Ashley and the LinkedIn team ask, “What’s one thing not on your LinkedIn profile that you want people to know about you?”
Of course, there are tons of non-work related things like my love of cooking, my inability to sing, etc. but the reason for that is optimizing for the way LinkedIn works. In a series of interviews on the This Week in Machine Learning podcast, LinkedIn’s data scientists have explained in great technical detail how LinkedIn decides what you see. Watch the video for a review of those key concepts.
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What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.
In today’s episode, Ashley asks, what one thing not on your LinkedIn profile that you want people
to know about you? That’s an interesting question.
There isn’t much that I isn’t already on there of them. The random hobbies, like for example cooking, or that I’m a terrible singer, or they practice the martial arts, which by the way, if you want to, like hear a really in depth look at some of these things. I did a lot of this in touch about a lot of this in Episode One of the punching out podcast, which you can find on iTunes, Google podcasts, carrier shake, or grown. And Katie, Roberto, or did a whole season of interviews with people in the marketing space about everything except marketing. So it’s like all the hobbies, the music, the stuff that people like the the the after work kinds of like you were just sitting around having dinner with somebody, these are the kinds of things you’d find out. So check that out.
But I think the question is, to make a fun question, not fun, which is something I do,
the way LinkedIn algorithm works is there are it takes a look at who your connections are, who their connections, our first, second or third degree connections. And the text that is on your profile, the text is on their profiles, and so on and so forth, in terms of those into what are called vectors, mathematical representations of those profiles. And then there is a the content that you post when you share posts and updates on LinkedIn. All of this goes into a type of networking database called a graph
network. I was listening to a bunch of episodes of this week and machine learning and AI, which is another great podcast, if you want to get you like super, super technical.
This is the show to listen to.
It is for the folks who are building machine learning. But one of the things that was interesting is that they’ve now had, I think, three or four different people from LinkedIn on the show, who explain in great detail how different aspects of the LinkedIn algorithm work. LinkedIn is the only social network that has gone on records. And here’s how our technology works in the back
end, which has been a terrific for
technical marketers like me to say, oh, okay, so here’s the things I need to do in order to make my LinkedIn profile more find able
to understand the algorithm and in more depth to
understand how people are seeing my content.
And so that vector ization of your text that you create that you share, is used in matching algorithms, zoos and prediction algorithms for LinkedIn to say, Who should I show your content to
write so I put up a blog post on LinkedIn or a link or if video or something, and it’s
cooking hobby, for example.
LinkedIn will look at my network. And we’ll look at the people within my network. And it will show that content selectively to people for where there is a a topical match of sorts of we’re predicts, hey,
Katie would is also interested in cooking and she might
from this post on LinkedIn.
has known as never expressed any interest in and his network has never expressing interest in cooking. So let’s not show this content to Chris.
And Tom only post look like cooking. So maybe we’ll show Tom one test post, see what happens, see if he engages with it.
In one in another episode, Romero’s Alice, who is the head
of LinkedIn AI group said that they do what’s called
the technical term is multi parameter optimization. And that means that LinkedIn is optimizing for many different objectives,
one of which is complaints.
Dr. rozas was saying, this is a metric that we that they look at, to make sure that they’re delivering a high quality experience, which is really cool, which also means that if the algorithm shows something that is so out of touch, so attitude, with what somebody wants, they’ve done actually complain about the second, why am I seeing this kind this post, or, or block this person, those are forms of complaints.
when it comes to what you put on LinkedIn,
when it comes to what you want to share, what you want to talk about what you what other content, other people’s content, you share who you’d had all that matters, because all that is part of the graph algorithm. It’s part of getting your content in front of as many relevant people as possible.
give real thought to what’s in your LinkedIn profile. What do you want to be found for? That means not just one line job descriptions, but having keywords and phrases and ideas in your LinkedIn profile in your job descriptions,
in your testimonials, your recommendations,
what you are endorsed for so those endorsements that that seemed a little bit trivial, our topic indicators. We go on
a whole the time about hierarchies and ontology and stuff within those topic indicates the endorsements do matter,
for helping your content gets shown to other people. Because if somebody says, Chris Patten knows about machine learning, and AI
post about machine learning, and somebody else has an interest in machine learning,
there’s a much more much clear predictive predictive
strength to that of they hated I’m going to show Chris’s machine learning post
of what she’s an authority about machine learning to somebody who’s interested in machine learning who’s posted about it, but may not have it in their profile, and see if they engage with it.
When you look at the stuff you can add your LinkedIn profile like your testimonials, the gear blog posts, would you publish on the publishing platform, when you post up a new post,
all of that is relevant. All of that
helps the graph network side how to make connections and predictions about connections in the people you may know section is a whole
set of algorithms about
how is your profile featured and people you may know
it’s not a monolithic algorithm It is based on all these different factors.
yes, some of my hobbies and things and not in my, my LinkedIn profile. Why? Because I’m trying to optimize it
to work with LinkedIn algorithm as best as possible.
Obviously, staying within the terms of service
being focused on because it is a professional
network, the things
that other people are looking for.
Think of it is all this content that we share as like SEO for LinkedIn annoyed because LinkedIn is a search engine for people
instead of what you’re looking for who
so a very long Not at all, what was intended answer to the question that actually had on what’s not on my LinkedIn profile, I do a lot of a lot of cooking. Enjoy the science of cooking.
science of making social networks work for us and hope some of these tips help you make your LinkedIn profile and the activities you do perform on LinkedIn
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