基于知识图谱的多粒度社交网络用户画像构建方法

doi:10.13229/j.cnki.jdxbgxb20210884

Abstract

Abstract:

Aiming at the ambiguity of attribute information partition of social users in network， a multi-granularity social user image construction method based on knowledge graph was proposed. Using knowledge graph to map user information and classify multi-dimensional attributes of mapped data， a set of social users was established. The same weight value of each user's corresponding attribute tag in the set was given， and the weight parameters and the frequency of interest tag were calculated. Then， according to the click-frequency information of users， a sequence of continuous active locations and active ranges of users in a multi-granularity social network was constructed， and the features of each user in the sequence were calculated and divided in a unified manner to complete the establishment of user images. Simulation experiments show that the user profile construction time under the proposed method does not exceed 25 ms， and the maximum number of matching overlaps of corresponding attributes is 705. The matching degree is high， indicating that the constructed user profile information is clearly divided.

Key words: computer application, knowledge graph, social users, efficient mapping, multi-dimensions, weight values

CLC Number:

TP18

Cai-mao LI,Shao-fan CHEN,Cheng-rong LIN,Hao LIN,Qiu-hong CHEN. Multi⁃grained social network user portrait construction method based on knowledge graph[J].Journal of Jilin University(Engineering and Technology Edition), 2022, 52(12): 2947-2953.

Figures/Tables 7

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序号	群组数量	成员数量	社交话题	属性数量
1	2	55	电影	100
2	5	81	文学	100
3	4	128	创业	300
4	5	135	摄影	300
5	4	122	生活	200

Multi⁃grained social network user portrait construction method based on knowledge graph

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