Next-Generation LinkedIn Talent Search

Ryan WuStaff Machine Learning Scientist & Tech LeadLinkedIn Corporation

Outline

Mission & Product

Expertise Search

Candidate Discovery via Similar Profiles Modeling

Search By Example

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• 200+ countries and territories

• 2+ new members per second

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● Dual Roles of Search○ Enable talent discover opportunity○ Help companies to search for the right talent

Standardized Member ProfileAt its core, LinkedIn is a digital representation of the business world—a collection of the people, companies, educations, skills, jobs, and the connections between them. Our products use this data to connect members with relevant information, contacts, content, and opportunities. The success of these products relies on data standardization, our ability to understand user data and to effectively make use of this information.

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Flagship Search

Recruiter Search

Sales Navigator Search

Agenda

Mission & Product

Expertise Search

Candidate Discovery via Similar Profiles Modeling

Search By Example

Expertise (Skill) Search at LinkedIn

▪Skills– 104’s of standardized skills– Members get endorsed for

skills listed on their profile.– Represent professional

expertise. 8

Searching by Skills ▪Unique challenges to LinkedIn expertise Search

– Scale: 450M members x 35K standardized skills– Sparsity of skills in profiles

– Personalization9

…

Skill Reputation Scores [BigData’15]

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▪Decision Maker: searcher

▪Record: Professional career

▪Skill reputation: member expertise on a skill

▪Judgment: Hire?

Estimating Skill Reputation

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Endorse profile

browsemap

? .85 .45? ? .35? .42 ?? ? .05M

embe

rs

Skills

P(expert| member, skill)

Supervised Learning algorithm

Estimating Skill Reputation

12

Endorse profile browse

map

? .85 .45? ? .35? .42 ?? ? .05M

embe

rs

Skills0.5 1

0.7 00 0.6

0.1 0

0.2 0.3 0.50.5 0.7 0.2

Mem

bers Skills

Each row is a representation of a member in latent space

Each column represents a skill in

latent space

Matrix Factorization

Estimating Skill Reputation

13

Endorse profile

browsemap

? .85 .45? ? .35? .42 ?

.02 ? ?Mem

bers

Skills0.5 1

0.7 00 0.6

0.1 0

0.2 0.3 0.50.5 0.7 0.2

Mem

bers Skills

.6 .85 .45.14 .21 .35.3 .42 .12

.02 .03 .05

Mem

bers

SkillsFill in unknown cells in

the original matrix

Agenda

Mission & Product

Expertise Search

Candidate Discovery via Similar Profiles Modeling

Search By Example

Similar People How you rank for profile views

People You May Hire Lead Recommendations

Similar Profiles Recommender

Title : Software Engineer, Research Engineer, Research Assistant

Specialty : Machine Learning, Data Analysis, Hadoop, Networks

Company: Cisco, Linkedin, Penn State

Summary: Software Engineer, Research Engineer, Machine Learning, Data Analysis, Networks, Research Assistant

How to model a profile with career trajectory?

- Summary: ML, Hadoop- Company: Linkedin- Title: Software Engineer- Duration: (2011.5-2013.3)

- Summary: Data Analysis- Company: Cisco- Title: Research Engineer- Duration: (2010.7-2011.4)

- Summary: Networks- Company: Penn State- Title: Research Assistant- Duration: (2006.9-2010.6)

Keywords Profile Model Sequence Profile Model

How to match two profiles ?

Title : Software Engineer, RA

Specialty: ML, Networks…

Company: Cisco, Linkedin, Penn StateSummary: Software Engineer ML, Networks, …

Title : Software Engineer, Ph.D.

