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Learning to Rank Personalized Search Results in Professional Networks

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SIGIR Industry track 2016

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Learning to Rank Personalized Search Results in Professional Networks

  1. 1. Recruiting SolutionsRecruiting SolutionsRecruiting Solutions Learning to Rank Personalized Search Results in Professional Networks Viet Ha-Thuc and Shakti Sinha SIGIR Industry - 2016 1
  2. 2. 2 ● Dual Roles of Search ○ Enable talent discover opportunity ○ Help companies to search for the right talent
  3. 3. Unique Nature of LinkedIn Search ▪ Heterogeneous sources – People, jobs, companies, slideshares, members’ posts, groups ▪Support many use-cases – Recruiting, connecting, job seeking, research, sales, etc. ▪Deep Personalization 3
  4. 4. Overview 4 Query Federated Search Spell Correction Query Tagging Intent Prediction People Companies Federated Search Name Title Skill Jobs
  5. 5. Personalized Job Search ▪ Short and vague queries –“San francisco”, “microsoft” –Augment queries with searcher information ▪ Skill Homophily [Li, Ha-Thuc et al. KDD’16] –“Classic” homophily: People tend to connect with similar ones –Skill homophily: People tend to apply for jobs requiring similar skills –Skills in job descriptions 5
  6. 6. Member Skills ▪ Skills – 35K+ standardized skills – Represent professional expertise ▪Challenges – Sparsity – Outlier skills ▪Approach: skill reputation 6
  7. 7. Reputation Information a decision maker uses to make a judgment on an entity with a record (*) 7 (*) “Building web reputation systems”, Glass and Farmer, 2010
  8. 8. Skill Reputation Scores [Ha-Thuc et al. BigData’15] 8 ▪ Decision Maker: searcher ▪ Record: Professional career ▪ Skill reputation: member expertise on a skill ▪ Judgment: Hire?
  9. 9. Estimating Skill Reputation 9 ● Remove outlier skills ● Infer missing ones
  10. 10. Overview 10 Query Federated Search Spell Correction Query Tagging Intent Prediction People Companies Federated Search Name Title Skill Jobs
  11. 11. ▪ Why do we need this? 11 Personalized Federated Search - Motivation
  12. 12. Personalized Federated Model [Arya, Ha-Thuc et al. CIKM’15] ▪ Searcher intent – Mine searcher profiles and past behavior to infer intent ▪ Title recruiter -> recruiting intent ▪ Search for jobs -> job seeking intent – Machine-learned models predict member intents: ▪ Job seeking ▪ Recruiting ▪ Content consuming 12
  13. 13. Calibrate Signals across Verticals ▪ Verticals associate with different intents 13 People Result Job Result Group Result Recruiting Intent Job Seeking Intent Content Consuming Intent
  14. 14. Calibrate Signals across Verticals ▪ Verticals associate with different intents 14 People Result Job Result Group Result Recruiting Intent Job Seeking Intent Content Consuming Intent
  15. 15. Calibrate Signals across Verticals ▪ Verticals associate with different intents 15 People Result Job Result Group Result Recruiting Intent Job Seeking Intent Content Consuming Intent
  16. 16. Take-Aways ▪ Text match is still important but not enough ▪ Go beyond text ▪Semi-structured data ▪Behavioral data ▪ Collaborative filtering works for skill reputation ▪ Personalized Learning-to-Rank is crucial 16
  17. 17. References ▪“Personalized Expertise Search at LinkedIn”, Ha-Thuc, Venkataraman, Rodriguez, Sinha, Sundaram and Guo, BigData, 2015 ▪“Personalized Federated Search at LinkedIn”, Arya, Ha- Thuc and Sinha, CIKM, 2015 ▪“How to Get Them a Dream Job?”, Li, Arya, Ha-Thuc, Sinha, KDD, 2016 17

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