How LinkedIn uses Data Science to build your feed - LinkedIn Feed Algorithm Explained

Key Takeaways

hey everyone today i want to talk about something that i spend a lot of time on linkedin a couple of my friends recently asked me if i was not active on linkedin this came as a slight surprise to me because i was still actively posting but then of late i've observed that my posts aren't reaching as many people as they used to so i set out to understand how linkedin's algorithm actually decides what to show in the field let's try and understand it with an example alia is a regular linkedin user with close to about 500 connections she logs on to linkedin and starts to scroll let's say some of her friends have written some posts on linkedin already now the linking algorithm comes into picture the linkedin algorithm decides what to show when to show and how to rank each post and here's the assumptions the algorithm makes the algorithm assumes that if all your way to see someone's post serum beers and if she were to find it relevant she would actively click on it engage with the content or with the conversation or with ranbir himself she might initiate one of these three actions or viral actions as dingdin likes to call them she might do a reaction which is either a thumbs up or like or celebrate or or the other reactions she might comment on that post or she might reshare it as a result of her actions she creates two kinds of values downstream and upstream downstream value is created when alia re-shares the posts with her connections which implies that somebody had created the post alia has shared that post and has now wanted to share it with all her connections upstream value is created when alia commenced on that post as now she's actively providing feedback to ranbir so therefore for any update randir or any of other alias connection makes the algorithm calculates a score it calculates a score using the following three things which are obtained using machine learning models it calculates the probability of alia taking action on the post remember an action could either be a reaction a comment or a reshare it calculates the expected downstream clicks or viral actions as linking likes to call them when alia interacts with the post it calculates the expected upstream value to run there if alia takes this action the outputs of each of these models are then synthesized into one score using a weighted linear combination and finally this score is used to pointwise rank all of alias connections updates and therefore uh show the posts to earlier so what does this entire exercise helps us understand specifically me uh with my posts right so it tells me that the comments that the audience makes on their posts are really important for me as a creator it tells me that the audience comments on my posts are really important it signals intent from the audience's side to engage with the posts that i write resharing also seems to be very important so if you really want to see my posts or anyone else's posts on linkedin do engage with that content so you can either comment on it you can either do a reaction or perhaps reshare it this gives a signal to linkedin that you really want to see this person's content on your feed ranked up one topic that i didn't talk about here and that linkedin has actually introduced in its uh calculations is dwell time which i think i will cover in another video until then keep interacting with my posts on linkedin and please don't forget to like and share this video and also please don't forget to subscribe to my channel thank you so much for watching