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. While they have been shown to be expressive, capturing customer heterogeneity and behaviour, they are also hard to estimate, often based on many unobservables like utilities; and moreover, they still fail to capture many salient features of customer behaviour. A natural question then, given their success in other contexts, is if neural networks can eliminate the necessity of carefully building a context-dependent customer behaviour model and hand-coding and tuning the estimation. It is unclear however how one would incorporate assortment effects into such a neural network, and also how one would optimize the assortment with such a black-box generative model of choice probabilities. In this paper we investigate first whether a single neural network architecture can predict purchase probabilities for datasets from various contexts and generated under various models and assumptions. Next, we develop an assortment optimization formulation that is solvable by off-the-shelf integer programming solvers. We compare against a variety of benchmark discrete-choice models on simulated as well as real-world datasets, developing training tricks along the way to make the neural network prediction and subsequent optimization robust and comparable in performance to the alternates.

翻译：离散选择模型广泛应用于经济学、市场营销和收益管理领域，用于预测客户购买概率（例如作为价格及所提供品种其他特征的函数）。尽管这类模型已被证明具有表达力，能够捕捉客户异质性与行为特征，但其估计过程较为困难，且通常依赖于众多不可观测变量（如效用）；此外，这些模型仍无法捕捉客户行为的诸多显著特征。鉴于神经网络在其他领域的成功应用，一个自然的问题是：神经网络能否消除人工构建情境依赖的客户行为模型、手动编码及调整估计过程的必要性？然而，如何将品种效应融入此类神经网络，以及如何利用这种黑箱式选择概率生成模型优化品种组合，目前尚不明确。本文首先探究单一神经网络架构能否预测来自不同情境、基于多种模型与假设生成的数据集的购买概率。其次，我们开发了一种可利用现成整数规划求解器求解的品种优化模型。通过模拟数据集与真实数据集，我们将所提模型与多种基准离散选择模型进行对比，并在训练过程中开发了相关技巧，使神经网络预测及其后续优化具有鲁棒性，且性能与替代方案可比。