Collaborative Web service performance prediction method based on position clustering

Abstract

The invention discloses a collaborative Web service performance prediction method based on position clustering. The method comprises the following steps: according to position information of users and services, respectively clustering the users and services which are the same as an Autonomous System; calculating a centroid-based similarity; using a quick sorting algorithm to perform descending sort on similar clusters, finding the previous K neighbor clusters as candidate clusters, so as to finish the neighbor pre-selection; calculating the similarity between an active user and a user in each user candidate cluster, and the similarity between an active service and each service candidate cluster; respectively using the previous K most similar users to predict a performance value of an unknown service for the active user and using the previous K most similar services to predict a performance value of the unknown service for the active service; and synthesizing all performance values to calculate a quality predicted value of a source user relative to a target service. The collaborative Web service performance prediction method based on the position clustering provided by the invention can solve the problems like data sparse and cold boot in Web service recommendation, and improve the precision and coverage rate of Web service performance prediction and Web service recommendation.

Description

technical field [0001] The invention relates to Web service quality prediction technology, in particular to a collaborative Web service performance prediction method based on location clustering for Web service selection or recommendation. Background technique [0002] Web service is a self-describing and self-contained available network module, which is used to help realize the interactive operation between different machines through remote calls, and has become the main technology for constructing distributed systems, modular applications and service-oriented application integration. , such as e-commerce, vehicle systems, multimedia services, etc. With the rapid growth of the number of Web services on the network, it becomes more challenging to recommend the optimal service for users. In order to find the optimal service among a large number of alternative Web services with the same function, the quality of Web services, that is, performance, is widely used to describe an...

AI Technical Summary

Problems solved by technology

[0004] In the prior art, the most commonly used method of predicting missing performance values ​​has the following defects: 1) The method of predicting by calculating the similarity of users or services cannot provide accurate performance predictions for cold-start users, and it may be due to sparse data leading to performance prediction failure

2) The previous methods may suffer from data sparsity and cold start problems, which reduces the prediction accuracy, and when faced with a large number of users and services, the scalability is not good

In addition, some studies have proposed to cluster users or services based on performance similarity, and then use the cluster center performance value to pre-fill the user-service performance matrix, which can solve the above problems, but the calculation cost is relatively high, and When the data is very sparse, the similarity is difficult to measure accurately, resulting in reduced performance prediction accuracy

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