The “People You May Know” feature on LinkedIn suggests new connections for you based on your existing network and profile details. This algorithmic recommendation system helps LinkedIn users expand their networks by connecting them with relevant professionals, colleagues, classmates, and more.
In this comprehensive guide, we’ll explore how the LinkedIn “People You May Know” feature works, its intended benefits, and tips for managing your recommendations.
How Does “People You May Know” Work?
LinkedIn’s “People You May Know” feature uses sophisticated data science to generate personalized connection suggestions for each user. While LinkedIn does not publicly share the full details of its algorithm, here are some of the main factors it considers:
– Shared Connections – LinkedIn looks for 2nd and 3rd degree connections to recommend. For example, it may suggest a friend of a friend.
– Shared Work Experience – Colleagues at the same companies, past or present, are commonly recommended. Alumni of the same schools are also suggested.
– Shared Groups & Interests – LinkedIn analyzes profile details to find those with similar organizational memberships, skills, locations, and interests.
– Contact Information – If you have shared contacts with someone in your email or phone, LinkedIn may suggest them.
– Profile Views – People who have viewed your profile are more likely to be recommended to you.
In essence, the “People You May Know” algorithm uses a blend of social graph factors, organizational factors, and behavioral signals to uncover the most relevant potential connections.
Why Does LinkedIn Have This Feature?
LinkedIn’s “People You May Know” feature provides several core benefits for users:
– Helps users discover new connections easily – Rather than needing to search broadly, the algorithm brings relevant suggestions directly to users. This saves effort in finding relevant new contacts.
– Enables users to expand their networks organically – By facilitating discovery of 2nd/3rd degree connections, the feature helps entire networks become more tightly connected through mutual associations.
– Strengthens the value of the LinkedIn social graph – With more direct connections between users, LinkedIn can use its social graph better for targeting content, jobs, and other opportunities. A richer graph creates more value across the platform.
– Creates more engagement between users – When users receive and accept “People You May Know” invitations, it typically leads to more LinkedIn activity and interactions. This boosts platform “stickiness” and time spent.
Overall, the “People You May Know” feature complements LinkedIn’s mission of creating economic opportunity by helping professionals stay connected and discover new opportunities through the relationships in their networks. The suggested contacts enhance users’ ability to build relationships, learn, and advance their careers through the LinkedIn platform.
How Are Suggestions Ranked?
The LinkedIn “People You May Know” algorithm does not simply provide an unordered list of possible connections. It uses ranking systems to surface the most relevant suggestions at the top of the list.
While LinkedIn does not share specifics, the algorithm likely prioritizes recommendations based on factors like:
– Number of shared connections – People with more mutual connections are ranked higher.
– Strength of connections – Connections with 1st degree connections are ranked above 2nd or 3rd degree connections.
– Level of profile interaction – Those who have viewed your profile may be ranked higher than those who have not.
– Relevance of profile details – A former colleague may be ranked higher than someone who simply shared a mutual group.
– Recency of shared affiliation – A former classmate from 20 years ago may have lower priority than a recent colleague.
– Frequency of cross-profile activity – If another user engages frequently with your profile and content, they may be surfaced faster.
– Premium account status – LinkedIn may prioritize suggestions who are premium account holders, improving value-for-money.
As more data comes in around user responses to suggestions, LinkedIn can also tune the algorithm to boost relevance over time. The ranking system allows the platform to maximize the value of the recommendations.
How Often are Suggestions Updated?
The LinkedIn “People You May Know” suggestions do not remain static. Instead, they refresh on an ongoing basis to adapt to updates in user profiles and activity.
Some key points around the cadence and triggers for new suggestions:
– Suggestions refresh whenever you return to your account. There is no fixed time period; new recommendations are provided when you reengage.
– Major profile updates, like adding a new position or skill, will often trigger more relevant recommendations.
– New connections in your network lead to new 2nd and 3rd degree suggestions because of the expanded graph.
– Ongoing user activity creates data signals that impact relevance and ranking of suggestions.
– Seasonal triggers can prompt refreshes, like heading back to campus in the fall or starting a new job.
– Manual changes to suggestions settings (see below) will instantly refresh recommendations.
Overall, the frequency of updated suggestions reflects the continual changes in users’ profiles and networks. This powers ongoing discovery of the most relevant connections.
How Can I Manage My Suggestions?
LinkedIn provides settings to control which types of suggestions you see in your “People You May Know” recommendations. You can customize these from the LinkedIn desktop site under your Account > Settings & Privacy > How LinkedIn uses your data.
The key options to manage suggestions are:
– Selecting your preferred geographic radius – Choose from suggestions in your company/industry, current city, country, or worldwide.
– Setting a profile visibility level – Private mode reduces some types of recommendations from 3rd degree networks.
– Opting out of customized ads – This can reduce external data used to enrich suggestions.
