After graduating from MIT with degrees in computer science and math and then completing a PhD at CMU, Daniel joined the founding team of Endeca, where he served as Chief Scientist. he then worked at Google to improve local search quality. He is currently a Principal Data Scientist at LinkedIn where he leads a team of data scientists focused on the 3 Rs: Relevance, Recommendations, and Reputation. .