Cross-platform data matching method, device, computer device and storage medium

Abstract

The invention relates to a cross-platform data matching method, which particularly includes steps of receiving a data matching request sent by a terminal; acquiring group behavior data corresponding to a first user group from a first social networking service platform; learning the group behavior data to obtain a group characteristic distribution function; acquiring associated users and corresponding behavior data of an appointed root node user from a second social networking service platform; learning the behavior data of the root node user, and generating the group characteristic distribution function after matching with the root node user; carrying out the behavior learning on the behavior data of the associated user; calculating the maximum entropy of the group characteristic distribution function after matching with associated users, and confirming the associated user with the maximum entropy correspondingly as a matching user of the first user group; taking the confirmed matchinguser as the current root node user, and confirming the next matching user until the confirmed matching user meets the condition of setting number; completing the group matching. The method can realize the data matching of different social networking service platforms.

Description

Technical field [0001] The present invention relates to the field of computer technology, in particular to a cross-platform data matching method, device, computer equipment and storage medium. Background technique [0002] Social networks have become "sensors" that perceive individual online behavior. A large number of social "sensors" can perceive information at different levels of users, including name, age, gender, spending power, making friends, payment habits, shopping tendencies, and opinions , The attitude of goods and services, etc. [0003] In financial big data applications, different social networks are connected to the same real users, and the same users on different social network platforms may publish different content (themes are different, such as the food social network and the picture social network. Information is difficult to match; the scale of published content is different, for example, the content density of a new platform is different from that of a platfo...

AI Technical Summary

Problems solved by technology

[0003] In the application of financial big data, the same real users are connected in different social networks, and the content published by the same user on different social network platforms is likely to be different (different themes, such as food social network and picture social network) It is difficult to match information; the scale of published content is different, such as the content density of a new platform and a platform that has been used for a long time is different), then, it is difficult to match user identities with these different contents, which eventually leads to cross-platform data fusion Obstacles, so that big data analysis is limited to a single social network platform, the analysis effect is poor, and big data cannot be fully utilized

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