A cross-platform social network user identification method

Abstract

The invention discloses a cross-platform social network user identity identification method, which combines user profile data and user behavior data; in the analysis of user behavior data, a text similarity calculation method based on frequent patterns is proposed, The personalized similarity calculation of the user's text is realized; in terms of the weight distribution of many dimensional data, a posterior probability weight distribution method based on information entropy is proposed to solve the problem of multi-dimensional data weight imbalance; The matching method realizes many-to-many user identification, and the present invention improves the user identification accuracy.

Description

technical field [0001] The invention belongs to the field of data mining, and more specifically, is a cross-platform social network user identity identification method. Background technique [0002] With the rapid development of social networks, an entity user generally has multiple network accounts on social network platforms. It is of great significance to identify the entity users corresponding to these network accounts, such as in business applications, network security, recommendation systems, etc. In recent years, many scholars have done a lot of work in this area. [0003] In the existing cross-platform social network identification technology, cross-platform social network user identification is realized by using ranking-based cross-matching method (RCM), but the RCM method lacks the right to analyze user behavior and assign user attributes reasonably Therefore, the accuracy of the final recognition result is not high, because the user behavior data can often reflec...

AI Technical Summary

Problems solved by technology

[0003] In the existing cross-platform social network identification technology, the cross-platform social network user identification is realized by using the ranking-based cross-matching method (RCM), but the RCM method lacks the ability to analyze user behavior and assign reasonable weights to user attributes. value, which leads to the low accuracy of the final recognition result, because user behavior data often better reflect the real personality of the user, and different dimensions of the user play different weights, but in the existing cross-platform social network In identification technology, the weight of user attributes is rarely considered, or the weight of different dimensions of users is given by the posterior experience method, which also leads to the low accuracy of the final recognition result

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