Afterward, we propose an enterprise conductive-risk encoder based on enterprise relational information from the enterprise knowledge graph for its conductive-risk embedding.
Stock Movement Prediction (SMP) aims at predicting listed companies' stock future price trend, which is a challenging task due to the volatile nature of financial markets.
To address this issue, we propose a novel Dual Hierarchical Attention Networks (DHAN) based on the bi-typed multi-relational heterogeneous graphs to learn comprehensive node representations with the intra-class and inter-class attention-based encoder under a hierarchical mechanism.
Spatial and temporal features are critical for demand forecasting in BSSs, but it is challenging to extract spatiotemporal dynamics.
Distributed linguistic representations are powerful tools for modelling the uncertainty and complexity of preference information in linguistic decision making.
To collectively forecast the demand for ride-sourcing services in all regions of a city, the deep learning approaches have been applied with commendable results.