A QoS-aware Web service recommendation method based on user and service clustering

Abstract

The invention provides a Web service recommendation method based on QoS perception of user and service clustering, which clusters users with similar service preference by using cosine similarity calculation based on QoS record of user calling service. Then the WSDL file, which is the description document of the service itself, is extracted and normalized. Means + + algorithm clusters services withsimilar functional characteristics; Finally, the similar user set and the similar service set are integrated into the collaborative filtering algorithm to calculate the missing QoS value of the service invocation matrix and generate recommendations. By using the data set provided by the WSDream Open Source Project, the simulation results show that the proposed Web service recommendation method based on user and service clustering has better results on two mainstream evaluation indexes, namely mean absolute error and root mean square error, compared with the existing technology.

Description

technical field [0001] The invention relates to the field of service recommendation in data mining, in particular to a Web service recommendation method based on user and service clustering QoS perception. Background technique [0002] With the rapid development of the current Internet and the explosive growth of web services with various functions available on it, it is becoming more and more difficult for users to choose appropriate web services, so it is very necessary to have related methods to automatically and effectively select appropriate web services Recommend to users. In the face of massive service types and service information, how to efficiently and accurately recommend related services to users according to user specificity has increasingly become a research problem in the industry. Web service recommendation provides a research direction to solve this difficult problem. [0003] Web service recommendation is to establish a recommendation model for service re...

AI Technical Summary

Problems solved by technology

[0004] These studies have relatively good performance in predicting and recommending related services and variables based on QoS, but many of them ignore the significant impact of service functions and user similarity on the accuracy of recommendation results, and many recommendation models are deeply affected by the sparsity of QoS matrix data. These factors lead to the problem that the accuracy of the recommendation results is not high, which needs to be solved urgently

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