Online service reputation measurement method based on semi-supervised learning

Abstract

The invention discloses an online service reputation measurement method based on semi-supervised learning and belongs to the online reputation measurement and on-line service field. The method comprises the following steps of firstly, carrying out normalization processing on the attribute scoring matrix R of a service and analyzing a principal component, and carrying out dimension reduction on a service attribute; then, synthesizing service multi-dimensional attribute information, manually labeling a training set and training a classifier model, based on an improved semi-supervised cooperationtraining algorithm, using an acquired classifier to carry out reputation classification on services, and adding the classified services and classification tags to the training set so as to retrainingthe classifier; and finally, using the new and acquired classifier to classify the online services so as to realize reputation measurement. In the invention, the reputation measurement of the services is realized by establishing the multi-classifier model of the services, and simultaneously, the semi-supervised learning algorithm is used to add the unlabeled services to the training set so as toretrain the classifier when the classifier is modeled, and classifier model classification performance is increased and simultaneously the cost of manually labeled samples is reduced.

Description

technical field [0001] The invention relates to an online service reputation measurement method based on semi-supervised learning, which belongs to the field of online reputation measurement and online services. Background technique [0002] In recent years, with the continuous maturity of the Internet and pervasive computing technology, online services have been widely used due to their convenient acquisition, simple operation, and low cost, and have become the main driving force for the development of today's service industry. However, when faced with a large number of services with the same function, it is difficult for users to choose reasonably. Not only the functional requirements, but also the non-functional attribute Quality of Service (QoS) requirements must be considered. As an important index to measure QoS, reputation has become one of the main information that users refer to when choosing a service. Due to the virtuality and information asymmetry of the Interne...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide an online service reputation measurement method based on semi-supervised learning, which is used to solve the defects of excessive granularity and incomplete dimension consideration in the existing online service reputation measurement, and overcome the problems caused by manual labeling of service training sets. the problem of high cost

Images