An Interpretable Determinantal Choice Model for Subset Selection
This work addresses subset choice modeling for applications like assortment planning and wireless networks, offering an incremental improvement by bridging two existing model types.
The paper connects random utility models and determinantal point processes (DPPs) to create an interpretable determinantal choice model for subset selection, which subsumes logistic regression and MNL models, and demonstrates its ability to learn negative dependencies from data and provide insights on wireless interference in LoRa networks.
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.