86 papers with code • 1 benchmarks • 1 datasets
Many scientific and engineering challenges -- ranging from personalized medicine to customized marketing recommendations -- require an understanding of treatment effect heterogeneity.
The deluge of digital information in our daily life -- from user-generated content, such as microblogs and scientific papers, to online business, such as viral marketing and advertising -- offers unprecedented opportunities to explore and exploit the trajectories and structures of the evolution of information cascades.
Besides, MetaHeac has been successfully deployed in WeChat for the promotion of both contents and advertisements, leading to great improvement in the quality of marketing.
Probabilistic graphical models combine the graph theory and probability theory to give a multivariate statistical modeling.
Maximum likelihood estimation of a finite mixture of logistic regression models in a continuous data stream
In marketing we are often confronted with a continuous stream of responses to marketing messages.
Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform
This paper describes the approach to extend, evaluate, and implement the mRMR feature selection methods for classification problem in a marketing machine learning platform at Uber that automates creation and deployment of targeting and personalization models at scale.
This study provides a formal analysis of the customer targeting problem when the cost for a marketing action depends on the customer response and proposes a framework to estimate the decision variables for campaign profit optimization.