Building Nearline Contextual Recommendations for Active Communities on LinkedIn

author: Hema Raghavan, University of Massachusetts Amherst
published: Sept. 24, 2018,   recorded: August 2018,   views: 3
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Description

At LinkedIn our mission is to use AI to connect every member of the global workforce to make them more productive and successful. The social network is the backbone for professionals to engage with each other at every stage of their career. In the first half of this talk I will focus on technologies we have built to power LinkedIn’s “People You May Know” product, that is the primary driver to connect the world’s professionals to each other to form a basic community. The platform allows for triangle closing and other graph walk algorithms in real time. It also allows models to consider near real-time features based on a user’s context. We will demonstrate improvements through AB tests. We will then move on to discuss work done in predicting the downstream impact of forming an edge between two members on the overall activity of our ecosystem. We will show that how a member’s network evolves plays an important role in their downstream engagement. Finally, we will present our work on near real time optimization of activity-based notifications that ensure that our members never miss a conversation that matters. We will show through experiments that delivering the right information to the right user (through better content targeting) at the right time (through delivery time optimization and message spacing) is critical to building an actively engaged community.

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