Next generation linked in talent search
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Transcript of Next generation linked in talent search
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
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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
?
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