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Daniel,[object Object],Keeping It Professional:Relevance, Recommendations, and Reputation.,[object Object],Daniel Tunkelang,[object Object],Principal Data Scientist at LinkedIn ,[object Object],1,[object Object]
Overview,[object Object],[object Object]
Hard problems we’re tackling in:
Relevance
Recommendations
Reputation
Open problems2,[object Object]
Identity,[object Object],Connect, find and be found,[object Object],Rolodex, Resume, Business Card,[object Object],LinkedIn Profile, Address Book, Search,[object Object],Insights,[object Object],Be great at what you do,[object Object],Newspapers, ,[object Object],Trade Magazines, Events,[object Object],Homepage, LinkedIn Today, Groups,[object Object],Everywhere,[object Object],Desktop,[object Object],Work wherever our members work,[object Object],Mobile, APIs, Plug-Ins,[object Object],What is LinkedIn?,[object Object],3,[object Object]
Identity: Profile of Record,[object Object],4,[object Object]
Identity: Connect with Others,[object Object],5,[object Object]
Identity: Join the Conversation,[object Object],6,[object Object]
Insights: Power of Aggregation,[object Object],Before,[object Object],employees worked at,[object Object],Yahoo! (169),[object Object],	Google (96),[object Object],	Oracle (78),[object Object],	Microsoft (72),[object Object],	IBM (43),[object Object],Before,[object Object],employees worked at,[object Object],Google(475),[object Object],	Microsoft (448),[object Object],	LinkedIn (169),[object Object],	Apple, Inc. (154),[object Object],ebay(133),[object Object],7,[object Object]
Insights: Market Research,[object Object],8,[object Object]
Insights: Data Stories,[object Object],9,[object Object]
Everywhere,[object Object],10,[object Object]
Hard Problems: Examples,[object Object],[object Object]
People Search
Recommendations
Job Matching
Reputation
Skills11,[object Object]
People Search,[object Object],12,[object Object]
People Search: Scale,[object Object],[object Object]
2B searches in 2010
Based on                      (cf. http://sna-projects.com/)13,[object Object]
People Search: Faceted Search,[object Object],14,[object Object]
People Search: Network Facet,[object Object],15,[object Object]
People Search: Type-Ahead,[object Object],16,[object Object]
People Search: Relevance,[object Object],[object Object]
Network Rank, Profile Quality
Query-Dependent Signals
Field-Based Relevance
Personalized Signals
Network Distance17,[object Object]
People Search: Query-Independent Signals,[object Object],18,[object Object]
People Search: Network Rank,[object Object],19,[object Object]
People Search: Profile Quality,[object Object],20,[object Object]
People Search: SEO,[object Object],21,[object Object]
People Search: Query-Dependent Signals,[object Object],22,[object Object]
People Search: Inferring Structure,[object Object],23,[object Object]
People Search: Ambiguity,[object Object],vs.,[object Object],vs.,[object Object],24,[object Object]
People Search: HMM + Segmentation,[object Object],for i in [1..n],[object Object],  s  w1 w2 … wi,[object Object],  if Pc(s) > 0,[object Object],    a  new Segment(),[object Object],a.segs {s},[object Object],a.prob Pc(s),[object Object],    B[i]  {a},[object Object],  for j in [1..i-1],[object Object],     for b in B[j],[object Object],       s wjwj+1 … wi,[object Object],       if Pc(s) > 0,[object Object],         a  new Segment(),[object Object],a.segsb.segs U {s},[object Object],a.probb.prob * Pc(s),[object Object],         B[i]  B[i] U {a},[object Object],   sort B[i] by prob,[object Object],   truncate B[i] to size k,[object Object],25,[object Object]
People Search: Personalized Signals,[object Object],26,[object Object]
People Search: Further Reading,[object Object],[object Object]

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

Notas del editor

  1. 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.