基于QoS反向预测的服务推荐
发布时间：2018-08-25 20:04
【摘要】：随着云计算的发展,互联网上涌现出越来越多的功能相同但服务质量(QoS)不同的Web服务.基于服务质量的服务推荐,旨在从这些等功能服务中挑选出满足用户服务质量需求的服务,已成为服务计算领域的一个热门课题.由于极少有用户曾调用过所有候选服务,推荐系统将面临服务质量缺失的问题,因此,基于协同过滤的思想,提出一种服务质量预测算法RST.与以往算法相比,RST算法利用反向预测机制解决数据稀疏问题,提高了预测准确度.此外,RST算法基于用户对推荐结果的反馈,自动建立与维护信任度模型,可动态改善预测效果.最后,基于真实的数据集,验证RST预测算法的效果,并衡量各参数对预测结果的影响.
[Abstract]:With the development of cloud computing, more and more Web services with the same function but different quality of service (QoS) emerge on the Internet. Service recommendation based on quality of Service (QoS), which aims to select services from these functional services to meet the needs of users, has become a hot topic in the field of service computing. Because very few users have ever called all candidate services, the recommendation system will face the problem of missing quality of service. Therefore, based on the idea of collaborative filtering, a quality of service prediction algorithm RST. is proposed. Compared with the previous algorithms, the RST algorithm uses reverse prediction mechanism to solve the problem of data sparsity, and improves the accuracy of prediction. In addition, based on the user feedback to the recommended results, the RST algorithm automatically establishes and maintains the trust model, which can dynamically improve the prediction results. Finally, based on the real data set, the effectiveness of the RST prediction algorithm is verified, and the influence of various parameters on the prediction results is measured.
【作者单位】： 浙江大学计算机学院;
【基金】：国家科技支撑计划项目(2011BAH16B04)资助 国家自然科学基金项目(61173176)资助 浙江省科技项目(2008C03007)资助 国家“八六三”高技术研究发展计划项目(2011AA010501)资助
【分类号】：TP393.09
[Abstract]:With the development of cloud computing, more and more Web services with the same function but different quality of service (QoS) emerge on the Internet. Service recommendation based on quality of Service (QoS), which aims to select services from these functional services to meet the needs of users, has become a hot topic in the field of service computing. Because very few users have ever called all candidate services, the recommendation system will face the problem of missing quality of service. Therefore, based on the idea of collaborative filtering, a quality of service prediction algorithm RST. is proposed. Compared with the previous algorithms, the RST algorithm uses reverse prediction mechanism to solve the problem of data sparsity, and improves the accuracy of prediction. In addition, based on the user feedback to the recommended results, the RST algorithm automatically establishes and maintains the trust model, which can dynamically improve the prediction results. Finally, based on the real data set, the effectiveness of the RST prediction algorithm is verified, and the influence of various parameters on the prediction results is measured.
【作者单位】： 浙江大学计算机学院;
【基金】：国家科技支撑计划项目(2011BAH16B04)资助 国家自然科学基金项目(61173176)资助 浙江省科技项目(2008C03007)资助 国家“八六三”高技术研究发展计划项目(2011AA010501)资助
【分类号】：TP393.09
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