1.

Record Nr.

UNINA9910254932203321

Autore

Geiger David

Titolo

Personalized Task Recommendation in Crowdsourcing Systems / / by David Geiger

Pubbl/distr/stampa

Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016

ISBN

3-319-22291-0

Edizione

[1st ed. 2016.]

Descrizione fisica

1 online resource (116 p.)

Collana

Progress in IS, , 2196-8705

Disciplina

004.019

Soggetti

Information technology

Business—Data processing

User interfaces (Computer systems)

Application software

Data mining

Artificial intelligence

IT in Business

User Interfaces and Human Computer Interaction

Information Systems Applications (incl. Internet)

Data Mining and Knowledge Discovery

Artificial Intelligence

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Description based upon print version of record.

Nota di bibliografia

Includes bibliographical references.

Nota di contenuto

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

Sommario/riassunto

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.