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

研究成果: 書貢獻/報告類型會議貢獻

摘要

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.

原文英語
主出版物標題Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007
頁面2339-2344
頁數6
DOIs
出版狀態已發佈 - 2007 十二月 1
事件6th International Conference on Machine Learning and Cybernetics, ICMLC 2007 - Hong Kong, 中国
持續時間: 2007 八月 192007 八月 22

出版系列

名字Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007
4

會議

會議6th International Conference on Machine Learning and Cybernetics, ICMLC 2007
國家中国
城市Hong Kong
期間07/8/1907/8/22

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Software
  • Theoretical Computer Science

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  • 引用此

    Liu, D. R., Wu, I. C., & Chang, P. C. (2007). Measuring the variation in task-needs for knowledge delivery: A profiling via collaboration technique. 於 Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007 (頁 2339-2344). [4370536] (Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007; 卷 4). https://doi.org/10.1109/ICMLC.2007.4370536