臺灣博碩士論文加值系統

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.

1. Introduction 1
2. Literature review 4
2.1. Task-based knowledge management and retrieval 4
2.2. Information retrieval in a vector space model 5
2.3. User modeling by information filtering technique 6
2.4. Relevance feedback techniques 7
3. Overview of methodology 9
4. Modeling task needs 12
4.1. Capturing users' access behavior 12
4.2. Self profile adaptation 12
5. Measuring variations of user topic needs over Time 16
5.1. Variation measurement for a specific topic 16
5.2. Representation model: Topic-needs variation matrix 17
6. Collaborative profile adaptation 19
6.1. Identifying similar workers 19
6.2. Prediction of task needs 22
6.3. Result demonstrations 24
7. Experiments 28
7.1. Experimental setup 28
7.1.1. Experimental objective and design 28
7.1.2. Data and participants 29
7.1.3. Evaluation metrics 29
7.2. Experimental result and observations 30
7.2.1. Experiment 1-1: parameter selection for TP-V method 30
7.2.2. Experiment 1-2: parameter selection for TP-D method 31
7.2.3. Experiment 2: comparisons of self profile adaptation methods 32
7.2.4. Experiment 3: comparisons of various methods 34
7.2.5. Case inspections 35
7.2.6. Discussions 36
8. Conclusion and future research issues 38
Reference 39

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