• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

J4 ›› 2013, Vol. 35 ›› Issue (9): 132-140.

• 论文 • 上一篇    下一篇

Web服务个性化推荐研究综述

张秀伟1,2,何克清1,王健1,刘建晓3   

  1. (1.武汉大学计算机学院软件工程国家重点实验室, 湖北 武汉 430072;
    2.中国人民解放军94005部队,甘肃 酒泉 735000;3.华中农业大学理学院,湖北 武汉 430070)
  • 收稿日期:2013-04-20 修回日期:2013-07-16 出版日期:2013-09-25 发布日期:2013-09-25
  • 基金资助:

    国家973计划资助项目(2014CB340401,2014CB340404);国家自然科学基金资助项目(61373037,61202031,61100017);国家科技支撑计划项目(2012BAH07B01);国家云计算示范工程“中小企业管理云应用研发与产业化”项目

A survey of personalized web service recommendation 

ZHANG Xiuwei1,2,HE Keqing1,WANG Jian1,LIU Jianxiao3   

  1. (1.State Key Lab of Software Engineering,School of Computer,Wuhan University,Wuhan 430072;
    2.94005 Troops of PLA,Jiuquan 735000;3.College of Science,Huazhong Agricultural University,Wuhan 430070,China)
  • Received:2013-04-20 Revised:2013-07-16 Online:2013-09-25 Published:2013-09-25

摘要:

随着Web服务的广泛使用和互联网上服务数量的增加,如何向用户提供最佳的服务选择列表成为了新的挑战。 Web服务个性化推荐实现了由被动接受用户请求向主动感知用户需求的转变。个性化的Web服务推荐方法已经成为Web服务发现和选择的有效辅助手段。Web服务的个性化推荐技术也成为了近年来服务计算领域的研究热点。对当前Web服务个性化推荐的文献进行了归类分析,总结了当前Web服务个性化推荐的技术现状、研究方法和实验的数据集,列出了未来Web服务个性化推荐研究热点和挑战。

关键词: Web 服务推荐, 协同过滤, 个性化, 服务质量

Abstract:

With the widespread usage of web service and the increase of the number of services in Internet, how to recommend the best service selection list is gradually becoming a great challenge. Personalized technology can change the passive acceptance of user request to active awareness of user requirement. Personalized web service recommendation has gradually become the effective supplementary method for service discovery and selection, thus becoming one of research hotspots in service computing field. We analyze and review the techniques and approaches existing in current literatures that are related to personalized web service recommendation. Then we survey the stateoftheart techniques and methods on personalized recommendation of web service and collect open dataset that are used to validate the personalized web service recommendation approach. Finally, a few of research hotspots and challenges of personalized web service recommendation are outlined.

Key words: web service recommendation;collaborative filter;personalization;quality of service