Method and device for grouping user portraits

Abstract

The embodiment of the invention relates to a method and a device for grouping user portraits, which relate to the data analysis field and can enable user portrait grouping to be more stable and accurate. The method comprises steps: based on the label of each user portrait, a label network G (V, E) is built, wherein V presents a vertex set in the label network, each vertex presents one user portrait, E presents an edge set in the label network, and each edge presents a connection line of two vertexes corresponding to two user portraits having at least one common label; based on the label network, a line graph network G' (V', E', W') is built, wherein V' presents a vertex set in the line graph network, each vertex presents one edge in the label network, E' presents an edge set in the line graph network, each edge presents a connection line of two vertexes corresponding to two edges with the common vertex in the line graph network with the label network, and W' presents a weight set for edges in the line graph network; community division is carried out on vertexes in the line graph network; and a community division result is converted to a user portrait grouping result.

Description

technical field [0001] Embodiments of the present disclosure relate to the field of data analysis, and in particular, to a method and device for grouping user portraits. Background technique [0002] The biggest change of user portrait grouping to the network marketing environment is that it breaks the data silos and truly understands users, and can associate and group individual users in social networks. The current user portrait grouping method is very dependent on parameters. For example, the K-means algorithm is very dependent on the value of k and the vector selection of the initial center, which makes the result of user portrait grouping unstable. Contents of the invention [0003] The purpose of the embodiments of the present disclosure is to provide a user portrait grouping method and device, which can obtain a stable user portrait grouping result. [0004] In order to achieve the above purpose, an embodiment of the present disclosure provides a user portrait grou...

AI Technical Summary

Problems solved by technology

The current user portrait grouping method is very dependent on parameters. For example, the K-means algorithm is very dependent on the value of k and the vector selection of the initial center, which makes the result of user portrait grouping unstable.

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