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

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. © 2022 Editorial Board of Jilin University. All rights reserved.