Deep Correlation Mining Based on Hierarchical Hybrid Networks for Heterogeneous Big Data Recommendations
Xiaokang Zhou, Wei Liang, Kevin I‐Kai Wang, Laurence T. Yang
Abstract
The advancement of several significant technologies, such as artificial intelligence, cyber intelligence, and machine learning, has made big data penetrate not only into the industry and academic field but also our daily life along with a variety of cyber-enabled applications. In this article, we focus on a deep correlation mining method in heterogeneous big data environments. A hierarchical hybrid network (HHN) model is constructed to describe multitype relationships among different entities, and a series of measures are defined to quantify the internal correlations within one specific layer or external correlations between different layers. An intelligent router based on deep reinforcement learning framework is designed to generate optimal actions to route across the HHN. An improved random walk with the restart-based algorithm is then developed with the intelligent router, based on the hierarchical influence across network associated with multiple correlations. An intelligent recommendation mechanism is finally designed and applied to support users' collaboration works in scholarly big data environments. Experiments based on DBLP and ResearchGate data show the practicability and usefulness of our model and method.