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In this paper, we study the joint product sequencing and pricing problemfaced by many online retailers such as Amazon. We assume that consumers'purchase behavior can be explained by a ``consider-then-choose'' model: theyfirst form a consideration set by screening a subset of products sequentially,and then decide which product to purchase from their consideration set. Wepropose a \emph{cascade browse model} to capture the consumers' browsingbehavior, and use the Multinomial Logit (MNL) model as our choice model. Westudy two problems in this paper: in the first problem, we assume that eachproduct has a fixed revenue and preference weight, the goal is to identify thebest sequencing of products to offer so as to maximize the expected revenuesubject to a cardinality constraint. We propose a constant approximate solutionto this problem. As a byproduct, we propose the first fully polynomial-timeapproximation scheme (FPTAS) for the classic assortment optimization problemsubject to one capacity constraint and one cardinality constraint. In thesecond problem, we treat the price of each product as a decision variable andour objective is to jointly decide a sequence of product and their prices tomaximize the expected revenue. We propose a constant approximate solution tothis problem.

Product Sequencing and Pricing under Cascade Browse Model