This document summarizes LinkedIn's new skill endorsement feature, which allows members to endorse each other's skills. It discusses how skill endorsements have grown virally with over 800 million endorsements in 4 months. It also describes how LinkedIn uses data from profiles and relationships to suggest relevant skill endorsements to members.
8. Email News Feed Notification
2) Viral Loops & Network Effects
A
B B “accepts”
endorses
notified endorsement
B
Endorsement
recommendations
B B
endorses endorses
C D
14. Profile
Building the Skills Dictionary (specialties)
What is the skills dictionary?
– A growing taxonomy of skills
Tokenization
Clustering
– Generated by mining profiles and maintained by the
Skills team at LinkedIn
Crowdsourcing
– Created using clustering and crowdsourcing.
– Multiple phrases, acronyms, and misspellings map to
a single standardized skill.
250+ different phrases map to “Microsoft Office”
Taxonomy
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16. Skills Dictionary: Microsoft Office
– ms office
– ms office suite
– computer skills including ms office
– office 97
– microsoft office user
Microsoft Office
– mac office
– microsoft office 2003 & 2007 (Skill ID = 366)
– microsoft office suits
– microsoft ofice
– microsoft ofiice
– ms office certified
– office 98
– …
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21. Skills Classification
Use skill dictionary metadata to tag, standardize and infer skills
Run classifiers for each skill on member profiles
Public Speaking
Ruby on Rails
Entrepreneurship
Microsoft Office
AP Style
21
22. Document
Tagging Skill Phrases (ex: Profile)
Tagging: Extract potential skill phrases from text
Lead designer and engineer for the implementation of a user-
centric, fully-configurable UI for data aggregation and reporting.
Developed over 20 SaaS custom applications using Python,
Javascript and RoR. Tokenization
Phrases
JavaScript RoR SaaS Python
(up to 6 words)
Standardize unambiguous phrase variants Skills Tagger
ror
rubyonrails Skills
ruby on rails development Ruby on Rails (unordered)
ruby rails
ruby on rail Skills Classifier
Skills
(ranked by relevance)
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24. Skills Classification on Member Profiles
The skills classifier computes the likelihood of a member to have a skill based on
the member’s profile, other profiles which share common attributes and their
connections.
Tagging Standardization Inference
Profile
Tokenize free Transform tags Rank skills by
text
text into phrase tags into potential skills likelihood
Profile attributes & network signals
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25. Profile
Skill Inference
How suggested/inferred skills work:
Extract
– Profiles with skills help build a massive dataset of attributes
(attribute: skills).
Feature
- Company ID
Example with a title: Vectors
- Title ID
- Groups ID
Software Engineer Java 100 000
- Industry ID
Software Engineer C++ 88 000 -…
…
Skills Classifier
Title Skill Occurrences
Skills
(ranked by likelihood)
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26. Profile
Skill Inference
How suggested/inferred skills work:
Extract
– The skill likelihood is a conditional model attributes
Feature
- Company ID
– Probabilities are combined using a Naïve Bayes Vectors
- Title ID
Classifier - Groups ID
- Industry ID
-…
Skills Classifier
If you are an engineer at Apple, you probably know
about iPhone Development.
Skills
(ranked by likelihood)
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32. Suggesting Endorsements
Candidate
People-skill combinations in a member‟s network generation
Binary classification
Feature
- Company
Features Vectors
- Title
– Skill inference score - Groups
– Company overlap - Industry
– School overlap -…
– Group overlap
– Industry and functional area similarity Classifier
– Title similarity
– Site interactions
– Co-interactions
Suggested Endorsements
(ranked by likelihood)
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