计算机科学与探索 ›› 2015, Vol. 9 ›› Issue (5): 565-574.DOI: 10.3778/j.issn.1673-9418.1412034

• 系统软件与软件工程 • 上一篇    下一篇

基于混合协同过滤的个性化Web服务推荐

张雪洁1,2+,王志坚2,张伟建3   

  1. 1. 南京航空航天大学 计算机科学与技术学院,南京 210016
    2. 河海大学 计算机与信息学院,南京 210098
    3. 河海大学 远程与继续教育学院,南京 210098
  • 出版日期:2015-05-01 发布日期:2015-05-06

Personalized Web Services Recommendation Based on Hybrid Collaborative Filtering Algorithm

ZHANG Xuejie1,2+, WANG Zhijian2, ZHANG Weijian3   

  1. 1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
    2. College of Computer and Information, Hohai University, Nanjing 210098, China
    3. College of Distance Learning and Continuing Education, Hohai University, Nanjing 210098, China
  • Online:2015-05-01 Published:2015-05-06

摘要: 随着网络上Web服务的不断增加,Web服务的个性化选择和推荐成为服务计算领域最重要的挑战之一。对个性化Web服务推荐方法进行了研究,提出了基于模型和基于内存混合的Web服务推荐方法。该方法基于客观连续的服务质量(quality of service,QoS)数据和主观离散的评价数据,采用聚类、映射、聚合等算法预测服务的质量,并对用户的期望、评分和服务的QoS信息进行了量化描述。此外,设计了Web服务推荐框架,实现了信息的采集与处理、Web服务的个性化推荐。实验结果表明,与主流的推荐算法相比,所提方法在多种评分误差的评价指标上都取得了更好的结果。

关键词: Web服务, 聚类, 服务质量预测, 个性化, 推荐

Abstract: With the number increasing of Web services, recommending and selecting the personalized Web services for consumers has become one of the most important challenges in the field of service computing. This paper studies the approach for personalized Web services recommendation and proposes a model-based and memory-based quality of service (QoS) prediction approach for Web services. In the proposed approach, the consumers’ expectation, rating and the QoS information are quantified. And based on the objective QoS data and the subjective rating, the quality of services is predicted by clustering, mapping and aggregation. The result is a list of recommended services for selection. In addition, this paper designs a prediction framework, and realizes the information collecting and processing and personalized Web services recommendation through the framework. The experimental results demonstrate that compared with most other service recommendation approaches, the proposed approach increases the accuracy of recommendation results.

Key words: Web service, clustering, QoS prediction, personalization, recommendation