Power in Unity: Forming Teams in Large-Scale Community Systems

Power in Unity: Forming Teams in Large-Scale Community Systems Aris AnagnostopoulosS, Luca BecchettiS, Carlos CastilloY, Aris GionisY, Stefano LeonardiS Y Yahoo! Research – SSapienza University of Rome

Outline • Motivation • Problem definition • Algorithms • Experiments 2 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi

1960 2001 Wikipedia Category: Heist films

Do you have ...? • ... too many papers/proposals to review? • ... too many interviews to do? Review workload for last year: ~60 papers 4 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 5 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? 6 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi

Framework Jobs/Tasks k People n Skills m Teams k Score/fitness Load 7 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi

Properties • Pareto-dominant profiles • Non decreasing performance • Job monotonicity • Non-increasing marginal utility 8 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 9 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi

Team profiles • Maximum skill • Additive skills • Multiplicative skills • Binary profiles – All of the above are equivalent 10 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 11 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 12 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: 13 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi

Weighted set cover • Weight each set by – Competitive ratio • Weight each set by – Competitive ratio  14 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi

Experiments

Datasets Mapping of data to problem instances Summary statistics 16 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi

Results (center) 17 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi

Results (center) 18 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi

Results (most loaded users) 19 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi

Results (most loaded users) 20 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 21 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. 22 Power in Unity – Anagnostopoulos, Becchetti, Castillo, Gionis, Leonardi

Buddy Venturanza @ Flickr (Creative Commons) Thank you!

http://chato.cl	66
http://www.chato.cl	12
http://www.slideshare.net	1

Power in Unity: Forming Teams in Large-Scale Community Systems

by Carlos Castillo on Nov 02, 2010

Accessibility

Categories

Tags

Upload Details

Usage Rights

3 Embeds 79

Statistics

Power in Unity: Forming Teams in Large-Scale Community Systems — Presentation Transcript