Let’s dive into an example. Empirical CDFs of dwell time per update on the LinkedIn feed (mobile app). We call these “click bounces.”, member-side features (e.g., member’s profile data), update-side features (e.g., number of global clicks and viral actions on this update), member-update features (e.g., member’s historical affinity to posts from the same author), other contextual features (e.g., time of day). The Feed AI Team’s mission is to help LinkedIn’s members discover the most relevant conversations and content in their feed to help them be more productive and successful. Many colleagues and teams played a role in this work in one way or another. We saw a large decrease in the number of skipped updates and observed that our members interacted much more with their feed updates through clicks and viral actions. We also consider who would benefit from hearing from you, and may rank a connection’s post higher if their post needs more engagement. A first-pass, candidate generation layer applies an efficient and lightweight ranking algorithm to identify the top candidate updates to show her. On the other hand, a comment from Alice will have an upstream effect, as it provides valuable feedback to the creator (Bob) that may encourage him to post more often. The same is generally true online. Many of us have lots of connections, or follow lots of people and companies. Genuine conversation around real experiences spark better and deeper conversation. To accomplish this, we train our machine learning models to predict several quantities for each possible click and viral action (click, react, comment, share): The outputs of these models are then synthesized into a single score using a weighted linear combination, where the weights are tuned to ensure that all three components are appropriately balanced in order to maintain a healthy feed ecosystem. For example, if you’re posting a link, express an opinion with it. ), comment, or re-share—these three options are what we call “viral actions” because they can have downstream and/or upstream network effects. When you unfollow a person, you will no longer see their posts, but you’ll remain connected on LinkedIn and the person will not know you’ve unfollowed them. We start with the assumption that if Alice were to see Bob’s post and find it to be relevant, she would click on it to engage with the content, the author, or the conversation. Clicks are noisy indicators of engagement. In real life, most of us feel more comfortable talking with people we know. We have a saying at LinkedIn: “People You Know, Talking About Things You Care About.” This is, simply, how we think about the LinkedIn Feed. As demonstrated by the above example, analyzing members’ dwell time led to useful insights that allowed us to directly improve ranking on the LinkedIn feed. 1. I’ll save you the trouble…. Click and viral actions are primarily binary indicators of engagement—either you carry out the action or you don’t. There are many ways you can signal what you’re interested in: the most obvious is joining groups, and following hashtags, people, and pages. On the LinkedIn mobile app, you can tailor the content in your feed by tapping on the control icon on the top right corner of any update and going to “Improve my feed.” From here you can discover new industry leaders, publications and companies to follow, and we’ll automatically deliver news and updates that … We are firm believers that time well spent is better than more time spent. Below, we take a deep dive into one example where analyzing dwell time data led us to add a new machine learning model that brought significant improvements to feed ranking. We invest a lot in understanding what you’re interested in and matching that to what the posts are about. Authenticity is key: all the tips above work out better when members talk about things they truly care about, in a way that’s natural for them. The Feed AI Team’s mission is to help LinkedIn’s members discover the most relevant conversations and content in their feed to help them be more productive and successful. Finally, this score is used to perform a point-wise ranking of all the candidate updates. We analyze members’ dwell time on the feed by computing the empirical CDFs (cumulative distribution functions) of dwell time per update while on mobile. While these members may still visit the feed frequently and find value in the updates they see, they may shy away from taking click and viral actions. We call this creator side optimization. At a high level, each update viewed on the feed generates two types of dwell time. Note that the ML models used above to generate the final score for each update focus primarily on predicting click- and viral-related quantities. Again, all with the goal of showing you the content and conversations that you care about. Therefore, for each candidate update, we need to consider both Alice’s likelihood of engagement, and the potential downstream and upstream effects on her network as a result. Or because a connection liked, commented, or shared someone else’s post. For example, a member may click on an article, but quickly close out, realizing it’s not relevant, and return to the feed within a few seconds. With this assumption in mind, dwell time has the following advantages over solely looking at click and viral actions: Given these advantages, we explored several different methods of incorporating dwell time into our modeling. To measure the impact of our new P(skip) model and features, we conducted several online A/B experiments on a small percentage of LinkedIn members. To summarize, your LinkedIn feed is made up of the conversations happening across your professional communities: among connections; in the groups you’ve joined; and the people, pages, and hashtags that you follow. More are on the way. Second, there is dwell time “after the click,” which is the time spent on content after clicking on an update in the feed. In some cases, we decide not to spend additional time viewing an update, and skip over it to continue scrolling. We strongly believe that people need their professional communities to help them along the way, whether that's current or former colleagues, peers in the same industry, or those that share similar interests or career ambitions. It is personalized for you based on your profile and relationships in order to surface topics you care about from people who matter in your professional world. If you're an active poster on LinkedIn, you might be reverse engineering this article to figure out best practices for reaching an audience with the LinkedIn feed. Post things that encourage a response.
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