Personalized Task Recommendation In Crowdsourcing Systems (progress In Is)
by David Geiger /
2015 / English / PDF
2.5 MB Download
This book examines the principles of and advances in personalized
task recommendation in crowdsourcing systems, with the aim of
improving their overall efficiency. It discusses the challenges
faced by personalized task recommendation when crowdsourcing
systems channel human workforces, knowledge, skills and
perspectives beyond traditional organizational boundaries. The
solutions presented help interested individuals find tasks that
closely match their personal interests and capabilities in a
context of ever-increasing opportunities of participating in
crowdsourcing activities.
This book examines the principles of and advances in personalized
task recommendation in crowdsourcing systems, with the aim of
improving their overall efficiency. It discusses the challenges
faced by personalized task recommendation when crowdsourcing
systems channel human workforces, knowledge, skills and
perspectives beyond traditional organizational boundaries. The
solutions presented help interested individuals find tasks that
closely match their personal interests and capabilities in a
context of ever-increasing opportunities of participating in
crowdsourcing activities.In order to explore the design of mechanisms that generate task
recommendations based on individual preferences, the book first
lays out a conceptual framework that guides the analysis and design
of crowdsourcing systems. Based on a comprehensive review of
existing research, it then develops and evaluates a new kind of
task recommendation service that integrates with existing systems.
The resulting prototype provides a platform for both the field
study and the practical implementation of task recommendation in
productive environments.
In order to explore the design of mechanisms that generate task
recommendations based on individual preferences, the book first
lays out a conceptual framework that guides the analysis and design
of crowdsourcing systems. Based on a comprehensive review of
existing research, it then develops and evaluates a new kind of
task recommendation service that integrates with existing systems.
The resulting prototype provides a platform for both the field
study and the practical implementation of task recommendation in
productive environments.
