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Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
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Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn

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Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn …

Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
Daniel Tunkelang (LinkedIn)

LinkedIn operates the world's largest professional network on the Internet with more than 100 million members in over 200 countries. In order to connect its users to the people, opportunities, and content
that best advance their careers, LinkedIn has developed a variety of algorithms that surface relevant content, offer personalized recommendations, and establish topic-sensitive reputation -- all at a
massive scale. In this talk, I will discuss some of the most challenging technical problems we face at LinkedIn, and the approaches we are taking to address them.

Note: This talk was presented at the Carnegie Mellon University School of Computer Science Intelligence Seminar on September 20, 2011. As of May 2013, LinkedIn has over 225 million members.

Published in: Technology, Business
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Notes
  • Job matching: or any matching.

    Use LIVE Objects where the Job Descriptions and Resumes are live and intelligent objects that seek and negotiate with each other.

    See http://www.slideshare.net/putchavn/systematic-identification-of-design-classes-with-live-objects
       Reply 
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  • LinkedIn is all about connecting professionals with opportunity. We are taking the tools that made people successful in the 20th century and bringing them into the 21st century.
  • Transcript

    • 1. Daniel
      Keeping It Professional:Relevance, Recommendations, and Reputation.
      Daniel Tunkelang
      Principal Data Scientist at LinkedIn
      1
    • 2. Overview
      • What is LinkedIn?
      • 3. Hard problems we’re tackling in:
      • 4. Relevance
      • 5. Recommendations
      • 6. Reputation
      • 7. Open problems
      2
    • 8. Identity
      Connect, find and be found
      Rolodex, Resume, Business Card
      LinkedIn Profile, Address Book, Search
      Insights
      Be great at what you do
      Newspapers,
      Trade Magazines, Events
      Homepage, LinkedIn Today, Groups
      Everywhere
      Desktop
      Work wherever our members work
      Mobile, APIs, Plug-Ins
      What is LinkedIn?
      3
    • 9. Identity: Profile of Record
      4
    • 10. Identity: Connect with Others
      5
    • 11. Identity: Join the Conversation
      6
    • 12. Insights: Power of Aggregation
      Before
      employees worked at
      Yahoo! (169)
      Google (96)
      Oracle (78)
      Microsoft (72)
      IBM (43)
      Before
      employees worked at
      Google(475)
      Microsoft (448)
      LinkedIn (169)
      Apple, Inc. (154)
      ebay(133)
      7
    • 13. Insights: Market Research
      8
    • 14. Insights: Data Stories
      9
    • 15. Everywhere
      10
    • 16. Hard Problems: Examples
      11
    • 22. People Search
      12
    • 23. People Search: Scale
      • 120M+ members
      • 24. 2B searches in 2010
      • 25. Based on (cf. http://sna-projects.com/)
      13
    • 26. People Search: Faceted Search
      14
    • 27. People Search: Network Facet
      15
    • 28. People Search: Type-Ahead
      16
    • 29. People Search: Relevance
      • Query-Independent Signals
      • 30. Network Rank, Profile Quality
      • 31. Query-Dependent Signals
      • 32. Field-Based Relevance
      • 33. Personalized Signals
      • 34. Network Distance
      17
    • 35. People Search: Query-Independent Signals
      18
    • 36. People Search: Network Rank
      19
    • 37. People Search: Profile Quality
      20
    • 38. People Search: SEO
      21
    • 39. People Search: Query-Dependent Signals
      22
    • 40. People Search: Inferring Structure
      23
    • 41. People Search: Ambiguity
      vs.
      vs.
      24
    • 42. People Search: HMM + Segmentation
      for i in [1..n]
      s  w1 w2 … wi
      if Pc(s) > 0
      a  new Segment()
      a.segs {s}
      a.prob Pc(s)
      B[i]  {a}
      for j in [1..i-1]
      for b in B[j]
      s wjwj+1 … wi
      if Pc(s) > 0
      a  new Segment()
      a.segsb.segs U {s}
      a.probb.prob * Pc(s)
      B[i]  B[i] U {a}
      sort B[i] by prob
      truncate B[i] to size k
      25
    • 43. People Search: Personalized Signals
      26
    • 44. People Search: Further Reading
      • QCon 2010 presentation by John Wang on “LinkedIn Search: Searching the Social Graph in Real Time”http://www.infoq.com/presentations/LinkedIn-Search
      • 45. SIGIR 2011 Workshop on Entity-Oriented Searchhttp://research.microsoft.com/en-us/um/beijing/events/eos2011/
      • 46. HCIR 2011 paper by Jonathan Koren on “Faceted Search Query Log Analysis” (forthcoming)http://hcir.info/hcir-2011/
      27
    • 47. Job Matching
      28
    • 48. Job Matching: Overview
      • Job Features
      • 49. Job Description, Location, Similar Jobs, …
      • 50. Candidate Features
      • 51. Profile Data, Network, Activity, …
      • 52. Standardization
      • 53. Companies, Job Titles, Education, …
      29
    • 54.
      industry
      description
      functional area
      Current Position
      title
      summary
      tenure length
      industry
      functional area