– Deleting contacts to sync suggestions – Removing uploaded contacts from LinkedIn filters any matching suggestions.
In addition, you can provide direct feedback on individual suggestions by indicating you don’t know a person, removing the suggestion. While not definitive, this input helps refine the algorithm over time.
How Accurate Are the Suggestions?
The accuracy of LinkedIn’s “People You May Know” suggestions depends on a few core factors:
– Completeness of user profiles – Thorough profile information like full job histories enables more accurate matching.
– Extensiveness of user networks – Those with large 1st and 2nd degree networks get more robust recommendations.
– Algorithm precision – LinkedIn is continually tuning its models to analyze the right signals for accurate suggestions.
– User feedback – Direct user input on irrelevant suggestions helps LinkedIn boost precision.
While the accuracy varies across individuals, in general the suggestions are highly relevant for most users. Typical accuracy metrics might be:
– 75%+ of suggestions are known as some capacity to users already.
– 85-90%+ of suggestions are considered relevant enough that users would consider connecting.
– 95%+ of suggestions have a clear basis for being recommended based on user connections or profile details.
Overall, LinkedIn’s accuracy rates exceed most predictive recommendation algorithms. But there is still room for improvement as more data and user feedback accumulates.
Why Do Some Connections Not Show Up?
In some cases, LinkedIn users are surprised that an existing connection is not showing up as a suggestion. There are a few potential reasons for this:
– The algorithm decided that user is not likely to be a relevant suggestion based on other data signals.
– Privacy and visibility settings of a user may restrict them from appearing to some connections.
– A previous negative response from a user through reporting or feedback influenced the absence.
– There is an aspect of the relationship that is not reflected fully in user profile data.
– The algorithm has a gap in analyzing certain types of relationship formations, such as through non-LinkedIn contexts.
Ultimately, LinkedIn has to balance the risk of creating “false positive” irrelevant suggestions with the benefit of surfacing unexpected but useful connections through “false negatives”. The system cannot reflect all real-world relationships completely.
Is the Feature Always On?
By default, all LinkedIn members see “People You May Know” suggestions automatically as part of their standard feed and profile experience. However, there are ways to limit or disable the recommendations if desired:
– In feed preferences, you can uncheck the box to stop seeing suggestions mixed into your main feed.
– On desktop under account privacy settings, you can uncheck “Allow InMail from suggested connections” to remove emails inviting you to connect from suggestions.
– The mobile app provides an option to “Pause Suggestions” which temporarily halts new suggestions for 3, 7 or 14 days.
– You can also set your profile visibility to a very limited “Private Mode” to reduce (but not eliminate) people who can be suggested to you.
For those concerned about data privacy, limiting the feature can help reduce the amount of background profile matching and analytics performed by LinkedIn to generate new recommendations. However, it also minimizes the potential value of discovering new professional connections.
Pros of Engaging with Suggestions
While users can disable LinkedIn suggestions, there are also good reasons to keep the feature active:
– It’s an easy way to expand your professional network organically.
– Suggestions often rediscover colleagues and classmates you have lost touch with over the years.
– It surfaces contacts you likely have things in common with, but might not come across otherwise.
– Broad networks create more opportunities. Wider 2nd and 3rd degree connections increase access to insights, job openings, and referrals.
– Interacting with suggestions leads to new social interactions and strengthens your existing network.
– It’s a simple way to gain new followers and listeners for your own content and personal brand.
– Ignoring and reporting irrelevant suggestions helps improve the algorithm over time for everyone.
Overall, while some suggestions may not seem useful at first glance, keeping an open mind can lead to unexpected value. The feature is designed for professional networking and development.
Cons of Engaging with Suggestions
On the other hand, there are some downsides to engaging with “People You May Know”:
– It can feel intrusive or annoying if suggestions are irrelevant to you.
– Some users prefer to grow their networks more selectively vs. bulk connections.
– It exposes your profile to new people whom you haven’t evaluated or vetted.
– Mass connection growth can undermine the credibility and exclusivity of the network.
– Some suggested contacts are essentially strangers who found you via mutual groups or indirect networks.
– It enables solicitation outreach from sales professionals, recruiters, or other opportunistic connectors.
– Responding to suggestions creates more data for LinkedIn’s algorithm to mine for further recommendations.
– Feature engagement fuels more user data collection and tracking by the platform.
Essentially, users more protective of their data and existing networks may choose to be selective. But avoiding all suggestions represents a missed opportunity to expand your horizons.
Best Practices for Managing Suggestions
Here are some recommended tips on how to engage with “People You May Know” in a strategic way:
– Curate your profile data carefully so suggestions are more relevant to you. Keep work experience, education, skills, etc. up to date.
– Connect sparingly, and focus on individuals most likely to add value based on shared affiliations or interests. Avoid mass connection behavior.
