The insight of this work enlightens the notion of dense feature model design for KGC which is a new alternative to Deep Neural networks (DNN) in this task or even a better choice.
For this model, a tensor factorization based method is applied and the corresponding convergence anlysis is established.
Optimization and Control
Based on these testing data, a response model is then built to measure the heterogeneous treatment effect of increasing credit limits (i. e. treatments) for different customers, who are depicted by several control variables (i. e. features).
If the uncertainty of an enterprise's revenue forecasting can be estimated, a more proper credit limit can be granted.
Considering the above challenges and the special scenario in Ant Financial, we try to incorporate default prediction with network information to alleviate the cold-start problem.
Additionally, among the network, only very few of the users are labelled, which also poses a great challenge for only utilizing labeled data to achieve a satisfied performance on fraud detection.