標題: 透過分析資訊需求變動修正工作特徵檔以提供工作相關資訊
Profile Adaptation for Providing Task-relevant Information by Variation of Task Needs
作者: 張北晨
Pei-Cheng Chang
劉敦仁
Duen-Ren Liu
資訊管理研究所
關鍵字: 資訊過濾;工作需求;適性化技術;工作特徵檔;工作主題變動;合作式工作特徵檔;information filtering;task needs;adaptive profiling technique;self task profile;variation of task-needs on topics;collaborative profile
公開日期: 2005
摘要: 在以工作為基礎的企業環境中,如何有效提供工作相關資訊以滿足知識工作者的資訊需求為部署知識管理系統之重要課題。此外,知識密集工作環境中的工作通常具有需要參閱大量文件資料的特性;因此,透過資訊過濾相關技術分析並建構工作者的工作相關特徵檔,為有效提供工作相關知識之重要技術。本研究提出需求特徵化修正方法來分析工作者的動態資訊需求,該方法主要是分析工作者存取的文件及工作主題相關性,並考慮時間因素建構工作特徵檔。此外本研究分析工作者在工作主題之需求變化以找出具相似主題需求變化的相似工作者,並進一步依據相似工作者的主題需求變化進行合作式調整工作特徵檔並推論潛在資訊需求。最後,本研究進行實驗評估,以比較所提方法在提供工作相關資訊之成效。
In task-based business environments, an important issue of deploying Knowledge Management System (KMS) is providing task-relevant information (codified knowledge) to fulfill the information needs of knowledge workers. In addition, workers need to access lots of textual documents in conducting knowledge-intensive tasks. Accordingly, effective knowledge management relies on using information filtering (IF) techniques to model worker's information needs as profiles and provide relevant information based on the modeled profiles. This research proposes a novel adaptive task-profiling technique to model worker's information needs on tasks, i.e., task-needs. The proposed technique adjusts task profiles to model worker's dynamic task-needs based on the documents accessed by workers and the relevance on the task-based topic taxonomy. Generally, the more recent the document accessed the more important it is to reflect a work's current task needs. Thus, the effect of time factor is considered in profile adaptation. In addition, the proposed profiling technique adopts a novel collaborative profile adaptation approach to adjust task profiles. We analyze the variations of workers' task needs on the topic taxonomy to identify workers with similar variations of task needs on topics (i.e., topic needs) over time. Similar workers' variations of topic needs are used to predict the target worker's future variations of topic needs, and are used to adjust the target worker's task profile. The codified knowledge that is relevant to the current task can be retrieved based on the adjusted task profile to fit the worker's dynamic task needs. Empirical experiments demonstrate that the proposed approach models workers' task-needs effectively and helps provide task-relevant knowledge.
URI: http://140.113.39.130/cdrfb3/record/nctu/#GT009334519
http://hdl.handle.net/11536/79541
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