Recommender systems for human resources task assignment

Isaias, Pedro, Casaca, Cristiane and Pifano, Sara (2010). Recommender systems for human resources task assignment. In: 24th IEEE International Conference on Advanced Information Networking and Applications, AINA 2010. 24th IEEE International Conference on Advanced Information Networking and Applications, AINA2010, Perth, WA, (214-221). April 20, 2010-April 23, 2010. doi:10.1109/AINA.2010.168


Author Isaias, Pedro
Casaca, Cristiane
Pifano, Sara
Title of paper Recommender systems for human resources task assignment
Conference name 24th IEEE International Conference on Advanced Information Networking and Applications, AINA2010
Conference location Perth, WA
Conference dates April 20, 2010-April 23, 2010
Proceedings title 24th IEEE International Conference on Advanced Information Networking and Applications, AINA 2010
Journal name 2010 24Th Ieee International Conference On Advanced Information Networking and Applications (Aina)
Series Proceedings - International Conference on Advanced Information Networking and Applications, AINA
Publication Year 2010
Sub-type Fully published paper
DOI 10.1109/AINA.2010.168
ISBN 9780769540184
ISSN 1550-445X
Start page 214
End page 221
Total pages 8
Language eng
Abstract/Summary In Portugal, the organisations responsible for the internal control of the State's financial administration need to progressively optimise their human resources in order to maximise their effectiveness. Part of this important responsibility relates to competence management and the assignment of their most suitable human resources to the tasks that insure their mission accomplishment. Such endeavour can benefit from a central concept of the Computer Supported Collaborative Work (CSCW) field: the application of computer technology to support group work. This paper outlines a recommender system, the 2HRT that aims to facilitate a more proficient human resources' task assignment, helping the human resources department to respond more efficiently to the demands for personnel of other departments. This research uses a Delphi study, with semi-structured interviews to collect the views of inspection agents in Portugal. The proposed recommender system incorporates the collaborative filtering and content-based recommendation techniques and the case-based reasoning approach.
Subjects 2200 Engineering
Keyword Case-based reasoning
Collaborative filtering
Competences management
Content-based filtering
Portuguese public inspection organisations
Recommender systems
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status Unknown

 
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