面向用户群体的Web服务推荐

针对Web服务推荐中服务用户调用Web服务的服务质量数据稀疏性导致的低推荐质量问题,提出了一种面向用户群体并基于协同过滤的Web服务推荐算法(WRUG)。首先,为每个服务用户根据用户相似性矩阵构建其个性化的相似用户群体;其次,以相似用户群体中心点代替群体从而计算用户群体相似性矩阵;最后,构造面向群体的Web服务推荐公式并为目标用户预测缺失的Web服务质量。通过对197万条真实Web服务质量调用记录的数据集进行对比实验,与传统基于协同过滤的推荐算法(TCF)和基于用户群体影响的协同过滤推荐算法(CFBUGI)相比,WRUG的平均绝对误差下降幅度分别为28.9%和4.57%;并且WRUG的覆盖率上升幅度分别为110%和22.5%。实验结果表明,在相同实验条件下WRUG不仅能提高Web服务推荐系统的预测准确性,而且能显著地提高其有效预测服务质量的百分比。

Web service recommendation for user group

The sparse data of Web services Quality of Service (QoS) which is invoked by service users in Web service recommendation may lead to low recommendation quality. In order to solve the problem, a collaborative filtering based Web service Recommendation algorithm for User Group (WRUG) was proposed. Firstly, personalized similar user group was constructed for each service user according to user similarity matrix. Secondly, instead of the group, the center of similar user group was employed to compute the user group similarity matrix. Finally, Web service recommendation equation with user group was defined and missing QoS values of Web service were predicted for target user. And a dataset was used for experiments which included 1.97 million real-world Web QoS invocation records. Compared with Traditional Collaborative Filtering algorithm (TCF) and Collaborative Filtering recommendation algorithm Based on User Group Influence (CFBUGI), the mean absolute error of the proposed WRUG was decreased by 28.9% and 4.57% respectively, and the coverage rate of WRUG was increased by 110% and 22.5% separately. The experimental results show that the proposed WRUG can not only achieve better prediction accuracy of Web service recommendation system, but also noticeably enhance the percentage of valuable predicted QoS values under the same experimental settings.