Deep Modeling of the Evolution of User Preferences and Item Attributes in Dynamic Social Networks

被引：6

作者：

Wu, Peizhi ^{[1
]}

Tu, Yi ^{[2
]}

Yang, Zhenglu ^{[1
]}

Jatowt, Adam ^{[3
]}

Odagaki, Masato ^{[4
]}

机构：

[1] Nankai Univ, CCCE, Tianjin, Peoples R China

[2] George Washington Univ, Washington, DC USA

[3] Kyoto Univ, Kyoto, Japan

[4] Maebashi Inst Technol, Maebashi, Gumma, Japan

来源：

COMPANION PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2018 (WWW 2018) | 2018年

基金：

中国国家自然科学基金;

关键词：

user modeling; social networks; MLP; RNN;

D O I：

10.1145/3184558.3186956

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Modeling the evolution of user preferences and item attributes in a dynamic social network is important because it is the basis for many applications, including recommendation systems and user behavior analysis. This study introduces a comprehensive general neural framework with several optimal strategies to jointly model the evolution of user preferences and item attributes in dynamic social networks. Preliminary experimental results conducted on real-world datasets demonstrate that our model performs better than the state-of-the-art methods.

引用

页码：115 / 116

页数：2

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