Personalized Task Recommendation in Crowdsourcing Systems

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.

Introduces a conceptual framework for crowdsourcing systems and highlights their organizational functions Presents the current state and future research agenda of personalized task recommendation Describes the design of a modular recommendation system in a productive environment Provides new insights into the potential of personalized task recommendation Includes supplementary material: sn.pub/extras

Autorentext
David Geiger is a researcher in the field of information systems with a particular interest in crowdsourcing approaches and innovative software solutions. He holds a PhD and a master's degree from the Business School of the University of Mannheim, Germany. During his time as a research associate and lecturer, he has been a visiting scholar at the Queensland University of Technology in Brisbane and the Victoria University in Melbourne. His research has been supported by the German Research Foundation (DFG), the German Federal Ministry of Education and Research (BMBF), and the German Academic Exchange Service (DAAD).

Inhalt
Introduction.- Crowdsourcing Systems.- Current state of Personalized Task Recommendation.- Design of a Third-Party Task Recommendation Service .- Personalized Task Recommendation in the Field.- Conclusion.