 In particular, we handle dynamic preferences and the choice overload phenomenon using a Markovian comparison model that is a generalization of the Markovian substitution framework of Blanchet et al. The Markovian comparison framework allows us to implicitly model the search cost in the choice process and thereby, modeling both dynamic preferences as well as the choice overload phenomenon. We consider the assortment optimization problem for the special case of our generalized Markov chain model where the underlying Markov chain is rank-1 (this is a generalization of the Multinomial Logit model). We show that the assortment optimization problem under this model is NP-hard and present a fully polynomial-time approximation scheme (FPTAS) for this problem .
<meaning-changed>  In particular, we handle dynamic preferences and the choice overload phenomenon using a Markovian comparison model that is a generalization of the Markovian substitution framework of Blanchet et al. The Markovian comparison framework allows us to implicitly model the search cost in the choice process and thereby, modeling both dynamic preferences as well as the choice overload phenomenon. We consider the assortment optimization problem for the special case of our generalized Markov chain model where the underlying Markov chain is rank-1 (this is a generalization of the Multinomial Logit model). We show that the assortment optimization problem under this model is NP-hard and present a fully polynomial-time approximation scheme (FPTAS) for this problem .
 In particular, we handle dynamic preferences and the choice overload phenomenon using a Markovian comparison model that is a generalization of MNL with no-purchase attractions dependent on the assortment S and strictly increasing with the size of assortment S. While we show that the assortment optimization problem under this model is NP-hard and present a fully polynomial-time approximation scheme (FPTAS) for this problem .