Learning to Rank Personalized Search Results in Professional Networks
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Transcript of Learning to Rank Personalized Search Results in Professional Networks
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Recruiting SolutionsRecruiting SolutionsRecruiting Solutions
Learning to Rank Personalized Search Results in
Professional Networks
Viet Ha-Thuc and Shakti Sinha
SIGIR Industry - 2016
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● Dual Roles of Search
○ Enable talent discover opportunity
○ Help companies to search for the right talent
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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
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Overview
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Query
Federated SearchSpell Correction
Query Tagging
Intent Prediction
People Companies
Federated Search
Name Title Skill
Jobs
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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
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Member Skills
▪ Skills
– 35K+ standardized skills
– Represent professional
expertise
▪Challenges– Sparsity
– Outlier skills
▪Approach: skill reputation
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ReputationInformation a decision maker uses to make a
judgment on an entity with a record (*)
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(*) “Building web reputation systems”, Glass and Farmer, 2010
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Skill Reputation Scores [Ha-Thuc et al. BigData’15]
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▪ Decision Maker: searcher
▪ Record: Professional
career
▪ Skill reputation: member
expertise on a skill
▪ Judgment: Hire?
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Estimating Skill Reputation
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● Remove outlier skills
● Infer missing ones
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Overview
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Query
Federated SearchSpell Correction
Query Tagging
Intent Prediction
People Companies
Federated Search
Name Title Skill
Jobs
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▪ Why do we need this?
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Personalized Federated Search - Motivation
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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
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Calibrate Signals across Verticals
▪ Verticals associate with different intents
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People Result
Job Result
Group Result
Recruiting
Intent
Job Seeking
Intent
Content
Consuming
Intent
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Calibrate Signals across Verticals
▪ Verticals associate with different intents
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People Result
Job Result
Group Result
Recruiting
Intent
Job Seeking
Intent
Content
Consuming
Intent
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Calibrate Signals across Verticals
▪ Verticals associate with different intents
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People Result
Job Result
Group Result
Recruiting
Intent
Job Seeking
Intent
Content
Consuming
Intent
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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
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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
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