標題: Measuring the variation in task-needs for knowledge delivery: A profiling via collaboration technique
作者: Liu, Duen-Ren
Wu, I-Chin
Chang, Pei-Cheng
資訊管理與財務金融系 註:原資管所+財金所
Department of Information Management and Finance
關鍵字: adaptive task-profiling;similar workers;topic taxonomy;variation in task-needs
公開日期: 2007
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.
URI: http://hdl.handle.net/11536/9866
ISBN: 978-1-4244-0972-3
期刊: PROCEEDINGS OF 2007 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7
起始頁: 2339
結束頁: 2344
Appears in Collections:Conferences Paper