基于改进协同过滤的个性化Web服务推荐方法研究

摘要

目前基于协同过滤(collaborative filtering,CF)的Web服务推荐算法,使用的是Web服务的非功能性属性服务质量(quality of services,QoS),但是这类方法直接使用所有用户的QoS数据进行预测,并没有考虑用户的个性化偏好问题,导致在相似邻居的选择阶段会得到不真实的相似度结果,进而影响QoS预测准确率.针对以上问题,提出了一种基于用户偏好的改进协同过滤Web服务推荐算法.该算法从QoS数据中提取出用户偏好数据,并将其作为近似邻居的选择标准,然后使用top-k算法确定目标用户及服务的相似邻居集合,最后联合相似邻居偏好比重,使用调和的皮尔逊相关系数算法(Pearson correlation coefficient,PCC)预测目标用户及服务的QoS值.实验结果表明,该算法能有效提高QoS预测准确率,从而提高了Web服务推荐质量.%Existing Web service recommendation algorithms based on collaborative filtering uses quality of services of non-functional attrib-ute. However,they make a prediction directly by means of QoS data from all users without considering the preferences of them,which lead to unreal similarity in selection of similar neighbors and further affect the accuracy of QoS. In view of that,we propose an improved collabora-tive filtering algorithm based on users preference. It takes the preferable data of users in QoS as the standard of similar neighbors,and then i-dentifies the similar neighbor sets of target users or services by top-k algorithm. Finally,the Pearson correlation coefficient is used to predict the QoS of targets users or services in combination of preference ratio of similar neighbors. The experiment shows that the algorithm proposed can effectively improve the accuracy of QoS,thus enhancement of the recommendation quality of Web service.