【摘要】 为改善Web服务QoS预测效率,向用户提供高质量的Web服务,提出一种融合位置最近邻法则的扩展矩阵分解算法.该方法首先利用用户和服务的位置信息进行用户和服务的最近邻选择,克服了传统的QoS预测算法对Web服务位置信息利用不准确或不足的问题.然后将邻域信息融入矩阵分解框架,改善了矩阵分解技术在QoS预测中本地信息利用不足的问题,同时采用梯度下降算法进行QoS的预测.最后,本文基于真实Web服务数据集WSRec进行了对比实验,实验结果表明了本文所提算法的有效性.更多还原

【Abstract】 To improve the efficiency of Web service QoS prediction,providing users with high-quality Web services,a collaborative filtering method fused matrix factorization and nearest neighbor rule is proposed. Firstly,this method uses the location information of users and services to select neighborhoods of user and service,to overcome the inaccurate or insufficient use of location information in traditional QoS prediction algorithms. Then the neighborhood information is combined into the matrix factorization framework to improve the lack use of local information in matrix factorization technique,and gradient descent algorithm is used for QoS prediction. Finally,based on real Web service dataset WSRec,we conducted experiments compared with other algorithms and the experimental results validate the approach.更多还原