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**Harvard**

Bellevik, S. och Ekman, P. (2017) *Recommendation system for workers and tasks Recommending the optimal assignment of workers to tasks*. Göteborg : Chalmers University of Technology

** BibTeX **

@mastersthesis{

Bellevik2017,

author={Bellevik, Sebastian and Ekman, Philip},

title={Recommendation system for workers and tasks Recommending the optimal assignment of workers to tasks},

abstract={This thesis tries to solve the problem of matching workers with tasks when unknown
parameters are involved. Looking at the trend where outsourcing tasks to previously
unknown parties is becoming more common, a need is definitely there to solve this
problem in an efficient way. The problem can be described as a list of workers, each
with an unknown list of skills, and a list of tasks, each with a known list of requirements.
Any method assigning all tasks to workers, while maximizing the reward
given for doing so, must be able to accurately estimate the skills of every worker to
provide good results.
To solve this problem when each worker only has a single skill has been shown to
be possible with an algorithm called Bounded Epsilon First. This algorithm is used
as a starting point for testing data with single-skill workers and single-requirement
tasks, before moving on to multi-skill workers and multi-requirement tasks. No real
world data was available for multi-skill matching, which is why all experimentation
is done on synthetic data, generated uniformly at random. After the first phase,
different matching algorithms and methods of rating worker performance were implemented
and tested, producing varying results.
Testing all implemented methods on real world data would surely produce interesting
results, but overall, the results presented in this thesis show good promise. Our best
solution, given time to estimate each worker’s skills, give results approaching 85%
of the result produces by matching with all parameters known.},

publisher={Institutionen för data- och informationsteknik (Chalmers), Chalmers tekniska högskola},

place={Göteborg},

year={2017},

keywords={recommendation system, outsourcing, crowdsourcing, estimating unknown properties, maximizing reward, exploration, exploitation},

note={58},

}

** RefWorks **

RT Generic

SR Electronic

ID 251316

A1 Bellevik, Sebastian

A1 Ekman, Philip

T1 Recommendation system for workers and tasks Recommending the optimal assignment of workers to tasks

YR 2017

AB This thesis tries to solve the problem of matching workers with tasks when unknown
parameters are involved. Looking at the trend where outsourcing tasks to previously
unknown parties is becoming more common, a need is definitely there to solve this
problem in an efficient way. The problem can be described as a list of workers, each
with an unknown list of skills, and a list of tasks, each with a known list of requirements.
Any method assigning all tasks to workers, while maximizing the reward
given for doing so, must be able to accurately estimate the skills of every worker to
provide good results.
To solve this problem when each worker only has a single skill has been shown to
be possible with an algorithm called Bounded Epsilon First. This algorithm is used
as a starting point for testing data with single-skill workers and single-requirement
tasks, before moving on to multi-skill workers and multi-requirement tasks. No real
world data was available for multi-skill matching, which is why all experimentation
is done on synthetic data, generated uniformly at random. After the first phase,
different matching algorithms and methods of rating worker performance were implemented
and tested, producing varying results.
Testing all implemented methods on real world data would surely produce interesting
results, but overall, the results presented in this thesis show good promise. Our best
solution, given time to estimate each worker’s skills, give results approaching 85%
of the result produces by matching with all parameters known.

PB Institutionen för data- och informationsteknik (Chalmers), Chalmers tekniska högskola,PB Institutionen för data- och informationsteknik (Chalmers), Chalmers tekniska högskola,

LA eng

LK http://publications.lib.chalmers.se/records/fulltext/251316/251316.pdf

OL 30