Quick Answer
There are a few main reasons why LinkedIn may keep recommending your own profile to you:
- You recently made changes to your profile – LinkedIn’s algorithm may pick up on this activity and think your updated profile is relevant.
- You don’t have enough connections yet – With a smaller network, LinkedIn has less data to determine relevant recommendations.
- You engage heavily with your own profile – Looking at your profile often tricks the algorithm into thinking you want to see it more.
- It’s a glitch – Sometimes LinkedIn’s algorithm accidentally recommends your profile repeatedly.
The best way to get more useful recommendations is to grow your network, engage with other profiles, and tweak your settings. With time, LinkedIn’s algorithm will learn your preferences and reduce self-recommendations.
In-Depth Explanation
LinkedIn’s feed algorithm utilizes complex machine learning models to recommend content to users. The goal is to show you profiles and posts that are most relevant to your interests and connections. However, it doesn’t always work perfectly. Here are some of the key reasons LinkedIn may keep recommending you your own profile:
You Recently Made Changes
If you recently made updates to your LinkedIn profile – whether editing your summary, adding a new position, uploading a profile photo, or anything else – this triggers LinkedIn’s algorithm to think your profile is fresh, important content worth recommending.
From LinkedIn’s perspective, if you just spent time improving your profile, you must want to see it more frequently and share your updates. So for a short period, your own profile may show up repeatedly in your feed.
You Don’t Have Enough Connections
The more networked you are on LinkedIn, the more data the algorithm has to generate useful recommendations tailored to your interests and connections. With only a few connections, LinkedIn lacks the information needed for ultra-personalized content.
As a result, LinkedIn may default to recommending your own profile as relevant content until you build up your network. The more quality connections you have, the more LinkedIn can match you with posts and profiles actually relevant to your industry, interests, location, and connections.
You Engage Heavily With Your Profile
LinkedIn’s algorithm monitors how you interact with content on the platform. If you spend a lot of time looking at your own profile, liking or commenting on your posts, or editing your profile, LinkedIn sees this as a signal you want to see more of your own content.
Even if you don’t mean to tell LinkedIn you love your own profile, frequent engagement with your profile tricks the algorithm into promoting it within your feed. It thinks it’s giving you what you want.
It’s a Glitch in the Algorithm
Of course, LinkedIn’s algorithm isn’t perfect. Sometimes there are just inexplicable glitches that cause LinkedIn to recommend the same content (like your profile) over and over. The machine learning models can get stuck in loops, fail to pick up on user signals, or produce weird results.
In these cases, there may not be any good explanation beyond LinkedIn’s recommendation technology acting flaky or getting confused. The good news is these glitches are usually temporary and get resolved as engineers tweak the algorithms.
How to Fix Excessive Self-Recommendations
If you’re seeing your own profile appear too often in LinkedIn’s feed recommendations, here are some tips to reduce self-recommendations and see more useful content:
- Build up your network with meaningful connections.
- Engage with other profiles, posts, and content.
- Limit your engagement with your own profile.
- Adjust your post, profile, and feed preferences in Settings.
- Wait it out. Self-recommendations tend to decrease over time as algorithms learn.
Growing your network is by far the best way for LinkedIn to gather the data needed to feed you relevant recommendations tailored to your connections and interests.
Spend time engaging thoughtfully with other profiles, posts, and content you find worthwhile. This trains the algorithm that you want to see more of that type of content.
Be judicious in how often you check back on your own profile or reshare your own posts. Too much self-engagement reinforces the wrong message.
Tweak your settings to have some control over the content you see. For example, you can turn off profile recommendations or reduce frequency.
Finally, have patience. LinkedIn’s algorithms do get smarter and typically cut down on repetitive self-recommendations as they gather more data on actual user preferences.
Why LinkedIn Profile Recommendations Matter
While excessive recommendations of your own profile may seem odd, LinkedIn’s algorithm is designed with good intentions to show you the most relevant content. Here’s why profile recommendations matter:
- Lead to new connections – Recommended profiles let you discover professionals in your industry.
