LinkedIn recommendations are an important part of establishing credibility and trust on the platform. Recommendations allow LinkedIn members to endorse others for their skills, accomplishments, and character. However, many users wonder how LinkedIn actually determines which recommendations to show you. Here is an in-depth look at how LinkedIn’s recommendation algorithm works.
Factors LinkedIn Considers for Recommendations
LinkedIn’s recommendation algorithm is designed to surface the most relevant, trustworthy, and useful recommendations for each member. There are several factors that LinkedIn considers:
Your Connections
The most heavily weighted factor is your 1st degree connections on LinkedIn. Recommendations from people you are directly connected to are deemed more relevant and credible. LinkedIn will prominently display recommendations from your connections at the top of your profile.
2nd & 3rd Degree Connections
After 1st degree connections, LinkedIn also considers recommendations from 2nd and 3rd degree connections. However, these carry less weight and credibility compared to 1st degree connections. The algorithm views them as less relevant the further away someone is in your extended network.
Skills & Expertise
If your connections recommend you for specific skills or expertise, LinkedIn will factor that in. For example, if several of your connections endorse you for marketing, project management, or other skills, LinkedIn will likely recommend you based on those keywords.
Content Engagement
If you regularly engage with someone’s content on LinkedIn by liking, commenting, and sharing, you are more likely to be recommended by that person. The algorithm sees active engagement as a signal that you have a closer relationship.
Profile Strength
The strength of your own LinkedIn profile can impact recommendations. Profiles with a photo, work experience, education, skills, accomplishments, and recommendations tend to attract more recommendations. A more robust profile indicates credibility and trustworthiness.
Shared Connections & Experiences
LinkedIn will examine how closely you are connected to someone. If you have many shared connections and overlapping work experience, especially at the same company, you are more likely to be recommended by them.
LinkedIn’s Recommendation Models
LinkedIn uses sophisticated machine learning models to determine the most relevant recommendations for each user. There are two primary models:
Collaborative Filtering
This model analyzes patterns across the entire LinkedIn member network. It looks at how similar members receive and interact with recommendations. The model aims to predict which recommendations you are most likely to find useful based on recommendations your “lookalike” members engage with.
Social Graph Modeling
This model specifically examines your own social graph and activity. It looks at who you are connected to, how you interact with connections, shared experiences, groups, content, and other engagement signals. The model tailors recommendations based on your unique LinkedIn social graph.
Why You Might See Certain Recommendations
Based on LinkedIn’s models and criteria, here are some common reasons you might see certain recommendations:
You recently connected with someone
New 1st degree connections are very likely to recommend you, since LinkedIn wants to encourage new connections.
You engaged with someone’s content
Liking, commenting on, or sharing someone’s posts makes it more likely they will recommend you.
You have overlapping connections & experiences
Many shared connections and past work experience, especially at the same companies, increases the likelihood of recommendations between members.
You have similar roles & industries
LinkedIn will recommend industry peers, people with similar job titles and roles, since their recommendations carry more weight.
You share common skills & interests
The more common skills and interests you share with another member, the more likely LinkedIn will recommend you to each other.
Profile strength & completeness
A well-rounded, fleshed out profile indicates credibility and attracts more recommendations.
It’s a 2nd degree connection recommending you
2nd degree connections are still reasonably well-connected enough for LinkedIn to recommend them.
Examples of Quality Recommendations
Not all recommendations carry the same weight. High quality recommendations that LinkedIn deems more credible and relevant include:
1st Degree Connections
Getting recommended by people you are directly connected to is the highest trust signal.
Senior Level Connections
Being recommended by managers, executives, partners, CXOs, founders, and leaders indicates strong credibility.
Skills & Expertise
When connections specifically endorse your skills, qualifications, and expertise it adds more value than a generic recommendation.
Detailed & Personalized
Recommendations that have detailed, personalized stories and anecdotes about working with you are more meaningful.
Wide Range of Connections
Being recommended by connections across different companies, industries, and roles demonstrates broad credibility.
Call Out Key Accomplishments
When recommendations cite specific projects, accomplishments, outcomes, and quantifiable results you achieved it carries more weight.
Risks of Low Quality Recommendations
While most recommendations are authentic, LinkedIn also works to prevent low-quality recommendations that may indicate fraud. Here are some red flags:
Mass Recommendations
When a member recommends large numbers of connections in a short period, it can appear suspicious.
Excessive Praise
Over-the-top, general recommendations that seem fake, forced, or exaggerate someone’s qualifications come across as inauthentic.
Quid Pro Quo
When multiple connections clearly recommend each other simultaneously, it suggests a tit-for-tat situation rather than genuine recommendations.
Sparse Profiles
When the recommender has a thin profile lacking details, connections, or recommendations themselves, it undermines their credibility.
Irrelevant Connections
Being recommended by someone who has no obvious affiliation, experience, or logical connection can seem odd and suspicious.
Best Practices for Quality Recommendations
To garner the most helpful and credible recommendations, members should follow these best practices:
Recommend Thoughtfully
Take the time to write meaningful, useful recommendations for your genuinely respected connections. Include detailed anecdotes when possible.
Recommend Multi-directionally
Avoid recommending only one person multiple times. Recommend a diverse mix of your connections.
Focus on Skills & Expertise
When possible, speak to someone’s specific capabilities, rather than just general praise. Mention projects and results.
Recommend Connections Relevant to You
The more logical reasons you have to be affiliated with someone (shared company, industry, role, group, etc) the stronger the recommendation.
Keep Your Profile Robust
A complete, detailed profile will earn you higher quality recommendations in return.
Engage With Your Network
Liking, commenting on, and sharing content with connections makes them more inclined to recommend you.
Reviewing Your Own Recommendations
It is also good practice to periodically review the recommendations others have written for you:
Audit Recommendations
Read through all your recommendations and confirm they come from credible sources and seem authentic.
Remove Outdated Ones
If you have recommendations from roles or companies you left long ago that no longer seem relevant, consider removing them.
Address Suspicious Ones
If certain recommendations seem questionable or in violation of LinkedIn’s policies, flag them for LinkedIn to review.
Thank Recommenders
When appropriate, thank those who took the time to thoughtfully recommend you. Nurture these valuable connections.
Request Updates
If someone has recommended you years ago, consider reaching out and asking them to update it based on your recent accomplishments and growth.
Conclusion
In summary, LinkedIn’s recommendation algorithm relies heavily on your 1st degree connections, shared experiences, engagement activity, and profile completeness to determine relevance. Members should give and receive recommendations thoughtfully and organically. Authentic, detailed recommendations from respected connections carry significant weight in establishing professional credibility on LinkedIn.
