The Relevance of Data: Going Behind The Scenes at LinkedIn
Deepak Agarwal, LinkedIn’s director of relevance science, discusses how his company uses data and analytics — and the potential impacts for the rest of the world.
Topics
Competing With Data & Analytics
It is an understatement to say LinkedIn is growing like a weed. It’s rare that any company — much less one started in its founder’s livingroom a little more than a decade ago — can boast the kind of growth LinkedIn has right now: Every second, 2 people (or more!) join the site. With 238 million members in over 200 countries, 2.8 million active company profiles, and 1 million professionally oriented groups, LinkedIn has become the world’s largest professional networking site. It’s an unrivaled achievement; literally none of its competitors (or imitators) has anywhere near that reach.
The company’s mission is deceptively simple: Connect the world’s professionals to make them more productive and successful. But its vision is something completely different. In a 2012 blog, Jeff Weiner, LinkedIn’s CEO (@jeffweiner), explained how the company initially developed an infrastructure that could map its members’ professional relationships up to three degrees.
That reach — and the massive amount of data that’s connected and parsed through LinkedIn’s complex machine learning and optimization algorithms — has interesting implications for members and employers. But the vision extends to future global economic development. Deepak Agarwal, director of relevance science at LinkedIn, talks with MIT Sloan Management Review contributing editor Renee Boucher Ferguson about LinkedIn’s relentless focus on data relevance. And what the Age of Analytics, coupled with LinkedIn data, could mean for the world.
Can you provide a bit of background on what relevance science means at LinkedIn?
Broadly speaking, the role of relevance science at LinkedIn is to improve the relevancy of products by extracting the signal from LinkedIn data. This is a difficult problem and requires an interdisciplinary approach. Our relevance scientists have diverse backgrounds ranging from computer science, machine learning, optimization, statistics, information retrieval, economics, and software engineering. We have made a significant impact on products like advertising, LinkedIn feed, news, job recommendation, people recommendation, and many others.
How does that work, in practical terms, to improve relevance at LinkedIn?
Since the inventory of items we can display to users (e.g., feed updates, ads, news, people, jobs and others) is selected from a very large and dynamic pool, it is infeasible to select the best items for every user visit manually.