Open AccessDirected Association-based User Identification Algorithm
Author(s) -
Qiuyan Jiang,
Daofu Gong,
Fenlin Liu
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2171/1/012078
Subject(s) - computer science , identification (biology) , association (psychology) , representation (politics) , directed graph , directed acyclic graph , embedding , semantics (computer science) , graph , artificial intelligence , theoretical computer science , machine learning , data mining , algorithm , philosophy , botany , epistemology , politics , political science , law , biology , programming language
This paper studies the social network with directed association semantics, and proposes an unsupervised user identification algorithm (DAUM-P). The algorithm construct a user associations graph based on multiple types of directed user behaviors, and define directed associations; then, walk based on directed associations to obtain a walking sequence, and then combine the network embedding model to learn user characteristics vector representation; finally, the identification result is given based on the representation vector distance. Finally, the experiments show that the user identification effect is better than that of the baseline method.