Skip to main content

Please enter a keyword and click the arrow to search the site

Display Optimization for Vertically Differentiated Locations Under Multinomial Logit Preferences

Publishing details

Social Sciences Research Network

Publication Year



We introduce a new optimization model, dubbed the display optimization problem, that captures a common aspect of choice behavior, known as the framing bias. In this setting, the objective is to optimize how distinct items (corresponding to products, web links, ads, etc.) are being displayed to a heterogeneous audience, whose choice preferences are influenced by the relative locations of items. Once items are assigned to vertically differentiated locations, customers consider a subset of the items displayed in the most favorable locations, before picking an alternative through Multinomial Logit choice probabilities. The main contribution of this paper is to derive a polynomial-time approximation scheme for the display optimization problem. Our algorithm is based on an approximate dynamic programming formulation that exploits various structural properties to derive a compact state space representation of provably near-optimal item-to-position assignment decisions. As a by-product, our results improve on existing constant-factor approximations for closely-related models, and apply to general distributions over consideration sets. We develop the notion of approximate assortments, that may be of independent interest and applicable in additional revenue management settings. Lastly, we conduct extensive numerical studies to validate the proposed modeling approach and algorithm. Experiments on a public hotel booking data set demonstrate the superior predictive accuracy of our choice model vis-a-vis the Multinomial Logit choice model with location bias, proposed in earlier literature. In synthetic computational experiments, our approximation scheme dominates various benchmarks, including natural heuristics -- greedy methods, local-search, priority rules -- as well as state-of-the-art algorithms developed for closely-related models.


Choice Models; Display Optimization; Approximation Schemes; Revenue Management


Social Sciences Research Network

Available on ECCH


Select up to 4 programmes to compare

Select one more to compare
subscribe_image_desktop 5949B9BFE33243D782D1C7A17E3345D0

Sign up to receive our latest news and business thinking direct to your inbox