Measuring the variation in task-needs for knowledge delivery: A profiling via collaboration technique

Duen-Ren Liu*, I. Chin Wu, Pei Cheng Chang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Effective knowledge management (KM) in a knowledge-intensive working environment requires an understanding of workers' information needs for tasks, (task-needs), so that they can be provided with appropriate codified knowledge (textual documents) when performing long-term tasks. This work proposes a novel profiling technique based on implicit relevance feedback and collaborative filtering techniques that model workers' task-needs. The proposed profiling method analyses variations in workers' task-needs for topics (i.e., topic needs) in a topic taxonomy over time. Variations in the topic needs of similar workers' are used to predict variations in a target worker's topic needs and adjust his/her task profile accordingly. Experiment results suggest that considering variations in the topic needs of similar workers' during the profile adaptation process is effective in improving the precision of document retrieval.

Original languageEnglish
Title of host publicationProceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007
Pages2339-2344
Number of pages6
DOIs
StatePublished - 1 Dec 2007
Event6th International Conference on Machine Learning and Cybernetics, ICMLC 2007 - Hong Kong, China
Duration: 19 Aug 200722 Aug 2007

Publication series

NameProceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007
Volume4

Conference

Conference6th International Conference on Machine Learning and Cybernetics, ICMLC 2007
CountryChina
CityHong Kong
Period19/08/0722/08/07

Keywords

  • Adaptive task-profiling
  • Similar workers
  • Topic taxonomy
  • Variation in task-needs

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