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

Peizhi Wu, Yi Tu, Zhenglu Yang, Adam Jatowt, Masato Odagaki

Abstract: 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.