A Market Discovery Algorithm to Estimate a General Class of Nonparametric Choice Models
提出一种仅用销售交易和产品可得性数据估计顾客偏好的方法，结合非参数离散选择模型与伯努利到达过程，通过市场发现算法自动生成顾客类型，在酒店业数据上将预测误差降低67%至93%。
We propose an approach for estimating customer preferences for a set of substitutable products using only sales transactions and product availability data. The underlying demand framework combines a general, nonparametric discrete choice model with a Bernoulli process of arrivals over time. The choice model is defined by a discrete probability mass function (pmf) on a set of possible preference rankings of alternatives, and it is compatible with any random utility model. An arriving customer is assumed to purchase the available option that ranks highest in her preference list. The problem we address is how to jointly estimate the arrival rate and the pmf of the rank-based choice model under a maximum likelihood criterion. Since the potential number of customer types is factorial, we propose a market discovery algorithm that starts with a parsimonious set of types and enlarge it by automatically generating new types that increase the likelihood value. Numerical experiments confirm the potential of our proposal. For a realistic data set in the hospitality industry, our approach improves the root mean square errors between predicted and observed purchases computed under independent demand model estimates by 67% to 93%. This paper was accepted by Serguei Netessine, operations management.