標題: Learning Dynamic Information Needs: A Collaborative Topic Variation Inspection Approach
作者: Wu, I-Chin
Liu, Duen-Ren
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
公開日期: 1-十二月-2009
摘要: For projects in knowledge-intensive domains, it is crucially important that knowledge management systems are able to track and infer workers' up-to-date information needs so that task-relevant information can be delivered in a timely manner. To put a worker's dynamic information needs into perspective, we propose a topic variation inspection model to facilitate the application of an implicit relevance feedback (IRF) algorithm and collaborative filtering in user modeling. The model analyzes variations in a worker's task-needs for a topic (i.e., personal topic needs) over time, monitors changes in the topics of collaborative actors, and then adjusts the worker's profile accordingly. We conducted a number of experiments to evaluate the efficacy of the model in terms of precision, recall, and F-measure. The results suggest that the proposed collaborative topic variation inspection approach can substantially improve the performance of a basic profiling method adapted from the classical RF algorithm. It can also improve the accuracy of other methods when a worker's information needs are vague or evolving, i.e., when there is a high degree of variation in the worker's topic-needs. Our findings have implications for the design of an effective collaborative information filtering and retrieval model, which is crucial for reusing an organization's knowledge assets effectively.
URI: http://dx.doi.org/10.1002/asi.21201
http://hdl.handle.net/11536/6381
ISSN: 1532-2882
DOI: 10.1002/asi.21201
期刊: JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY
Volume: 60
Issue: 12
起始頁: 2430
結束頁: 2451
顯示於類別:期刊論文


文件中的檔案:

  1. 000272257800005.pdf