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