Big data innovation_summit_2014

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Information about Big data innovation_summit_2014
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Published on April 25, 2014

Author: AnujGoyal11

Source: slideshare.net

Description

The talk I gave at the Big Data Innovation Summit.

Recruiting SolutionsRecruiting SolutionsRecruiting Solutions Recommending Jobs You May Be Interested In Anuj Goyal Recommendations at LinkedIn Anuj

§  About LinkedIn §  Search vs. Recommendations §  Recommendation Opportunities §  Evaluating Job Recommendations §  Job Recommendation Algorithm §  Challenges §  Summary 2 Overview

270+ M Company Pages >3M * Professional searches in 2012 ~5.7B 90%Fortune 100 Companies use LinkedIn to hire * *as of March 31, 2014 New Members joining ~2/sec 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 3 World’s largest professional network Over 65% of members are now international

4 Recommendation Versus Search Explicit Implicit

The Recommendations Opportunity 5

6

50%7 Value of Recommendations

Job Recommendations 8

9 Jobs You May be Interested In

Evaluation §  Upside metrics –  Are users getting relevant jobs? §  Downside metrics –  Are users getting offending jobs? 10

Evaluation - Upside §  Total Job Views (Clicks) §  Total Applications §  Total Viewers §  Total Applicants 11

Evaluation - Downside §  Applications per Click §  Clicks per Impression §  Applications per Impression §  Expert Judgments 12

User Features & Recommendation Algorithm

14 Positions Education Summary Experience Skills User Features

Corpus StatsCandidate Jobs User Base title geo company industry description functional area … Candidate General expertise specialties education headline geo experience Current Position title summary tenure length industry functional area … Similarity (candidate expertise, job description) 0.56 Similarity (candidate specialties, job description) 0.2 Transition probability (candidate industry, job industry) 0.43 Title Similarity 0.8 Similarity (headline, title) 0.7 . . . derived Matching Binary Exact matches: geo, industry, … Soft transition probabilities, similarity, … Text Recommendation Algorithm Transition probabilities Connectivity yrs of experience to reach title education needed for this title … 15 Job Collection

Challenges & Feature Engineering

Challenge: Entity Resolution 17

‘IBM’ has 13000+ variations -  ibm – ireland -  ibm research -  T J Watson Labs -  International Bus. Machines Are All Companies The Same? 18

-  Software Engineer -  Technical Yahoo -  Member Technical Staff -  Software Development Engineer -  SDE Are All Titles The Same? 19

Same company with different name Same name but different companies Name Variations for IBM? “Orion” refers to 20 diff. companies large scale: 100M+ members, 2M+ company entities IBM: Intl Brotherhood of Magicians ~ 13000 Challenges – Entity Resolution 20

§  Binary classifier (LR), not ranker §  P({position, company entity} is a match) §  Features §  Content §  Social §  Behavior §  Company candidate set leveraged from Social graph and cosine similarity 97% Precision at 50% Coverage Asonam’11, KDD’11 Challenges – Entity Resolution 21 Precision Coverage

Challenges – Geo Location 22

§  Zip code mapped to Regions §  How sticky are those locations? Feature Engineering – Sticky locations 23

§  Open to relocation ? §  Region similarity based on profiles or network §  Region transition probability §  Predict individuals propensity to migrate and most likely migration target Feature Engineering – Sticky locations 24

Feature Engineering – The Network effect 25

Hybrid Recommendation Title : Research Engineer Company : Yahoo! Location : CA,USA Skills : Stats, ML, Java Title : Data Scientist Company : Samsung Location : PA,USA Skills : Stats, R Title : Analyst Company : Microsoft Location : CA, USA Skills : R, ML Title : Research Engineer <1>, Data Scientist <1>, Analyst <1> Company : Yahoo<1>, Samsung<1>, Microsoft<1> Location : CA,USA <2>, PA,USA<1> Skills : Stats<2>, ML<2>, R<2>, Java<1> Applicant Features Distribution Data Scientist / Senior Data Scientist San Jose 26

Information Gain Pick Top K overrepresented features from the applicants distribution A representative projection of the job in the member feature space 27 Hybrid Recommendation

§  Why Jobs Recommendations are Different §  Recommendation Algorithm §  Challenges –  Entity Resolution –  Location Resolution 28 Summary

Questions? Contact: agoyal@linkedin.com We’re Hiring! http://data.linkedin.com/ Thank You! 29

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