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.
In our approach, a semi-black-box model is built to forecast the dynamic market response and an efficient optimization method is proposed to solve the complex allocation task.
By leveraging the mixture layer, the proposed method can adaptively update states according to the similarities between encoded inputs and prototype vectors, leading to a stronger capacity in assimilating sequences with multiple patterns.
Many machine intelligence techniques are developed in E-commerce and one of the most essential components is the representation of IDs, including user ID, item ID, product ID, store ID, brand ID, category ID etc.
Dynamic and personalized elements such as top stories, recommended list in a webpage are vital to the understanding of the dynamic nature of web 2. 0 sites.
To overcome the limitations of existing methods, we propose a novel approach in this paper to learn effective features automatically from the structured data using the Convolutional Neural Network (CNN).
In order to answer these kinds of questions, we attempt to model human sense of style compatibility in this paper.