Group relationship type identification method and device

Abstract

The invention discloses a group relationship type identification method and device, belonging to the field of data mining and analysis. The group relationship type identification method includes: receiving a group relationship type identification request; obtaining group information corresponding to the request; extracting target data corresponding to each member of the group from the group information according to a predefined target data field, Composing a personal data set; inputting the personal data set into the group classification deep neural network model, the group classification deep neural network model is obtained according to the training of the predefined target data field; according to the output of the group classification deep neural network model, Determine the relationship type of the group. The technical solution of the present invention combines data mining and analysis technology, and the relationship type of the group can be identified through simple preprocessing operations and the deep neural network model of group classification, which reduces the labor cost required for early feature acquisition, and the deployment and construction of the model is low in complexity , the generality of the model is strong.

Description

technical field [0001] The invention relates to the field of data mining and analysis, in particular to a group relationship type identification method and device. Background technique [0002] There are various social circles in the social network, and each social circle has certain related user members, such as family members, company colleagues, school classmates, and so on. In the context of the big data era, identifying the relationship types of social circles is a very important issue, and the identification results have a wide range of practical applications, such as big data analysis, advertising and so on. [0003] Existing technical solutions either use manual identification and classification, or use traditional machine learning classification models, which require a lot of feature engineering work at the community level. The required features include members of social circles, age and gender distribution of members, geographical distribution, etc. . Improvement...

AI Technical Summary

Problems solved by technology

[0004] 1> The completion of feature engineering requires a lot of manpower input, and the development cycle is long;

[0005] 2> Different circle classification scenarios require different feature engineering work, which has poor versatility;

[0006] 3> Due to the large amount of feature processing involved, the complexity of model deployment is high;

[0007] 4> The characteristics of the circle come from the statistics of personal characteristics, and a lot of information that is helpful to improve the accuracy rate is lost

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