English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 49521/84656 (58%)
造訪人次 : 7589348      線上人數 : 102
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜尋範圍 查詢小技巧:
  • 您可在西文檢索詞彙前後加上"雙引號",以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋,建議至進階搜尋限定作者欄位,可獲得較完整資料
  • 進階搜尋
    請使用永久網址來引用或連結此文件: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/109376


    題名: A Hybrid Approach for Knowledge Recommendation
    作者: Liang, Wen-Yau;Huang, Chun-Che;Pan, Yu-Ting
    關鍵詞: Knowledge recommendation;clustering techniques;rough set theory;genetic algorithm
    日期: 2016
    上傳時間: 2017-01-17 11:02:19 (UTC+8)
    出版者: 淡江大學出版中心
    摘要: 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.
    關聯: International Journal of Information and Management Sciences 27(1), pp.17-39
    DOI: 10.6186/IJIMS.2016.27.1.2
    顯示於類別:[資訊與管理科學期刊] 第27卷第1期

    文件中的檔案:

    檔案 描述 大小格式瀏覽次數
    index.html全文連結0KbHTML102檢視/開啟

    在機構典藏中所有的資料項目都受到原著作權保護.

    TAIR相關文章

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - 回饋