An Interpretable Determinantal Choice Model for Subset Selection

• URL: http://arxiv.org/abs/2302.11477v1
• Date: Wed, 22 Feb 2023 16:26:38 GMT
• Title: An Interpretable Determinantal Choice Model for Subset Selection
• Authors: Sander Aarts and David B. Shmoys and Alex Coy
• Abstract summary: This paper connects two subset choice models: intuitive random utility models and tractable determinantal point processes. A determinantal choice model that enjoys the best of both worlds is specified. A simulation study verifies that the model can learn a continuum of negative dependencies from data, and an applied study produces novel insights on wireless interference in LoRa networks.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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