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LinkedInsight

Find the skills and experience !
of your professional network
Cian Menzel-Jones - Insight 2014A
LinkedInsight
Changing fields / Looking for a new career ?!
!
!
!
!
!
!
!
LinkedInsight
Changing fields / Looking for a new career ?!
!

What are the skills that matter and will get you noticed ?!
!
!
!
!
!
LinkedInsight
Changing fields / Looking for a new career ?!
!

What are the skills that matter and will get you noticed ?!
!

Who in your professional network can help ?!
!
!
!
LinkedInsight
Changing fields / Looking for a new career ?!
!

What are the skills that matter and will get you noticed ?!
!

Who in your professional network can help ?!
!

Where are the leaders in the field (future mentors) ?!
!
LinkedInsight
Changing fields / Looking for a new career ?!
!

What are the skills that matter and will get you noticed ?!
!

Who in your professional network can help ?!
!

Where are the leaders in the field (future mentors) ?!
!

Which groups should you join to stay up to date ?
LinkedInsight
LinkedInsight
Search for keyword

8000+ user profiles
{Headline, background,!
experience}
LinkedInsight
Search for keyword

8000+ user profiles
{Headline, background,!
experience}

Find the popular groups
LinkedInsight
Search for keyword

8000+ user profiles
{Headline, background,!
experience}

Find the popular groups
{Skills+Endorsements!
of group members}

Ranked skill set

e.g. “data scientist”
Machine Learning, Data Mining, Analytics, Data Analysis, Big Data, …
LinkedInsight
Search for keyword

8000+ user profiles
{Headline, background,!
experience}

Find the popular groups
{Skills+Endorsements!
of group members}

Ranked skill set

e.g. “data scientist”
Machine Learning, Data Mining, Analytics, Data Analysis, Big Data, …

Want to get the signal out of the noise
LinkedInsight
Classification: extract feature importance
Split members of the groups by experience (based on keyword)
LinkedInsight
Classification: extract feature importance
Split members of the groups by experience (based on keyword)
{Collect skill sets of each}

Use most popular skills as features (include education level)
LinkedInsight
Classification: extract feature importance
Split members of the groups by experience (based on keyword)
{Collect skill sets of each}

Use most popular skills as features (include education level)
{RandomForestClassifier}

Accuracy: 83%, F1 : 82 %

Order features by weights
LinkedInsight
About Me : Cian Menzel-Jones

Numerical simulations of!
light-matter interactions

Developed approaches to extract molecular structure !
and to image dynamics from spectroscopic data
LinkedInsight
LinkedInsight
Extensions!
!
1) Extract trending skills (comparing junior vs senior employers; time-dependence)!
!
2) Group similar skills using PCA ( e.g. SQL, MySQL, SQLite,…)!
!
3) Employment behavior in different sectors and companies!
!
!
!

— Job retention and promotion rate !
— Skills by company, education level, etc. given keyword!
— Observe 1-year cliff, who’s been most susceptible!

!
4) Link to online learning  (clustering of courses based on word similarities)!
!
5) Job/Field recommendations based on your skill set!
!
!

— Presents users unknown career possibilities!
— Location map of contacts, leaders and companies with skill sets!

!
!

6) TF-IDF of words in user’s background and profile given keyword
LinkedInsight
Challenges!
!
LinkedIn API or public profile (limited data)!
!
Scrap my own network!
! Change status to invisible!
!
!

Executive membership to allow viewing of 700 connections!
Join groups to see up to 500 members (See 3rd degree connections)!

!
Selenium Webdrive!
!
!

Javascript (lazy scroll, tab refresh,…)!
Random waits between scraps averaging 30s (cut off once a day)!

!
Parsing pages and profiles!
!
!

Logged in users connections, groups and companies (and members)!
Each user profile (title, background, education, skills, …)!

!
Algorithm!
!

