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.The experimental dataset was the QoSDataset2 from the publicly released WS-DREAM and the Web service searching engines: xmethods.net. The experimental dataset was the QoSDataset2 from the publicly released WS-DREAM, and the Web service searching engines: xmethods.net. The dataset includes 5301 Web services,214 Services Users and response time. We constructed three user-service matrices with different of size = 100 × 100, size = 100 × 150, size = 150 × 100 by randomly extracting a certain number of users and services