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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/109376


    Title: A Hybrid Approach for Knowledge Recommendation
    Authors: Liang, Wen-Yau;Huang, Chun-Che;Pan, Yu-Ting
    Keywords: Knowledge recommendation;clustering techniques;rough set theory;genetic algorithm
    Date: 2016
    Issue Date: 2017-01-17 11:02:19 (UTC+8)
    Publisher: 淡江大學出版中心
    Abstract: Knowledge sharing is critical to knowledge management as it enables employees to share their knowledge. However, knowledge searching is a very time-consuming work. Additionally, in the context of an unsolved puzzle or unknown task, users typically have to determine the knowledge for which they will search. Therefore, knowledge management platforms for enterprises should have knowledge recommendation functionality. Hybrid recommendation systems (RS) have been developed to overcome, or at least to mitigate, the limitations of collaborative filtering. Because Genetic Algorithm (GA) is good at searching, it can cluster data according to similarities. However, the increase in the amount of data and nformation reduces the performance of a GA, thereby increasing cost of finding a solution. This work applies a novel method for incorporating a GA and rough set theory into clustering. In this paper, this work presents a hybrid knowledge recommendation model, which has a two-phase model for clustering and recommending. Approach implementation is demonstrated, as are its effectiveness and efficiency.
    Relation: International Journal of Information and Management Sciences 27(1), pp.17-39
    DOI: 10.6186/IJIMS.2016.27.1.2
    Appears in Collections:[資訊與管理科學期刊] 第27卷第1期

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