How to represent features based on frequency, endorsements, education level,…
LinkedInsight
Linkedinsight-week4
Linkedinsight-week4
Linkedinsight-week4
Linkedinsight-week4
Linkedinsight-week4
Linkedinsight-week4
Linkedinsight-week4
Linkedinsight-week4

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Linkedinsight-week4

  • 1. LinkedInsight Find the skills and experience ! of your professional network Cian Menzel-Jones - Insight 2014A
  • 2. LinkedInsight Changing fields / Looking for a new career ?! ! ! ! ! ! ! !
  • 3. LinkedInsight Changing fields / Looking for a new career ?! ! What are the skills that matter and will get you noticed ?! ! ! ! ! !
  • 4. LinkedInsight Changing fields / Looking for a new career ?! ! What are the skills that matter and will get you noticed ?! ! Who in your professional network can help ?! ! ! !
  • 5. LinkedInsight Changing fields / Looking for a new career ?! ! What are the skills that matter and will get you noticed ?! ! Who in your professional network can help ?! ! Where are the leaders in the field (future mentors) ?! !
  • 6. LinkedInsight Changing fields / Looking for a new career ?! ! What are the skills that matter and will get you noticed ?! ! Who in your professional network can help ?! ! Where are the leaders in the field (future mentors) ?! ! Which groups should you join to stay up to date ?
  • 8. LinkedInsight Search for keyword 8000+ user profiles {Headline, background,! experience}
  • 9. LinkedInsight Search for keyword 8000+ user profiles {Headline, background,! experience} Find the popular groups
  • 10. LinkedInsight Search for keyword 8000+ user profiles {Headline, background,! experience} Find the popular groups {Skills+Endorsements! of group members} Ranked skill set e.g. “data scientist” Machine Learning, Data Mining, Analytics, Data Analysis, Big Data, …
  • 11. LinkedInsight Search for keyword 8000+ user profiles {Headline, background,! experience} Find the popular groups {Skills+Endorsements! of group members} Ranked skill set e.g. “data scientist” Machine Learning, Data Mining, Analytics, Data Analysis, Big Data, … Want to get the signal out of the noise
  • 12. LinkedInsight Classification: extract feature importance Split members of the groups by experience (based on keyword)
  • 13. LinkedInsight Classification: extract feature importance Split members of the groups by experience (based on keyword) {Collect skill sets of each} Use most popular skills as features (include education level)
  • 14. LinkedInsight Classification: extract feature importance Split members of the groups by experience (based on keyword) {Collect skill sets of each} Use most popular skills as features (include education level) {RandomForestClassifier} Accuracy: 83%, F1 : 82 % Order features by weights
  • 16. About Me : Cian Menzel-Jones Numerical simulations of! light-matter interactions Developed approaches to extract molecular structure ! and to image dynamics from spectroscopic data
  • 18. LinkedInsight Extensions! ! 1) Extract trending skills (comparing junior vs senior employers; time-dependence)! ! 2) Group similar skills using PCA ( e.g. SQL, MySQL, SQLite,…)! ! 3) Employment behavior in different sectors and companies! ! ! ! — Job retention and promotion rate ! — Skills by company, education level, etc. given keyword! — Observe 1-year cliff, who’s been most susceptible! ! 4) Link to online learning  (clustering of courses based on word similarities)! ! 5) Job/Field recommendations based on your skill set! ! ! — Presents users unknown career possibilities! — Location map of contacts, leaders and companies with skill sets! ! ! 6) TF-IDF of words in user’s background and profile given keyword
  • 19. LinkedInsight Challenges! ! LinkedIn API or public profile (limited data)! ! Scrap my own network! ! Change status to invisible! ! ! Executive membership to allow viewing of 700 connections! Join groups to see up to 500 members (See 3rd degree connections)! ! Selenium Webdrive! ! ! Javascript (lazy scroll, tab refresh,…)! Random waits between scraps averaging 30s (cut off once a day)! ! Parsing pages and profiles! ! ! Logged in users connections, groups and companies (and members)! Each user profile (title, background, education, skills, …)! ! Algorithm! ! How to represent features based on frequency, endorsements, education level,…