Part 2 - How LinkedIn uses Data Science to build your feed | LinkedIn Feed Algorithm Explained

Key Takeaways

hey everyone welcome to the second video on linkedin feed algorithm explained in the previous video we saw how linkedin decides to show you what you see on your feed based on the viral actions you take to summarize linden decides to show you what you see on your feed based on explicit and implicit actions at the end of the last video i talked about a concept known as dwell time today we're going to dive deep into that concept so let's get started dwell time is the amount of time a user spends on an update made by the connection or a person they are following there are two kinds of trail time on the feed and after the click on the feed starts measuring when at least half of the update is visible after the click measures the time spent on the content after clicking on an update on the feed the general assumption that linkedin makes is that users value their time and they only want to spend their time on reading or watching the content that really interests them dwell time has the following advantages over just looking at click and viral actions a it is always measurable b it is a real valued measure of engagement and therefore can be a more reliable indicator of engagement and see it contains a lot of signal so linkedin analyzes a user's drill time by computing the empirical cumulative distributive function or cdfs or dwell time per update while the user is on mobile and there are two main user behaviors that come out by observing that data number one is interactive update users tend to spend more time viewing an update if they decide to take a viral action or click on it as they go as they keep on reading it further so this is the first one the interactive update the second kind of update is known as the skipped update users view a post or a content for a short period of time but do not take an action over it and then just scroll by it so in essence they skip that update to formalize this concept of a skipped update linkedin defines a threshold a threshold time that a user takes before skipping an update and they call it t skip the time it takes for a user to skip and update and using a logistic regression model linkedin now calculates a new probability p of skip p of skip is equal to probability of users dwell time being less than t skip which is the threshold time for users keeping the update and use this probability in the final score that we had seen earlier in the previous video now this probability adds the concept of skipped updates to our original score so what does this all mean it means that apart from your click or viral actions that you take on the post linkedin takes your dwell time very seriously the amount of time you spend reading and scrolling through the content and therefore it becomes very important for you to realize how you can design the feed the way you want it to if you like someone's posts and you want to see them you should ideally try and take an action on their post make a viral action like linkedin calls or maybe just click on the post but let's say you don't want to register an action the other alternative for you is to spend enough time on that post or on that content so as to indicate to linkedin that hey you're interested in this in reading content from this person and you just do not want to linkedin to call this as a skipped update you want linkedin to register this as an update of interest so if you want to read something or read from somebody please make sure you either make an action or just read through that piece of content or just spend some time on that piece of content so as to give a signal to linkedin to say that hey this is very important for me and with that we come to the end of this video thank you so much for watching please let me know in the comments if you like this video or would you like if you would like me to create something else on some other platforms algorithm until then please do like and share this video and do subscribe to the channel thank you so much for watching