Core Concepts
The authors propose efficient algorithms for assortment optimization without unrealistic constraints, providing practical and optimal solutions.
Abstract
The content discusses the development of efficient algorithms for assortment optimization without the need for a 'No-Choice' item. The proposed algorithms address limitations in existing methods and offer practical and provably optimal solutions. Empirical evaluations confirm the superior performance of the new algorithms.
The problem of active online assortment optimization with preference feedback is explored, highlighting the importance of relative feedback over absolute ratings. The framework is applicable to various real-world scenarios such as ad placement, recommender systems, and online retail.
Existing literature on assortment optimization is reviewed, pointing out limitations in algorithm design that require repetitive selection of the same items. The proposed algorithms aim to overcome these limitations by introducing novel concentration guarantees and adaptive pivot selection.
Key contributions include a general AOA setup for PL models, practical algorithm designs, and empirical evaluations showcasing improved performance. The content also discusses future research directions to extend the findings to other choice models beyond PL.
Stats
Regtop T = O(θ3/2 max √KT log T)
Regwtd T = O(√θmaxKT log T)
Quotes
"We designed efficient algorithms for the problem of regret minimization in assortment selection with Plackett Luce (PL) based user choices."
"Our methods are practical, provably optimal, and devoid of the aforementioned limitations of the existing methods."