      Job Matching: Algorithm
      Corpus Stats
      Job
      Matching
      Transition probabilities
      Connectivity
      yrs of experience to reach title
      education needed for this title

      Binary
      Exact matches:
      geo, industry,

      Soft
      transition
      probabilities,
      similarity,

      Text
      title
      geo
      company
      User Base
      Similarity
      (candidate expertise, job description)
      0.56
      Filtered
      Similarity
      (candidate specialties, job description)
      0.2
      Candidate
      Transition probability
      (candidate industry, job industry)
      0.43
      General
      expertise
      specialties
      education
      headline
      geo
      experience
      Title Similarity
      0.8
      Similarity (headline, title)
      0.7
      derived
      .
      .
      .
      30
    • 55. Job Matching: Challenges
      • Most people aren't looking for jobs.
      • 56. Complicates evaluation, training.
      • 57. Important not to offend users.
      • 58. e.g., by offering Peter Norvig a postdoc.
      • 59. You can’t always get what you want
      • 60. Every employer wants the hottest candidates.
      31
    • 61. Job Matching: Further Reading
      • KDD 2011 paper by Bekkerman & Gavish on “High-Precision Phrase-based Document Classification”http://www.stanford.edu/~gavish/documents/phrase_based.pdf
      • 62. SIGIR 2011 paper by Cetintas et al. on “Identifying Similar People in Professional Social Networks”http://dl.acm.org/citation.cfm?id=2010123
      • 63. Blog post on LinkedIn’s recommendation enginehttp://blog.linkedin.com/2011/03/02/linkedin-products-you-may-like/
      32
    • 64. Skills
      33
    • 65. Skills: What are Skills?
      34
    • 66. Skills: Identifying Skills
      35
    • 67. Skills: Cluster and Disambiguate
      angel
      36
    • 68. Skills: Assigning Skills to People
      37
    • 69. Skills: Who are the Experts?
      38
    • 70. Summary: The 3 Rs
      • Relevance
      • 71. Combine query-independent, query-dependent, and personalized features.
      • 72. Recommendations
      • 73. Match people to jobs, groups, news, …
      • 74. Reputation
      • 75. Expertise relative to professional skills.
      39
    • 76. ¿
      Open Problems
      ?
      40
    • 77. Exploratory Search
      Exploratory Search
      Investigate
      Lookup
      Learn
      Accretion
      Analysis
      Exclusion/Negation
      Synthesis
      Evaluation
      Discovery
      Planning/Forecasting
      Transformation
      Fact retrieval
      Known item search
      Navigation
      Transition
      Verification
      Question answering
      Knowledge acquisition
      Comprehension/Interpretation
      Comparison
      Aggregation/Integration
      Socialize
      41
    • 78. Explore / Exploit
      42
    • 79. Incentives for Online Reputation
      43
    • 80. Thank You!
      Questions?
      Contact:
      dtunkelang@linkedin.com
      We’re Hiring!
      http://engineering.linkedin.com/
      44

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