Skills, Reputation, and Search

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This keynote presentation describes the critical role that search and Lucene has in building next generation products that understand reputation and relevance. We also describe how data science and machine learning have been applied at LinkedIn to collect, interpret, and index data around topical reputation. Lucene Revolution is the biggest open source conference dedicated to Apache Lucene/Solr.

Tecnologia Educação

Skills, Reputation, and Search
Pete Skomoroch
Principal Data Scientist, LinkedIn

Vision: Create Economic Opportunity for Every Professional
2
Location

LinkedIn Search: Connecting Talent with Opportunity
4

Skills Correlated with the Job Title “Data Scientist”
5

Reputation: Building the Endorsement Graph
14

15
Viral Growth: 1 Billion Endorsements in 5 Months

How Did We Gather this Data?
16
1. Desire + Social Proof
2. Viral Loops + Network Effects
3. Data Foundation + Recommendation Algorithms

A
endorses
B
B
notified
B “accepts”
endorsement
B
endorses
C
B
endorses
D
Endorsement
recommendations
Email NotificationNews Feed
2) Viral Loops & Network Effects

3) Data Foundation: Skills & Suggested Skills
19

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

Outline
22
Skill discovery
Skill tagging
Skill recommendations
Suggested endorsements

Skill Discovery: Unsupervised Topics from Profiles
23
Extract

Topic Clustering & Phrase Sense Disambiguation
24

Deduplication Signals from Mechanical Turk
25

Sample Task for Mechanical Turk Workers
26

Outline
28
Skill discovery
Skill tagging
Skill recommendations
Suggested endorsements

Outline
30
Skill discovery
Skill tagging
Skill recommendations
Suggested endorsements

Outline
38
Skill discovery
Skill tagging
Skill recommendations
Suggested endorsements

Social Tagging via Skill Endorsements
39

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

Data Amplifies Desire
41
1. Desire + Social Proof
2. Viral Loops + Network Effects
3. Data Catalyst + Recommendation Algorithms

Over 58 Million Profiles are now Tagged with Skills
42

All This Data Flows Back Into Our Lucene Index
43

Helping us Connect Talent & Opportunity
44
Location

Questions?
We’re hiring: data.linkedin.com
@peteskomoroch
©2012 LinkedIn Corporation. All Rights Reserved. 45

CONTACT
Pete Skomoroch
@peteskomoroch
http://data.linkedin.com

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Skills, Reputation, and Search

1. Skills, Reputation, and Search Pete Skomoroch Principal Data Scientist, LinkedIn

2. Vision: Create Economic Opportunity for Every Professional 2 Location

4. LinkedIn Search: Connecting Talent with Opportunity 4

5. Skills Correlated with the Job Title “Data Scientist” 5

6. Skills Related to “Big Data” 6

7. Information Retrieval 7

8. Soul Retrieval 8

9. 9

10. Lucene on LinkedIn 10

11. Lucene Endorsement Graph 11

12. Solr on LinkedIn 12

13. Solr Endorsement Graph 13

14. Reputation: Building the Endorsement Graph 14

15. 15 Viral Growth: 1 Billion Endorsements in 5 Months

16. How Did We Gather this Data? 16 1. Desire + Social Proof 2. Viral Loops + Network Effects 3. Data Foundation + Recommendation Algorithms

17. 17 1) Desire & Social Proof

18. A endorses B B notified B “accepts” endorsement B endorses C B endorses D Endorsement recommendations Email NotificationNews Feed 2) Viral Loops & Network Effects

19. 3) Data Foundation: Skills & Suggested Skills 19

20. Data Foundation: LinkedIn Skills 20

22. Outline 22 Skill discovery Skill tagging Skill recommendations Suggested endorsements

23. Skill Discovery: Unsupervised Topics from Profiles 23 Extract

24. Topic Clustering & Phrase Sense Disambiguation 24

25. Deduplication Signals from Mechanical Turk 25

26. Sample Task for Mechanical Turk Workers 26

27. Skill Phrase Deduplication 27

28. Outline 28 Skill discovery Skill tagging Skill recommendations Suggested endorsements

29. 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. Tagging Skill Phrases  Tagging: Extract potential skill phrases from text  Standardize unambiguous phrase variants 29 JavaScript RoR SaaS Python ror rubyonrails ruby on rails development ruby rails ruby on rail Ruby on Rails Document (ex: Profile) Tokenization Skills Tagger Phrases (up to 6 words) Skills Classifier Skills (unordered) Skills (ranked by relevance)

30. Outline 30 Skill discovery Skill tagging Skill recommendations Suggested endorsements

31. Skill Inference  How suggested/inferred skills work: – The skill likelihood is a conditional model – Probabilities are combined using a Naïve Bayes Classifier  If you are an engineer at Apple, you probably know about iPhone Development. 31 Profile Extract attributes - Company ID - Title ID - Groups ID - Industry ID - … Skills Classifier Skills (ranked by likelihood) Feature Vectors

32.

33.

34.

35.

36.

37. Skill Recommendations for Your LinkedIn Profile 37 49% Conversion 4% Conversion

38. Outline 38 Skill discovery Skill tagging Skill recommendations Suggested endorsements

39. Social Tagging via Skill Endorsements 39

41. Data Amplifies Desire 41 1. Desire + Social Proof 2. Viral Loops + Network Effects 3. Data Catalyst + Recommendation Algorithms

42. Over 58 Million Profiles are now Tagged with Skills 42

43. All This Data Flows Back Into Our Lucene Index 43

44. Helping us Connect Talent & Opportunity 44 Location

46. CONTACT Pete Skomoroch @peteskomoroch http://data.linkedin.com

Skills, Reputation, and Search

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