Joint Assortment Optimization and Customization Under a Mixture of Multinomial Logit Models: On the Value of Personalized Assortments

Housni, Omar El; Topaloğlu, Hüseyin

doi:10.1287/opre.2022.2384

Cited by 20 publications

(1 citation statement)

References 41 publications

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“…In this section, we supplement our analysis with an additional experiment using real data provided by Expedia, which was part of a Kaggle competition called Kaggle (2013). This dataset is commonly utilized in various research papers (Bai et al, 2023; El Housni & Topaloglu, 2023; Gao et al, 2021; Gui, 2023; Liu et al, 2023; Ursu, 2018 etc.). The key distinction between the Expedia dataset and the airline dataset is that the Expedia dataset includes information about nonpurchase assortments.…”

Section: Expedia Dataset Resultsmentioning

confidence: 99%

Transformer‐based choice model: A tool for assortment optimization evaluation

Peng,

Rong,

Zhu

2024

Naval Research Logistics

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Assessing the efficacy of algorithms plays a pivotal role in advancing various fields, both in theory and practice. Unlike the predictive models, due to the intricate relationship between decisions and the underlying data‐generating processes, the evaluation of decision algorithms cannot directly rely on real data. Hence, a simulator becomes indispensable for appraising decision algorithm effectiveness. In this paper, we aim to leverage assortment decisions, a widely used application in revenue management, to illustrate the utilization of a machine learning‐based simulation. The process can be summarised as: we utilize the modified Transformer‐based choice model, acting as a simulator, to generate a synthetic dataset that mimics consumer purchasing behavior. After training the MNL, DeepFM, and DeepFM‐a models, all of which can swiftly provide assortment decisions in real‐time, we utilize the simulator to evaluate the revenue generated by each assortment prescribed by different choice models. This approach mitigates the challenge of validating decision models that alter real‐world observed data. To show the benefit of such a simulation approach, we have conducted various numerical studies. These studies aim to examine the impact of outside option attractiveness, data size, the number of features, and cardinality. Admittedly, due to the close alignment between the simulator and complex consumer purchase choice datasets, some numerical observations may be challenging to explain. Nevertheless, by employing the simulator, we are able to contrast the differences between the MNL and DeepFM/DeepFM‐a models, shedding light on their respective model misspecifications.

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Section: Expedia Dataset Resultsmentioning

confidence: 99%

Transformer‐based choice model: A tool for assortment optimization evaluation

Peng,

Rong,

Zhu

2024

Naval Research Logistics

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Assortment Optimization with Visibility Constraints

Barré,

Housni,

Lodi

2024

Lecture Notes in Computer Science

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Assortment optimization: a systematic literature review

Heger,

Klein

2024

OR Spectrum

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Assortment optimization is a core topic of demand management that finds application in a broad set of different areas including retail, airline, hotel, and transportation industries as well as in the healthcare sector. Hence, the interest in research on assortment optimization has grown rapidly in recent years. However, the sheer number of publications on the topic of assortment optimization makes it difficult to keep track of all available approaches proposed in the literature. In this paper, we systematically review state-of-the-art studies on assortment optimization. We assemble an extensive literature overview by strategically searching for pre-defined keywords within leading scientific databases. The resulting literature is grouped by a proposed taxonomy that captures properties related to the optimization problem itself, the modelled customer behaviour, and the solution concept applied for solving the problem at hand. For each group, we provide an overview of the corresponding literature and analyse it based on a proposed selection of key factors.

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Joint Assortment Optimization and Customization Under a Mixture of Multinomial Logit Models: On the Value of Personalized Assortments

Cited by 20 publications

References 41 publications

Transformer‐based choice model: A tool for assortment optimization evaluation

Transformer‐based choice model: A tool for assortment optimization evaluation

Assortment Optimization with Visibility Constraints

Assortment optimization: a systematic literature review

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