  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.
