Title
User behavior prediction via heterogeneous information preserving network embedding
Source of Publication
Future Generation Computer Systems
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.
Document Type
Article
First Page
52
Last Page
58
Publication Date
3-1-2019
DOI
10.1016/j.future.2018.09.036
Recommended Citation
Yuan, Weiwei; He, Kangya; Han, Guangjie; Guan, Donghai; and Khattak, Asad Masood, "User behavior prediction via heterogeneous information preserving network embedding" (2019). Scopus Indexed Articles. 732.
https://zuscholars.zu.ac.ae/scopus-indexed-articles/732