LinkedIn Endorsements: Reputation, Virality, andSocial TaggingOReilly Strata - February 28, 2013Sam Shah @sam_shahPete Skomoroch @peteskomoroch2012 LinkedIn Corporation. All Rights Reserved.

Sam Shah Principal Engineer and Engineering Manager @sam_shah www.linkedin.com/in/shahsam Peter Skomoroch Principal Data Scientist @peteskomoroch www.linkedin.com/in/peterskomoroch2012 LinkedIn Corporation. All Rights Reserved.

LinkedIn: The Professional Profile of Record 200+M Members 200M Member Profiles 2012 LinkedIn Corporation. All Rights Reserved. 3

LinkedIns Latest Data Product: Skill Endorsements 4

Viral Growth: 800M Endorsements in 4 Months 5

Data Amplifies Desire1. Desire + Social Proof2. Viral Loops + Network Effects3. Data Foundation + Recommendation Algorithms 6

1) Desire & Social Proof 7

Email News Feed Notification2) Viral Loops & Network Effects A B B accepts endorses notified endorsement B Endorsement recommendations B B endorses endorses C D

3) Data Foundation: Skills & Suggested Skills 9

Data Foundation: LinkedIn Skills 10

Social Tagging Accelerates Adoption Skill marketingSkill recommendations Virality only Suggested endorsements 2012 LinkedIn Cororation. All Rights Reserved.

Outline Skill discovery Skill tagging Skill recommendations Suggested endorsements 12

Unsupervised Topic Discovery from Profiles Extract 13

ProfileBuilding 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 14

Topic Clustering & Phrase Sense Disambiguation 15

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 16

Deduplication Signals from Mechanical Turk 17

Sample Task for Mechanical Turk Workers 18

Skill Phrase Deduplication 19

Outline Skill discovery Skill tagging Skill recommendations Suggested endorsements 20

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

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) 22

Outline Skill discovery Skill tagging Skill recommendations Suggested endorsements 23

Skills Classification on Member Profiles The skills classifier computes the likelihood of a member to have a skill based on the members profile, other profiles which share common attributes and their connections. Tagging Standardization InferenceProfile Tokenize free Transform tags Rank skills by text text into phrase tags into potential skills likelihood Profile attributes & network signals 24

ProfileSkill 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) 25

ProfileSkill Inference How suggested/inferred skills work: Extract The skill likelihood is a conditional model attributes Feature - Company ID Probabilities are combined using a Nave 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) 26

Skill Suggestions for Your LinkedIn Profile 4% Conversion 49% Conversion 29

Outline Skill discovery Skill tagging Skill recommendations Suggested endorsements 30

Social Tagging via Skill Endorsements 31

Suggesting Endorsements Candidate People-skill combinations in a members 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) 32

Social Tagging Accelerates Adoption Skill marketingSkill recommendations Skill endorsements 2012 LinkedIn Cororation. All Rights Reserved.

Can We Find Influencers In Venture Capital? 34

Which Skills Are Important for a Data Scientist? 35

What Technologies are Professionals Adopting? 36

Data Amplifies Desire1. Desire + Social Proof2. Viral Loops + Network Effects3. Data Catalyst + Recommendation Algorithms 37

Infrastructure Apache Hadoop: Parallel processing architecture Apache Kafka: Ingress pipes Azkaban: Hadoop scheduler Voldemort: Egress database Apache Pig: High-level MR language DataFu: Convenience routineshttp://data.linkedin.comR. Sumbaly, J. Kreps, and S. Shah. The Big Data ecosystem at LinkedIn. In SIGMOD 2013 (to appear). 2012 LinkedIn Corporation. All Rights Reserved. 38

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