TRAINING A MACHINE TO DYNAMICALLY DETERMINE AND COMMUNICATE CUSTOMIZED, PRODUCT-DEPENDENT PROMOTIONS WITH NO OR LIMITED HISTORICAL DATA OVER A NETWORK

摘要

Training a machine to learn to offer personalized promotions over a network is provided. A promotion optimization engine may take logit models and their confidence measures, and compute the acceptance probability of each promotion based on the customer and product features. A target promotion may be determined based on an objective function, which jointly considers the acceptance probability and the logit model's confidence level. A cognitive engine receives a user response to the promotion and based on the user response, updates parameters of the logit model and confidence level associated with the logit model. In one aspect, a signal to offer the promotion is transmitted via a communication channel to a user's device, wherein the signal causes the user's device to automatically connect to one or more of the processors to receive the promotion, e.g., when the user's device is online.