You are viewing content from a past/completed QCon

Presentation: Nearline Recommendations for Active Communities @LinkedIn

Track: Applied AI & Machine Learning

Location: Pacific LMNO

Duration: 1:40pm - 2:30pm

Day of week: Wednesday

Level: Advanced

Persona: Data Engineering

Share this on:

This presentation is now available to view on InfoQ.com

Watch video with transcript

Abstract

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, the primary driver to connect the world’s professionals to each other to form a basic community. Our 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 describe our nearline platform for notification recommendation and 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.

Speaker: Hema Raghavan

Senior Manager & Heading AI for Growth and Communication Relevance @LinkedIn

Hema Raghavan heads the team that builds AI and ML at LinkedIn solutions for fueling the professional social network’s growth. Prior to that, she was a Research Staff Member at IBM T.J Watson. Hema started her career in the industry in Yahoo Labs. Her interests span the broad area of applications of AI and her experience spans a spectrum of products she has built in the areas of Search, Advertising, Question Answering and Recommendations. She has published in several conferences like WWW, SIGIR, ACL and COLING.

Find Hema Raghavan at

Similar Talks

Machine Learning on Mobile and Edge Devices With TensorFlow Lite

Qcon

Developer Advocate for TensorFlow Lite @Google and Co-Author of TinyML

Daniel Situnayake

CI/CD for Machine Learning

Qcon

Program Manager on the Azure DevOps Engineering Team @Microsoft

Sasha Rosenbaum

ML in the Browser: Interactive Experiences with Tensorflow.js

Qcon

Research Engineer in Machine Learning @cloudera

Victor Dibia

Scaling Patterns for Netflix's Edge

Qcon

Playback Edge Engineering @Netflix

Justin Ryan

Machine Learning 101

Qcon

Data Scientist @IBM

Grishma Jena

ML/AI Panel

Qcon

Staff Developer Relations Engineer @Google Cloud Platform

Amy Unruh

Tracks

Monday, 11 November

Tuesday, 12 November

Wednesday, 13 November