– Leverage the filtering, private mode, and profile visibility options to manage data exposure and suggestion volume.
– If you feel a suggestion is totally irrelevant, report them through the interface. This improves the system.
– Be selective responding to InMail connection requests based on the context included. Generic notes are easier to ignore.
– Consider turning off notifications so suggestions are less intrusive. Check them occasionally on your own initiative instead.
– View the “People You May Know” page for the full list of recommendations instead of just the feed snippets.
– Don’t worry about connecting with every colleague from past jobs and schools. Focus on those you want to stay in touch with.
– Monitor your PUBLIC profile page to understand how you are visible to suggestions and connections.
Overall, “People You May Know” is a valuable networking tool if used selectively. But be prudent sharing connections, data, and visibility settings.
Should I Connect with Suggestions I Don’t Know?
When suggestions surface contacts you have had no previous relationship or interactions with, it raises the question – should you still connect? Here are some pros and cons to weigh:
**Potential Pros**
– It forges new networking opportunities that may eventually prove valuable.
– You gain access to that person’s own connections and network.
– Your public profile gains another viewer and potential endorser or promoter.
– It continues the virtuous cycle of strengthening the LinkedIn social graph.
– You have full control over post-connection interactions and messaging.
**Potential Cons**
– It clutter your network with connections that have no clear value.
– It enables unwelcome communication from random contacts.
– Your data and privacy may be more exposed to strangers.
– It dilutes the credibility of a carefully curated “inner circle” on LinkedIn.
– Valuable network slots are occupied by low-relevance connections.
**Key Considerations**
– Review profiles thoroughly to look for any affiliation or interest hints of value.
– Check for other shared higher-level connections you may mutually know before connecting.
– Consider the risks of broad connection sharing with your own network.
– Determine if you can identify any future potential benefit from the person or their network.
There is no definitive right or wrong answer here. It comes down to personal networking preferences and goals. With care, some unknowns may become valuable – but focus on quality.
How Do My Profile Settings Affect Suggestions?
Your LinkedIn profile visibility settings directly impact which types of recommendations you will see:
– **Public mode** – This default option provides the most open visibility to maximize suggested connections.
– **Private mode** – Limits visibility from those outside connections, reducing unknown suggestions.
– **Profile visibility off** – Hides your profile from searches by non-connections, removing most suggestions.
Additionally:
– Feature settings like trusted colleagues/connections and data personalization will modify signals.
– Advertising preferences can minimize external data used to enrich suggestions.
– Groups and employer visibility affect visibility of shared characteristic recommendations.
– Published posts and activity also feed data that enhances connection suggestions.
**Key Takeaways**
– The more private your settings, the more suggestions are limited by data access. But you also minimize valuable discovery.
– Keeping work details public tends to produce most relevant professional suggestions.
– Higher privacy reduces unwanted solicitation, but can isolate you from new opportunities.
– Find the right balance for your preferences and networking goals. Monitor changes after adjustments.
Experiment with your profile visibility options while evaluating the quality and relevance of “People You May Know” recommendations. Lower visibility typically means fewer surprises.
Should I Report Wrong Suggestions?
If the “People You May Know” section surfaces recommendations that are clearly irrelevant, reporting them through LinkedIn’s interface is encouraged to improve the algorithm. Here’s what happens:
– The specific suggestion user is noted as not a recommendation for your profile.
– Your feedback contributes to improving LinkedIn’s matching rules and signals.
– You should see fewer similar low-quality suggestions in the future.
– LinkedIn gains data to tune relevance and ranking factors in its models.
– Your network value and experience is enhanced by removing noise.
Some key points on reporting:
– Be thoughtful separating true negatives from ones you don’t recall or interact with much currently.
– Consider if the suggestion may be reasonable from LinkedIn’s perspective based on profile data.
– Reporting is anonymous and should not offend users who show up as suggestions.
– Aggressive over-reporting undermines the usefulness of the feedback for improving quality.
In summary, judicious use of the “I don’t know this person” report option helps LinkedIn get better at making recommendations over time. But be careful not to over-optimize your network into an isolated bubble.
Conclusion
The LinkedIn “People You May Know” feature uses sophisticated data science to generate one of the most accurate and personalized professional networking recommendation engines available today. While results vary, most users find suggested connections highly relevant.
With the right profile management and privacy controls, users can optimize suggestions for their goals. The system rewards those who provide accurate profile data, selective engagement, and relevant feedback on the quality of results.
At its best, “People You May Know” facilitates valuable discovery of professional relationships that may not otherwise have connected. But judicious filtering and reporting of low-quality results is crucial to improving the experience.
In the end, networking technology can only go so far. Human judgment, common sense, and prudence are necessary to accept useful suggestions and dismiss irrelevant ones. By combining machines and users effectively, LinkedIn can continue to enhance its mission of connecting the world’s professionals to make them more productive and successful.