LinkedIn feed is the starting point for millions of users on this website and it builds the first impression for the users, which, as you know, will last. Having an interesting personalized feed for each user will deliver LinkedIn’s most important core value which is to keep the users connected to their network and their activities and build professional identity and network.
LinkedIn’s Personalized Feed offers users the convenience of being able to see the updates from their connections quickly, efficiently, and accurately. In addition to that, it filters out your spammy, unprofessional, and irrelevant content to keep you engaged. To do this, LinkedIn filters your newsfeed in real-time by applying a set of rules to determine what type of content belongs based on a series of actionable indicators & predictive signals. This solution is powered by Machine Learning and Deep Learning algorithms.
In this article, we will cover how LinkedIn uses machine learning to feed the user’s rank. We will follow the workflow of a conventional machine learning project as covered in these two articles before:
The machine learning project workflow starts with the business problem statement and defining the constraints. Then it is followed by data collection and data preparation. Then modeling part, and finally, the deployment and putting the model into production. These steps will be discussed in the context of ranking the LinkedIn feed.