Search Results for author:
Found 5 papers, 0 papers with code
Transformer Choice Net: A Transformer Neural Network for Choice Prediction
Discrete-choice models, such as Multinomial Logit, Probit, or Mixed-Logit, are widely used in Marketing, Economics, and Operations Research: given a set of alternatives, the customer is modeled as choosing one of the alternatives to maximize a (latent) utility function.
A Neural Network Based Choice Model for Assortment Optimization
Discrete-choice models are used in economics, marketing and revenue management to predict customer purchase probabilities, say as a function of prices and other features of the offered assortment.
Deep Learning for Choice Modeling
Choice modeling has been a central topic in the study of individual preference or utility across many fields including economics, marketing, operations research, and psychology.
Learning to Sell a Focal-ancillary Combination
A number of products are sold in the following sequence: First a focal product is shown, and if the customer purchases, one or more ancillary products are displayed for purchase.
On Dynamic Pricing with Covariates
In this paper, we show that UCB and Thompson sampling-based pricing algorithms can achieve an $O(d\sqrt{T}\log T)$ regret upper bound without assuming any statistical structure on the covariates $x_t$.