Specialty: ML, DM, Mobile…

Company: Yahoo, Linkedin, Intel, DartmouthSummary: Software Engineer ML, DM, Mobile …

- Summary: ML, Hadoop- Company: Linkedin- Title: Software Engineer- Duration: (2011.5-2013.3)

- Summary: Data Analysis- Company: Cisco- Title: Research Engineer- Duration: (2010.7-2011.4)

- Summary: ML- Company: Linkedin- Title: Software Engineer- Duration: (2012.7-2013.3)- Summary: Mobile- Company: Intel- Title: Software Engineer- Duration: (2010.5-2012.3)

- Summary: Hadoop, DM- Company: Yahoo- Title: Research Scientist - Duration: (2008.8-2010.4)

Similar Profiles

Similar Career Paths

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Set of Positions

- Summary: ML, Hadoop- Company: Linkedin- Title: Software Engineer- Duration: (2011.5-2013.3)

- Summary: Data Analysis- Company: Cisco- Title: Research Engineer- Duration: (2010.7-2011.4)

- Summary: Networks- Company: Penn State- Title: Research Assistant- Duration: (2006.9-2010.6)

- Summary: ML- Company: Linkedin- Title: Software Engineer- Duration: (2012.7-2013.3)- Summary: Mobile

- Company: Intel- Title: Software Engineer- Duration: (2010.5-2012.3)- Summary: Hadoop, DM

- Company: Yahoo- Title: Research Scientist - Duration: (2008.8-2010.4)

• Profile 2

- Summary: Sensor- Company: Dartmouth- Title: Ph.D. Student- Duration: (2002.9-2008.7)

• Profile 1

Sequence of Positions• Profile 1

- Summary: ML, Hadoop- Company: Linkedin- Title: Software Engineer- Duration: (2011.5-2013.3)

- Summary: Data Analysis- Company: Cisco- Title: Research Engineer- Duration: (2010.7-2011.4)

- Summary: Networks- Company: Penn State- Title: Research Assistant- Duration: (2006.9-2010.6)

• Profile 2- Summary: ML- Company: Linkedin- Title: Software Engineer- Duration: (2012.7-2013.3)

- Summary: Mobile- Company: Intel- Title: Software Engineer- Duration: (2010.5-2012.3)

- Summary: Hadoop, DM- Company: Yahoo- Title: Research Scientist - Duration: (2008.8-2010.4)

- Summary: Sensor- Company: Dartmouth- Title: Ph.D. Student- Duration: (2002.9-2008.7)

Agenda

Mission & Product

Expertise Search

Candidate Discovery via Similar Profiles Modeling

Search By Example

Search by Ideal Candidate

Challenges in Search by Example

1. Given a set of users determine

a. Most relevant skillsb. Most relevant titles + seniorityc. Related Companies

2. Maintaining high level of personalization for the user

Architecture : Search by Ideal Candidate

The first part in the function (f1) estimates how a result r is relevant to query q and searcher s, as in the standard personalized search. The second part (f2) aims to guarantee a direct similarity between a result and input ideal candidates (IC).

λ is a parameter controlling decay rate

Some of the contributing features

1. Skills reputation2. Career Similarity3. Related Companies

References

1. Search by Ideal Candidates: Next Generation of Talent Search at LinkedIn, Ha-Thuc, Viet; Xu, Ye; Pradeep Kanduri, Satya; Wu, Xianren; Dialani, Vijay; Yan, Yan; Gupta, Abhishek; Sinha, Shakti, WWW 2015

2. Personalized expertise search at linkedin, V. Ha-Thuc, G. Venkataraman, M. Rodriguez, S. Sinha, S. Sundaram, and L. Guo, In Proceedings of the 4th IEEE International Conference Big Data (BigData), IEEE, 2015.

3. Modeling Professional Similarity by mining Professional Career Trajectories, Y. Xu; Z. Li; A. Gupta; A. Bugdayci; A. Bhasin, In Proceedings of the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, IEEE, 2014.

Thanks to my collaborators:

4. Viet Ha-Thuc

5. Ye Xu

6. Satya Kanduri

7. Vijay Dialani

8. Yan Yan

9. Abhishek Gupta

10. Shakti Sinha