Dataset for collaborative prediction of web service quality based on user preferences and services

The prediction of web service quality plays an important role in improving user services; it has been one of the most popular topics in the field of Internet services. In traditional collaborative filtering methods, differences in the personalization and preferences of different users have been ignored. In this paper, we propose a prediction method for web service quality based on different types of quality of service (QoS) attributes. Different extraction rules are applied to extract the user preference matrices from the original web data, and the negative value filtering-based top-K method is used to merge the optimization results into the collaborative prediction method. Thus, the individualized differences are fully exploited, and the problem of inconsistent QoS values is resolved. The experimental results demonstrate the validity of the proposed method. Compared with other methods, the proposed method performs better, and the results are closer to the real values.Web服务质量预测对于提升用户服务体验具有重要意义，亦是互联网服务领域的热门研究课题之一。传统协同过滤方法往往忽略了不同用户的个性化特征与偏好差异。针对不同类型的服务质量（Quality of Service, QoS）属性，本文提出了一种Web服务质量预测方法。该方法通过多种提取规则从原始Web数据中提取用户偏好矩阵，并结合基于负值过滤的Top-K方法将优化结果融合至协同预测框架中，由此充分挖掘用户的个性化差异，同时解决了QoS数值不一致的问题。实验结果验证了所提方法的有效性。相较于其他同类方法，所提方法的预测性能更优，预测结果与真实值更为贴近。