LinkedIn is the world’s largest professional networking platform, with over 722 million users as of February 2023. With so many members connecting and interacting, recommendations are a key feature on LinkedIn that allow users to endorse others for their skills and accomplishments.
But how exactly does LinkedIn determine which recommendations to show you? Is there a special algorithm involved? Let’s take a closer look at how recommendations work on LinkedIn.
The Basics of LinkedIn Recommendations
On LinkedIn, members can give and receive two types of recommendations:
- Skill endorsements – endorsements for specific skills and abilities
- Written recommendations – detailed written statements endorsing a member’s qualifications
Skill endorsements are simple one-click actions members can give to endorse others for skills like “Social Media Marketing” or “Public Speaking.” Written recommendations require writing out a more thorough recommendation showcasing why you believe the member is qualified for a certain skill or role.
Both types of recommendations are displayed on a member’s profile and considered social proof of their abilities. Members can choose to ask others directly for recommendations or endorsements. You can also proactively give recommendations to your connections without being asked.
Factors That Determine Recommendations
So what determines which recommendations you see for a particular member? LinkedIn considers several factors:
Relevance
LinkedIn aims to show the most relevant recommendations first – those written by people who would logically vouch for the member’s skills and qualifications. For example, a recommendation from a current colleague would be more relevant than one from a distant connection.
Relationships
Stronger LinkedIn relationships are prioritized when displaying recommendations. Recommendations from 1st degree connections are shown before 2nd or 3rd degree connections.
Interaction History
If you have interacted more deeply with certain connections on LinkedIn, through messaging or profile views, recommendations from those connections may be favored.
Recent Activity
More recent recommendations are typically shown before older ones, as they represent the member’s latest endorsements.
Content
For written recommendations, those that are more detailed and enthusiastically worded tend to be displayed before sparse or generic endorsements.
Does LinkedIn Use a Recommendation Algorithm?
While the exact ranking and filtering process is proprietary, LinkedIn has confirmed they do utilize an algorithm to determine which recommendations to display in what order. The goal is to show the most credible and relevant recommendations first.
In a 2018 interview, LinkedIn engineering executive Ajay Agarwal stated:
“We do have a machine learning model that predicts what [recommendations] you’re most likely to appreciate and shows you those first before deciding whether to show the rest.”
So LinkedIn does apply machine learning and algorithms to personalize which recommendations you see. This system considers the factors like relationship strength, relevance, and level of detail when predicting which recommendations will be most useful for each member.
How You Can Improve Your Recommendations
Knowing that LinkedIn uses an algorithm to determine recommendations visibility, here are some tips to improve the quality and relevance of your own endorsements:
- Ask for recommendations from colleagues and managers who closely work with you and can enthusiastically endorse your skills.
- When possible, obtain recommendations from higher-ups and well-known connections, as those often carry more weight.
- Prompt past colleagues to write recommendations right when leaving a job while their positive memories are fresh.
- Endorse your top connections for their major skills so they’ll be more likely to reciprocate.
- Thank those who endorse you and return the favor by endorsing their skills.
- Ask the recommenders to include detailed, specific examples of times when you demonstrated the skills and qualities they are endorsing.
Following these best practices can help you get more recommendations that will be highly ranked and visible according to LinkedIn’s algorithms.
The Pros and Cons of LinkedIn’s Recommendation Algorithm
LinkedIn using an algorithm to determine which recommendations are shown has both advantages and disadvantages:
Pros
- Surfaces the most relevant, useful recommendations first
- Filters out spammy or irrelevant recommendations
- Considers recommendation context and relationship strength
- Adapts to each user’s unique connections and history
- Automates recommendation visibility vs. manual curation
Cons
- Lacks full transparency on how the algorithm works
- Could reinforce “walled gardens” if it favors internal endorsements
- Limits exposure for legitimate but lower-ranked recommendations
- Reduces control vs. chronological or manually sorted recommendations
- Could be manipulated by groups all endorsing each other
Overall, most agree the benefits outweigh the downsides for an algorithmic approach. But LinkedIn should continue improving transparency and auditing the system for fairness.
Frequently Asked Questions
Does the order of my recommendations matter?
Yes, the order of recommendations prominently impacts visibility. Recommendations shown first are seen the most, so LinkedIn’s algorithm aims to show the strongest endorsements first.
How often does LinkedIn update the recommendation algorithm?
LinkedIn does not share details on how often they update the recommendation algorithm. But they likely tune it periodically to optimize for relevance and reduce potential spam/abuse.
Can I pay to increase the visibility of a recommendation?
No, LinkedIn does not allow members to pay to boost certain recommendations. Their visibility is determined algorithmically based on relevance factors.
Should I focus on quantity or quality of recommendations?
Quality is more important than quantity when it comes to recommendations. A few detailed, enthusiastic recommendations from credible sources are ideal. Too many sparse recommendations could actually dilute a profile.
How does LinkedIn prevent recommendation spam/abuse?
LinkedIn’s algorithm likely detects suspicious patterns like bulk recommendations or members endorsing those far outside their network. Proactively detecting and filtering suspect endorsements helps maintain recommendation integrity.
Conclusion
In summary, LinkedIn does leverage a data-driven algorithm to determine which recommendations display for each member. The goal is to show the most relevant and credible recommendations first based on the relationship, context, and quality of the endorsement. Understanding how this algorithm works can help members showcase their skills effectively through trusted recommendations.
While not everything is known about LinkedIn’s proprietary system, the general factors considered include relevance, relationship strength, recent activity, content quality, and patterns suggesting authenticity. Members can optimize their profile by focusing on quality over quantity with recommendations and endorsements from those who know their work closely and can enthusiastically vouch for their abilities.
