Abstract
Among the algorithms used to assess user credibility in social networks, most of them quantify user information and then calculate the user credibility measure by linear summation. The algorithm above, however, ignores the aliasing of user credibility results under the linear summation dimension, resulting in a low evaluation accuracy. To solve this problem, we propose a user credibility evaluation method based on a soft-margin support-vector machine (SVM). This method transforms the user credibility evaluation dimension from a linear summation dimension to a plane coordinate dimension, which reduces the evaluation errors caused by user aliasing in the classification threshold interval. In the quantization of user information, the ladder assignment method is used to process the user text information and numeric information, and the weight assignment method of information entropy is used to calculate the weight assignment among different feature items, which reduces the errors caused by the inconsistency of the order of magnitude among different types of user information. Simulation results demonstrate the superiority of the proposed method in the user’s credibility evaluation results.
Highlights
In the era of big data, the number of social network platforms and users has been growing exponentially, making social network platforms indispensable information interaction platforms and information communication media in people’s daily lives and huge and complex user groups [1, 2]
Related Work is paper first proposes a social network user credibility evaluation method model UCSSVM based on a soft-margin support-vector machine (SVM). e model processes the user profile information and user generated content information using the ladder assignment method and information entropy weight distribution method and uses the soft-margin SVM algorithm to evaluate the measurement set of user reliability so as to avoid the evaluation result error caused by different types of user information in different orders of magnitude and types in other algorithms and solve the problem of user aliasing at the classification threshold in other algorithms
To alleviate the problem of SVM overfitting and the existence of the data to be classified in the interval, we propose allowing the SVM algorithm to have reasonable errors in some classification results so that the soft-margin SVM is introduced
Summary
In the era of big data, the number of social network platforms and users has been growing exponentially, making social network platforms indispensable information interaction platforms and information communication media in people’s daily lives and huge and complex user groups [1, 2]. Zheng and Qu [27] used the entropy weight method to solve the weight assignment problem in four factors (social relationship strength, social influence scope, information value, and information transmission control) to propose a new user credibility evaluation model. To solve the above problems, this paper first proposes a social network user credibility evaluation method model UCSSVM based on a soft-margin support-vector machine (SVM). E model processes the user profile information and user generated content information using the ladder assignment method and information entropy weight distribution method and uses the soft-margin SVM algorithm to evaluate the measurement set of user reliability so as to avoid the evaluation result error caused by different types of user information in different orders of magnitude and types in other algorithms and solve the problem of user aliasing at the classification threshold in other algorithms. An interval hyperplane is a reasonable proportion of user data as it simultaneously avoids introducing slack variable credibility evaluation errors to the customer while adding the balance coefficient C in the objective function to solve this problem. erefore, the equilibrium coefficient C is defined as the weight coefficient between the hyperplane with the largest interval of the balance support vector machine and the guarantee of the minimum deviation of the data points
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