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1 month ago
How LinkedIn Personalized Performance for Millions of Members Using Tensorflow.js

 
Originally published in the TensorFlow Blog, March 29, 2022.

The Performance team at LinkedIn optimizes latency to load web and mobile pages. Faster sites improve customer engagement and eventually revenue to LinkedIn. This concept is well documented by many other companies too who have had similar experiences but how do you define the optimal trade off between page load times and engagement?

The relationship between speed and engagement is non-linear. Fast loading sites, after a point, may not increase engagement by further reducing their load times. At LinkedIn we have used this relationship between engagement and speed to selectively customize the features on LinkedIn Lite – a lighter, faster version of LinkedIn, specifically built for mobile web browsers.

To do this, we trained a deep neural network to identify if a request to LinkedIn would result in a fast page load in real time. Based on the performance quality result predicted by this model we change the resolution of all images on a given user’s news feed before the resulting webpage was sent to the client. This led to an increase in the magnitude of billions for extra Feed Viral Actions (+0.23%) taken, millions more Engaged Feed Users (+0.16%) and Sponsored Revenue increased significantly for us too (+0.76%).

To continue reading this article, click here.

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