Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Wu, I-Chin | en_US |
dc.contributor.author | Liu, Duen-Ren | en_US |
dc.contributor.author | Chang, Pei-Cheng | en_US |
dc.date.accessioned | 2014-12-08T15:08:11Z | - |
dc.date.available | 2014-12-08T15:08:11Z | - |
dc.date.issued | 2009-12-01 | en_US |
dc.identifier.issn | 1532-2882 | en_US |
dc.identifier.uri | http://dx.doi.org/10.1002/asi.21201 | en_US |
dc.identifier.uri | http://hdl.handle.net/11536/6381 | - |
dc.description.abstract | 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. | en_US |
dc.language.iso | en_US | en_US |
dc.title | Learning Dynamic Information Needs: A Collaborative Topic Variation Inspection Approach | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1002/asi.21201 | en_US |
dc.identifier.journal | JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY | en_US |
dc.citation.volume | 60 | en_US |
dc.citation.issue | 12 | en_US |
dc.citation.spage | 2430 | en_US |
dc.citation.epage | 2451 | en_US |
dc.contributor.department | 資訊管理與財務金融系 註:原資管所+財金所 | zh_TW |
dc.contributor.department | Department of Information Management and Finance | en_US |
dc.identifier.wosnumber | WOS:000272257800005 | - |
dc.citation.woscount | 2 | - |
Appears in Collections: | Articles |
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