Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
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Daniel Tunkelang discusses three key areas - relevance, recommendations, and reputation - that LinkedIn is focusing on to improve the user experience. Relevance refers to improving search and discovery of profiles by combining different types of signals. Recommendations match members to jobs, groups, and other connections. Reputation involves identifying member expertise in skills and who the experts are in particular skills. Open problems remain around exploratory search, balancing exploration and exploitation of the data, and incentivizing the development of online reputation.
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Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
- 1. Daniel Keeping It Professional:Relevance, Recommendations, and Reputation. Daniel Tunkelang Principal Data Scientist at LinkedIn 1
- 3. Hard problems we’re tackling in:
- 4. Relevance
- 6. Reputation
- 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
- 14. Insights: Data Stories 9
- 15. Everywhere 10
- 17. People Search
- 18. Recommendations
- 19. Job Matching
- 20. Reputation
- 21. Skills11
- 22. People Search 12
- 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
- 30. Network Rank, Profile Quality
- 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.segsb.segs U {s} a.probb.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
- 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
- 49. Job Description, Location, Similar Jobs, …
- 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
- 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
- 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
- 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
Notes de l'éditeur
- 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.