With the rapid development of the Internet, a large number of Web services appear on the network. At present, many large e-commerce systems, are more or less use of various forms of Qos prediction system. Prediction system is called to the consumer's history as the foundation, through the calculation of similarity to recommend the consumer interest to consumer goods. The Web service prediction system is based on mass data is a kind of advanced business intelligence platform, it can help consumers save time to better, faster from a large number of similar items to choose their own things. So, technology research and prediction methods of Web service Qos of great significance.
This paper first introduces the research background prediction system, and puts forward the research significance. For concept, Web services and Qos model, technology and development trend as well as some related content has made the simple analysis. The prediction system of the existing analysis shows that the prediction system is mainly composed of the two important part of data and algorithms, so the data acquisition was introduced in detail. The algorithm is to predict the core of the system, this paper focuses on the arithmetical average algorithm, the item-based collaborative filtering algorithm and Euclidean distance similarity algorithm idea and the formula and a simple analysis of the existing problems based on the. The last is predicted through MATLAB algorithm, write down three kinds of algorithm and simulation experiments.
Simulation of Qos data using the Matlab of Guelph University Eyhab Al-Masri collected online. The simulation result shows that, there is significantly improved Euclidean distance similarity collaborative filtering algorithm for the arithmetical average algorithm and based on item rating accuracy in predicting the outcome of hand.
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