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dc.contributor.authorLai, Chin-Huien_US
dc.contributor.authorLiu, Duen-Renen_US
dc.date.accessioned2014-12-08T15:08:05Z-
dc.date.available2014-12-08T15:08:05Z-
dc.date.issued2009-12-01en_US
dc.identifier.issn0164-1212en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.jss.2009.06.044en_US
dc.identifier.urihttp://hdl.handle.net/11536/6328-
dc.description.abstractKnowledge is a critical resource that organizations use to gain and maintain competitive advantages. In the constantly changing business environment, organizations Must exploit effective and efficient methods of preserving, sharing and reusing knowledge in order to help knowledge workers find task-relevant information. Hence, an important issue is how to discover and model the knowledge flow (KF) of workers from their historical work records. The objectives of a knowledge flow model are to understand knowledge workers' task-needs and the ways they reference documents, and then provide adaptive knowledge support. This work proposes hybrid recommendation methods based on the knowledge flow model, which integrates KF mining, sequential rule mining and collaborative filtering techniques to recommend codified knowledge. These KF-based recommendation methods involve two phases: a KF mining phase and a KF-based recommendation phase. The KF mining phase identifies each worker's knowledge flow by analyzing his/her knowledge referencing behavior (information needs), while the KF-based recommendation phase utilizes the proposed hybrid methods to proactively provide relevant codified knowledge for the worker. Therefore, the proposed methods use workers' preferences for codified knowledge as well as their knowledge referencing behavior to predict their topics of interest and recommend task-related knowledge. Using data collected from a research institute laboratory, experiments are conducted to evaluate the performance of the proposed hybrid methods and compare them with the traditional CF method. The results of experiments demonstrate that utilizing the document preferences and knowledge referencing behavior of workers can effectively improve the quality of recommendations and facilitate efficient knowledge sharing. (C) 2009 Elsevier Inc. All rights reserved.en_US
dc.language.isoen_USen_US
dc.subjectKnowledge flowen_US
dc.subjectKnowledge flow miningen_US
dc.subjectKnowledge sharingen_US
dc.subjectDocument recommendationen_US
dc.subjectCollaborative filteringen_US
dc.subjectSequential rule miningen_US
dc.subjectRecommender systemen_US
dc.titleIntegrating knowledge flow mining and collaborative filtering to support document recommendationen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.jss.2009.06.044en_US
dc.identifier.journalJOURNAL OF SYSTEMS AND SOFTWAREen_US
dc.citation.volume82en_US
dc.citation.issue12en_US
dc.citation.spage2023en_US
dc.citation.epage2037en_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.departmentDepartment of Information Management and Financeen_US
dc.identifier.wosnumberWOS:000272061000009-
dc.citation.woscount11-
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