0
Power in Unity:
Forming Teams in Large-Scale
Community Systems
Aris AnagnostopoulosS
, Luca BecchettiS
,
Carlos CastilloY
...
2 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi
Outline
• Motivation
• Problem definition
• Algo...
Wikipedia Category: Heist films
1960 2001
4 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi
Do you have ...?
• ... too many papers/proposals...
5 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi
Motivation
• Staff of people with different skil...
6 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi
Criteria
• Fitness
– e.g. if fitness is success ...
7 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi
Framework
Jobs/Tasks k
People n
Skills m
Teams k...
8 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi
Properties
• Pareto-dominant profiles
• Non decr...
9 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi
Properties (cont.)
• Non decreasing performance
...
10 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi
Team profiles
• Maximum skill
• Additive skills...
11 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi
Score functions
• Fraction of skills possessed
...
12 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi
Balanced task covering
• Cover all the tasks
• ...
13 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi
Balanced task covering - Online
• Evaluate by c...
14 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi
Weighted set cover
• Weight each set by
– Compe...
Experiments
16 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi
Datasets
Mapping of data to problem instances
S...
17 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi
Results (center)
18 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi
Results (center)
19 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi
Results (most loaded users)
20 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi
Results (most loaded users)
21 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi
Related works
• Lots of works on matching and
s...
22 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi
Future works
• Building, retrieving and ranking...
Buddy Venturanza @ Flickr (Creative Commons)
Thank you!
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Power in Unity: Forming Teams in Large-Scale Community Systems

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Aris Anagnostopoulos, Carlos Castillo, Aristides Gionis, Luca Becchetti, Stefano Leonardi: "Power in Unity: Forming Teams in Large-Scale Community Systems". Proc. of CIKM 2010, pp. 599-608.Toronto, Canada. ACM Press.

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Transcript of "Power in Unity: Forming Teams in Large-Scale Community Systems"

  1. 1. Power in Unity: Forming Teams in Large-Scale Community Systems Aris AnagnostopoulosS , Luca BecchettiS , Carlos CastilloY , Aris GionisY , Stefano LeonardiS Y Yahoo! Research – S Sapienza University of Rome
  2. 2. 2 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi Outline • Motivation • Problem definition • Algorithms • Experiments
  3. 3. Wikipedia Category: Heist films 1960 2001
  4. 4. 4 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi Do you have ...? • ... too many papers/proposals to review? • ... too many interviews to do? Review workload for last year: ~60 papers
  5. 5. 5 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi Motivation • Staff of people with different skills • Stream of tasks arriving online • Create teams on-the-fly for each task – Teams should be fit for the tasks – Allocation should be fair to people
  6. 6. 6 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi Criteria • Fitness – e.g. if fitness is success rate, maximize expected number of successful tasks • Fairness – everybody should be involved in roughly the same number of tasks Trade-offs may appear: do you see how?
  7. 7. 7 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi Framework Jobs/Tasks k People n Skills m Teams k Score/fitness Load
  8. 8. 8 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi Properties • Pareto-dominant profiles • Non decreasing performance • Job monotonicity • Non-increasing marginal utility
  9. 9. 9 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi Properties (cont.) • Non decreasing performance – c.f. Brooks' Law: “adding manpower to a late software project makes it later” • Job monotonicity – May not hold e.g. start from unfeasible task • Non-increasing marginal utility – May not hold e.g. if all skills are required
  10. 10. 10 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi Team profiles • Maximum skill • Additive skills • Multiplicative skills • Binary profiles – All of the above are equivalent
  11. 11. 11 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi Score functions • Fraction of skills possessed • is sub-modular: greedy method provides an approximation within a constant factor • In other applications e.g. Ocean's 11, all skills are required: covering problem
  12. 12. 12 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi Balanced task covering • Cover all the tasks • Objective • NP-hard problem even with k = 2 – Reduction from MSAT • People = variables; Skills = clauses; • People in team 1: TRUE, People in team 2: FALSE • Maximum load of 1 is achieved if clauses satisfied • Offline setting has a randomized approx. algo. that succeeds with prob 1-± with ratio
  13. 13. 13 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi Balanced task covering - Online • Evaluate by competitive ratio – Compare with optimal offline assignment • Basic algorithms – Assemble the team of minimum size – Assemble the team that keeps the maximum load of a person low • Competitive ratios are bad:
  14. 14. 14 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi Weighted set cover • Weight each set by – Competitive ratio • Weight each set by – Competitive ratio 
  15. 15. Experiments
  16. 16. 16 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi Datasets Mapping of data to problem instances Summary statistics
  17. 17. 17 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi Results (center)
  18. 18. 18 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi Results (center)
  19. 19. 19 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi Results (most loaded users)
  20. 20. 20 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi Results (most loaded users)
  21. 21. 21 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi Related works • Lots of works on matching and scheduling problems • Lots of works on finding one expert – IR-style and SN-style • T. Lappas, K. Liu, E. Terzi. Finding a team of experts in social networks, KDD'09. – Focuses on communication costs
  22. 22. 22 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi Future works • Building, retrieving and ranking complex information elements – document/answer sets, photo sets, geo points, RDF sub-graphs, etc. • Algorithms to support massive collaboration – decisions, coordination, awareness, etc.
  23. 23. Buddy Venturanza @ Flickr (Creative Commons) Thank you!
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