KEEPING IT PROFESSIONAL:
RELEVANCE, RECOMMENDATIONS, AND REPUTATION AT LINKEDIN

DANIEL TUNKELANG

LinkedIn

LinkedIn operates the world's largest professional network on the
Internet with more than 120 million members in over 200 countries. In
order to connect its users to the people, opportunities, and content
that best advance their careers, LinkedIn has developed a variety of
algorithms that surface relevant content, offer personalized
recommendations, and establish topic-sensitive reputation -- all at a
massive scale. In this talk, I will discuss some of the most
challenging technical problems we face at LinkedIn, and the approaches
we are taking to address them.

BIO

Daniel Tunkelang oversees the data science team at LinkedIn, which
analyzes terabytes of data to produce products and insights that serve
LinkedIn's members. Prior to LinkedIn, Daniel led a local search
quality team at Google. Daniel was a founding employee and Chief
Scientist of Endeca, a leader in enterprise search and business
intelligence that pioneered the use of guided navigation in search
applications. He has authored eight patents, written a textbook on
faceted search, created the annual workshop on human-computer
interaction and information retrieval (HCIR), and participated in the
premier research conferences on information retrieval, knowledge
management, databases, and data mining (SIGIR, CIKM, SIGMOD, SIAM Data
Mining). Daniel holds a Ph.D. in Computer Science from CMU, as well as
B.S. and M.S. degrees from MIT.