標題: Integrating knowledge flow mining and collaborative filtering to support document recommendation
作者: Lai, Chin-Hui
Liu, Duen-Ren
資訊管理與財務金融系 註:原資管所+財金所
Department of Information Management and Finance
關鍵字: Knowledge flow;Knowledge flow mining;Knowledge sharing;Document recommendation;Collaborative filtering;Sequential rule mining;Recommender system
公開日期: 1-Dec-2009
摘要: Knowledge 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.
URI: http://dx.doi.org/10.1016/j.jss.2009.06.044
http://hdl.handle.net/11536/6328
ISSN: 0164-1212
DOI: 10.1016/j.jss.2009.06.044
期刊: JOURNAL OF SYSTEMS AND SOFTWARE
Volume: 82
Issue: 12
起始頁: 2023
結束頁: 2037
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