Building Nearline Contextual Recommendations for Active Communities on LinkedIn
published: Sept. 24, 2018, recorded: August 2018, views: 487
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !