[1]徐守坤,庄福宝.云环境下基于资源类别聚合的算法[J].常州大学学报(自然科学版),2014,(02):22-25.[doi:10.3969/j.issn.2095-0411.2014.02.007]
　XU Shou-kun,ZHUANG Fu-bao.Research of Resource Aggregation Algorithm Based on Cloud Environment[J].Journal of Changzhou University(Natural Science Edition),2014,(02):22-25.[doi:10.3969/j.issn.2095-0411.2014.02.007]
点击复制

云环境下基于资源类别聚合的算法()
分享到：

常州大学学报(自然科学版)[ISSN:2095-0411/CN:32-1822/N]

卷:
期数:
2014年02期
页码:
22-25
栏目:
出版日期:
2014-04-30

文章信息/Info

Title:
Research of Resource Aggregation Algorithm Based on Cloud Environment
作者:
徐守坤; 庄福宝
常州大学 信息科学与工程学院,江苏 常州 213164
Author(s):
XU Shou-kun; ZHUANG Fu-bao
School of Information Science and Engineering,Changzhou University,Changzhou 213164,China
关键词:
云环境; 资源分类; 类别聚合; 个性化推荐
Keywords:
cloud environment;  resource classification;  category aggregation;  personalized recommendation
分类号:
TP301.6
DOI:
10.3969/j.issn.2095-0411.2014.02.007
文献标志码:
A
摘要:
随着云服务类型和数量不断增长,用户很难从中选择有效的云服务。为解决云环境下海量服务的个性化推荐问题,提出了一种基于类别聚合的个性化推荐算法。首先对数据存储节点上的资源进行分类;然后计算类别之间的相关性;其次寻找资源的最近邻;最后产生推荐集。通过实验数据进行验证,提出的云环境下的协同过滤算法与传统协同过滤算法相比,推荐质量和系统性能都有很大提高。
Abstract:
With the growing of numbers and types of cloud services,user are faced with the issue of how to choose the best cloud service.In order to solve the problem of personalized recommendation of cloud environment,this paperpresents a cloud service recommendation algorithm based on category aggregation.Firstly,the resources on the data storage node is classified; secondly,the correlation between the categories is calculated; thirdly,a search of the nearest neighbor of the resources is made; finally,the user recommendation sets are created.Validated by the experiment data,the collaborative filtering algorithm based on cloud computing in this paper,compared with the traditional collaborative filtering algorithm,there has been great improvement in the recommendation quality and system performance.

参考文献/References:

[1]Schafer J B,Konstan J A,Riedl J.E-commerce recommendation applications[J].Data Mining and Knowledge Discovery,2001,5(1-2):115-153.
[2]
[2]Keunho Choi,Yongmoo Suh.A new similarity function for selecting neighbors for each target item in collaborative filtering[J].Knowledge-based systems,2013,37(1):146-153.
[3]Gediminas Adomavicius,Alexander Tuzhilin.Toward the next generation of recommender systems:A survey of the State-of-the-art and possible extensions[J].IEEE Transactions on Knowledge and Data Engineering,2005,17(6):734-749.
[4]Zhang L J,Zhang J,Cai H.Service Computing[M].Beijing:Tsinghua UniversityPress,2007:7-8.
[5]汪静,印鉴.一种优化的Item-Based协同过滤推荐算法[J].小型微型计算机系统,2010,31(12):2337-2342.
[6]潘托宇,朱珍民.一种改进的基于协同过滤的个性化推荐算法[J].微计算机信息,2010,26(123):228-229.
[7]陈志敏,姜艺.综合项目评分和属性的个性化推荐算法[J].微电子学与计算机,2011,28(9):186-189.
[8]朱丽中,徐秀娟,刘宇.基于项目和信任的协同过滤推荐算法[J].计算机工程,2013,39(1):58-66.
[9]张忠平,郭献丽.一种优化的基于项目评分预测的协同过滤推荐算法[J].计算机应用研究,2008,25(9):2658-2660.
[10]赵玉艳,谷胜伟.一种面向云计算环境的服务推荐算法[J].巢湖学院学报,2012,14(3):42-47.
[11]徐义峰,陈春明,徐云青.一种基于分类的协同过滤算法[J].计算机系统应用,2007(1):47-50.
[12]Resnick P,Iacovou N,Suchak M,et al.GroupLens:An open architecture for collaborative filtering of netnews[C]∥Proceedings of the 1994 ACM Conference onComputer Supported Cooperative Work.New York:ACM Press,1994:175-186.

备注/Memo

备注/Memo:
作者简介:徐守坤(1972-),男,吉林蛟河人,教授,博士,主要从事计算机应用、软件开发、智能空间等研究。
更新日期/Last Update: 2014-04-20