Estimation of Heterogeneity For Multinomial Probit Models Using Dictionary Learning
Empirical studies suggest that utility functions are often irregularly shaped, and individuals often deviate widely from each other. In this paper, we introduce a multinomial probit model involving both parametric covariates and nonparametric covariates. To combine heterogeneity across individuals with flexibility, we have two different strategies for the parametric component and the nonparametric component. For the parametric component, heterogeneity is incorporated by the inclusion of random effects. As for the nonparametric component, each individual has a unique individual-level function. However, all those nonparametric components share the same basis. Because the basis is unknown, we use dictionary learning to learn the basis. Additionally, an EM algorithm serves to estimate the multinomial probit models.
Click Prediction Using Deep Learning
We all have experienced real time bidding advertising. When we load a webpage with advertisements, they might be related to your google search history, or maybe your last amazon purchase. These advertisements are distributed by demand side platform companies, that try to determine if an advertisement is worth placing and how much they are willing to pay for it. One of these DSPs is iPinYou, who released a dataset of their advertisement transactions. The challenge is to determine based on the user information iPinYou receives, whether or not the user is going to click on a certain advertisement. This prediction process model is what I want to build with deep neural networks, and compare the results with traditional regression methods for this prediction problem.