A Hybrid Approach for Knowledge Recommendation

doi:10.6186/IJIMS.2016.27.1.2

淡江大學機構典藏 > 淡江出版期刊 > 資訊與管理科學期刊 > 第27卷第1期 > Item 987654321/109376

請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/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	0Kb	HTML	503	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

資料載入中.....