Understanding how subsets of items are chosen from offered sets is critical to assortment planning, wireless network planning, and many other applications. There are two seemingly unrelated subset choice models that capture dependencies between items: intuitive and interpretable random utility models; and tractable determinantal point processes (DPPs). This paper connects the two. First, all DPPs are shown to be random utility models. Next, a determinantal choice model that enjoys the best of both worlds is specified; the model is shown to subsume logistic regression when dependence is minimal, and MNL when dependence is maximally negative. This makes the model interpretable, while retaining the tractability of DPPs. A simulation study verifies that the model can learn a continuum of negative dependencies from data, and an applied study using original experimental data produces novel insights on wireless interference in LoRa networks.

翻译：理解项目子集如何从备选集合中被选择，对于品类规划、无线网络规划及许多其他应用至关重要。存在两种看似无关的子集选择模型，能够捕捉项目之间的依赖关系：直观且可解释的随机效用模型，以及易于处理的行列式点过程（DPP）。本文建立了这两类模型之间的联系。首先，证明所有DPP均为随机效用模型。其次，提出一种兼具两者优势的行列式选择模型；该模型在依赖关系最小时可简化为逻辑回归，在依赖关系最大负相关时可简化为MNL。这使得模型既具备可解释性，又保留了DPP的可处理性。仿真研究表明，该模型能从数据中学习连续的负依赖关系；而基于原始实验数据的应用研究则对LoRa网络中的无线干扰产生了新见解。