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CLC number: TP39
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2019-09-18
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Learning embeddings of a heterogeneous behavior network for potential behavior prediction
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Yue-yang Wang, Wei-hao Jiang, Shi-liang Pu, Yue-ting Zhuang. Learning embeddings of a heterogeneous behavior network for potential behavior prediction[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(3): 422-435.
@article{title="Learning embeddings of a heterogeneous behavior network for potential behavior prediction",
author="Yue-yang Wang, Wei-hao Jiang, Shi-liang Pu, Yue-ting Zhuang",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="21",
number="3",
pages="422-435",
year="2020",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1800493"
}
%0 Journal Article
%T Learning embeddings of a heterogeneous behavior network for potential behavior prediction
%A Yue-yang Wang
%A Wei-hao Jiang
%A Shi-liang Pu
%A Yue-ting Zhuang
%J Frontiers of Information Technology & Electronic Engineering
%V 21
%N 3
%P 422-435
%@ 2095-9184
%D 2020
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1800493
TY - JOUR
T1 - Learning embeddings of a heterogeneous behavior network for potential behavior prediction
A1 - Yue-yang Wang
A1 - Wei-hao Jiang
A1 - Shi-liang Pu
A1 - Yue-ting Zhuang
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 21
IS - 3
SP - 422
EP - 435
%@ 2095-9184
Y1 - 2020
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1800493
Abstract: Potential behavior prediction involves understanding the latent human behavior of specific groups, and can assist organizations in making strategic decisions. Progress in information technology has made it possible to acquire more and more data about human behavior. In this paper, we examine behavior data obtained in real-world scenarios as an information network composed of two types of objects (humans and actions) associated with various attributes and three types of relationships (human-human, human-action, and action-action), which we call the heterogeneous behavior network (HBN). To exploit the abundance and heterogeneity of the HBN, we propose a novel network embedding method, human-action-attribute-aware heterogeneous network embedding (a4HNE), which jointly considers structural proximity, attribute resemblance, and heterogeneity fusion. Experiments on two real-world datasets show that this approach outperforms other similar methods on various heterogeneous information network mining tasks for potential behavior prediction.
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