Many LinkedIn users have noticed that the platform seems to repeatedly recommend connecting with the same people, even after you’ve already connected with them or dismissed their profile from your recommendations. There are a few potential reasons why LinkedIn’s algorithm keeps suggesting the same profiles over and over again:
Your networks overlap significantly
If you and the recommended person share a large number of 1st, 2nd and 3rd degree connections, LinkedIn’s algorithm sees you as highly likely to know each other and want to connect. The more overlapping connections you have, the more LinkedIn will push that person’s profile to you since you are more likely to find value in connecting with them. Even if you’ve already connected or dismissed the recommendation, if your networks continue to significantly overlap, the algorithm will keep suggesting them.
You have similar profiles and interests
LinkedIn’s algorithm looks at all the information in your profile and the other person’s profile to find similarities that indicate you might get value out of connecting. This includes similarities in:
– Industry
– Companies worked for
– Schools attended
– Skills and expertise
– Shared interests and groups
– Location
The more interests, experiences and credentials you have in common, the more LinkedIn will assume you would benefit from connecting and continue suggesting that person.
You visited their profile recently
LinkedIn tracks the profiles you view to better understand your interests and priorities. If you view someone’s profile multiple times, LinkedIn takes that as a signal that you are interested in that person for some reason and want to connect with them. As a result, their profile may be recommended to you more frequently.
They engage heavily with your content
If someone is regularly viewing, commenting on or liking your posts and updates, LinkedIn sees that person as highly interested in you and your content. Therefore, it will be more likely to recommend you connect with each other so you can continue that engagement. Even if you dismiss the recommendation initially, their continued interest in your profile and content will cause LinkedIn to re-suggest them.
Why LinkedIn’s algorithm works this way
There are a few reasons why LinkedIn is designed to repeatedly recommend the same profiles rather than always suggesting new people:
Maximizing relevant suggestions
LinkedIn’s primary goal is to provide you with the most relevant and useful connection suggestions based on who you are most likely to want to connect with. If their algorithm determines someone is a strong match for you, it will keep suggesting them rather than looking for new potential connections that are less relevant.
Increasing engagement
LinkedIn also wants to promote engagement between users on the platform. If data shows you and another user are highly likely to engage with each other’s content and profiles, LinkedIn will continue to push you to connect in order to increase interaction.
Account for changes over time
Your own profile, interests and networks can change over time. As a result, someone LinkedIn may not have suggested to you in the past may become very relevant based on updated data. Repeatedly recommending connections accounts for these changes.
Encourage dismissed connections
By re-suggesting connections you previously dismissed, LinkedIn is essentially saying “Are you sure you don’t want to connect with this person? Look at all these reasons why they are a great match!” This frequent pushing encourages you to reconsider connections you may have dismissed too quickly.
How to stop seeing the same suggestions
If you are tired of LinkedIn constantly recommending the same people, here are a few ways to help change what profiles you see:
Remove overlapping connections
If you and the suggested person share a lot of connections, consider removing some of those mutual connections that likely led to the repeated recommendation. However, this can negatively impact your own networking, so use caution.
Update your profile details
Change details like your industry, interests and skills to reduce perceived similarities with the recommended individual’s profile. Just be careful not to be dishonest or misleading.
Block the user
Rather than just dismissing the recommendation, formally block the person’s profile. This tells LinkedIn’s algorithm you definitively do not want to connect or engage with them.
Hide your profile view history
In your account settings, enable the option to hide your profile views from others. This stops others from seeing when you view them and limits LinkedIn’s ability to track and leverage that data.
Wait it out
With enough dismissals over time, LinkedIn’s algorithm may eventually determine the person is not relevant enough to keep recommending. But this can take significant patience.
The pros and cons of LinkedIn’s approach
LinkedIn’s repetitive connection suggestions have some benefits, but also some downsides:
Pros
- Helps ensure you connect with the most relevant people in your networks and industry.
- Increases engagement between matched users.
- Accounts for changes in profiles and interests over time.
- Gives you opportunities to reconsider previously dismissed connections.
Cons
- Can be annoying and clutter your recommendations.
- Algorithm may misjudge relevance and fit between users.
- Repeated prompts may feel pushy and manipulative.
- Hard to fully stop recommendations from re-appearing.
The role of AI and machine learning
LinkedIn uses sophisticated AI and machine learning algorithms to determine which profiles and connections to recommend to you. Here are some key ways these technologies enable repetitive suggestions:
Identifying patterns and similarities
AI analyzes millions of data points across user profiles to detect patterns and meaningful similarities that indicate two people should connect. It does this far better than human analysts could.
Adapting to new information
Machine learning algorithms incorporate new data and feedback on an ongoing basis to refine recommendation relevance. Your new connections, interests and content engagement help teach the model.
Predicting engagement
Sophisticated machine learning models analyze historical data to predict which members are most likely to mutually engage based on similarities. These get recommended more.
Ranking and filtering
AI ranks recommended connections based on relevance scores calculated by machine learning. It filters out less relevant suggestions, allowing higher ranked ones like repetitive people to be prominently displayed.
Weighing dismissed suggestions
Algorithms look at dismissal rates and weigh that against other relevance signals to determine when to re-recommend dismissed connections.
Best practices for managing recommendations
Here are some tips for managing repetitive or irrelevant connection suggestions on LinkedIn:
- Proactively look for and connect with people you know and want in your network rather than relying solely on LinkedIn’s suggestions.
- Take time to complete your profile details to give LinkedIn’s algorithm more signals to determine relevance.
- Be selective in dismissing connections. Only dismiss those you are confident you would never want to connect with.
- Occasionally re-visit dismissed profiles in case they have been updated in a relevant way since initial dismissal.
- Don’t obsess over connecting with every recommendation. Focus on quality over quantity in your network.
- Utilize settings to control what types of notifications you receive around new suggestions.
Conclusion
LinkedIn’s algorithms favor repeatedly recommending the same potentially relevant connections rather than always suggesting new people. This persistence stems from the platform’s goals of maximizing engagement between well-matched members and adapting to evolving user data. While repetitive suggestions can become annoying, they do serve a purpose. Utilizing privacy controls and being selective when dismissing recommendations can help better shape the connections suggested to you. But some repetition is inevitable given how LinkedIn’s underlying technology works. The best approach is curating your own network proactively rather than purely relying on LinkedIn’s recommendations.