- Surface timely updates – See when connections add new roles, skills, etc.
- Keep your network engaged – Recommendations encourage more profile interaction.
- Allow monitoring your brand – You can see what colleagues or competitors are up to.
- Provide networking opportunities – Useful when recommendations reflect shared interests.
When working effectively, LinkedIn’s algorithms help you strengthen existing ties and form new ones through tailored profile recommendations. But too many self-recommendations defeats the purpose.
The best way to get value from LinkedIn is to actively build up your network with real connections based on shared interests, industries, locations, and connections. This gives LinkedIn the data it needs to give you better recommendations and advance your career.
The Role of Machine Learning in LinkedIn’s Algorithms
Here is an overview of how advanced machine learning powers LinkedIn’s sophisticated recommendation systems:
Machine Learning Models
LinkedIn utilizes a variety of sophisticated machine learning models designed to discern users’ preferences and interests based on their connections, engagement, profile data, and post content. Models include:
- Collaborative filtering – Analyzes common connections and interests
- Content-based filtering – Evaluates profile details and post keywords
- Embedding models – Maps relationships between entities using vector math
- Neural networks – Mimics human learning using interconnected nodes
These ML models continuously train themselves as more user data comes in, progressively improving their recommendations.
Features Used
To discern members’ interests, LinkedIn’s algorithms rely on many profile features, including:
- Industry
- Location
- Job history
- Education
- Skills
- Shared connections
Posts are evaluated based on keywords, text semantics, and metadata like links and hashtags.
Feedback Loops
User engagement with recommendations provides feedback that informs the machine learning models in a virtuous cycle:
- Model generates profile recommendations.
- User engages with some recommendations.
- Engagement is fed back into model as signal.
- Model updates its understanding of user interests.
- Future recommendations improve.
With enough quality data, LinkedIn’s machine learning algorithms gradually improve their ability to match users with relevant profiles and content.
The Bottom Line
LinkedIn aims to use sophisticated machine learning techniques to provide users with the most relevant and impactful profile recommendations. However, glitches can cause repetitive self-recommendations. The best way to improve relevance is to build out your network and thoughtfully engage with quality content. This provides LinkedIn’s algorithms the raw material they need to dial in recommendations aligned with your professional interests and goals.
Frequently Asked Questions
Why does LinkedIn think my own profile is so relevant to me?
LinkedIn’s algorithm recommends your profile for several reasons: recent profile changes, a limited network, frequently checking your profile, glitches, etc. With more connections and engagement beyond your profile, recommendations will become more useful.
How many connections do I need before LinkedIn stops recommending my profile?
There is no magic number of connections that will stop self-recommendations. However, most experts recommend having at least 100-200+ quality connections for LinkedIn to have sufficient data to give useful recommendations.
Should I take any action to get LinkedIn to recommend my profile less?
Don’t obsessively check or update your profile, as too much self-engagement reinforces the algorithm. Focus on thoughtful engagement with other members’ content. You can also tweak profile recommendation settings.
How long will it take for my profile to stop showing up so much?
It depends on the reason you’re seeing excessive self-recommendations, but relevance typically improves over weeks or months as you expand your network and LinkedIn’s algorithms learn your preferences.
Is this happening because of a problem with my profile?
No – quirks in LinkedIn’s algorithm cause self-recommendations. It’s not a reflection on your profile. Building your network and allowing time for machine learning improvements will help.
Conclusion
LinkedIn’s goal with profile recommendations is to connect you with the most relevant professional content tailored to your needs. But self-recommendations can happen due to limitations in your network, algorithm quirks, or too much engagement with your own profile. Fix this by focusing outward to build connections, engage thoughtfully, and give LinkedIn’s machine learning models time to learn your preferences and interests. Soon you’ll see recommendations that add value rather than promoting your own profile.
