User behavior prediction via heterogeneous information preserving network embedding
ORCID Identifiers
Document Type
Article
Source of Publication
Future Generation Computer Systems
Publication Date
3-1-2019
Abstract
© 2018 Elsevier B.V. User behavior prediction with low-dimensional vectors generated by user network embedding models has been verified to be efficient and reliable in real applications. However, most user network embedding models utilize homogeneous properties to represent users, such as attributes or user network structure. Though some works try to combine two kinds of properties, the existing works are still not enough to leverage the rich semantics of users. In this paper, we propose a novel heterogeneous information preserving user network embedding model, which is named HINE, for user behavior classification in user network. HINE applies attributes, user network connection, user network structure, and user behavior label information for user representation in user network embedding. The embedded vectors considering these multi-type properties of users contribute to better user behavior classification performances. Experiments verified the superior performances of the proposed approach on real-world complex user network dataset.
DOI Link
ISSN
Publisher
Elsevier B.V.
Volume
92
First Page
52
Last Page
58
Disciplines
Computer Sciences
Keywords
Behavior prediction, Complex networks analysis, Heterogeneous information, Network embedding
Scopus ID
Recommended Citation
Yuan, Weiwei; He, Kangya; Han, Guangjie; Guan, Donghai; and Khattak, Asad Masood, "User behavior prediction via heterogeneous information preserving network embedding" (2019). All Works. 3839.
https://zuscholars.zu.ac.ae/works/3839
Indexed in Scopus
yes
Open Access
no