Personalization enables businesses to learn customer preferences from past interactions and thus to target individual customers with more relevant content. We consider the problem of predicting the optimal promotional offer for a given customer out of several options as a contextual bandit problem. Identifying information for the customer and/or the campaign can be used to deduce unknown customer/campaign features that improve optimal offer prediction. Using a generated synthetic email promo dataset, we demonstrate similar prediction accuracies for (a) a wide and deep network that takes identifying information (or other categorical features) as input to the wide part and (b) a deep-only neural network that includes embeddings of categorical features in the input. Improvements in accuracy from including categorical features depends on the variability of the unknown numerical features for each category. We also show that selecting options using upper confidence bound or Thompson sampling, approximated via Monte Carlo dropout layers in the wide and deep models, slightly improves model performance.

翻译：个人化使企业能够从过去的互动中了解客户的偏好,从而以内容更为相关的个人客户为目标。我们认为,从几种选择中预测对特定客户的最佳促销报价是一个背景强盗问题。为客户和(或)运动确定信息可以用来推断出未知的客户/运动特征,从而改进最佳出价预测。我们利用生成的合成电子邮件促销数据集,展示了类似的预测宽广和深厚的网络,将识别信息(或其他绝对特征)作为输入宽广部分的输入;以及(b) 包含输入中绝对特征的深层神经网络。纳入绝对特征的准确性取决于每一类别未知数字特征的变异性。我们还表明,利用高置信任度或汤普森抽样选择选项,在宽广和深度模型中以蒙特卡洛辍学层为近似点,略微改进模型性能。