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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.
With the development of cloud computing, more and more Web services with the same functions and different qualities of service (QoS) appear on the Internet.The service recommendation based on service quality aims at selecting the service quality of users Demand service has become a hot topic in the field of service computing.As very few users have called all the candidate services, the recommendation system will face the problem of the lack of service quality. Therefore, based on the idea of collaborative filtering, this paper proposes a service quality prediction Algorithm RST.Compared with the previous algorithms, the RST algorithm uses the reverse prediction mechanism to solve the data sparseness problem and improves the prediction accuracy.In addition, the RST algorithm can automatically improve the confidence model based on the user’s feedback on the recommended results, Predict the effect.Finally, based on the real data set, verify the effect of RST prediction algorithm and measure the influence of each parameter on the prediction result.