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Assortment Optimization Under the Mallows model

Antoine Desir · Vineet Goyal · Srikanth Jagabathula · Danny Segev

Area 5+6+7+8 #121

Keywords: [ Ranking and Preference Learning ] [ (Application) Collaborative Filtering and Recommender Systems ] [ Combinatorial Optimization ]


We consider the assortment optimization problem when customer preferences follow a mixture of Mallows distributions. The assortment optimization problem focuses on determining the revenue/profit maximizing subset of products from a large universe of products; it is an important decision that is commonly faced by retailers in determining what to offer their customers. There are two key challenges: (a) the Mallows distribution lacks a closed-form expression (and requires summing an exponential number of terms) to compute the choice probability and, hence, the expected revenue/profit per customer; and (b) finding the best subset may require an exhaustive search. Our key contributions are an efficiently computable closed-form expression for the choice probability under the Mallows model and a compact mixed integer linear program (MIP) formulation for the assortment problem.

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