Critiquing is a common and a powerful form of feedback in conversational recommender systems. The goal of such systems is to retrieve the best product that satisfies the user preferences minimizing the session length. However, in some cases critiquing feedback may greatly lengthen a user navigation time. Although the user is unaware of her preferred target product, it is common that she has a particular preference over at least one of the product's features. In this case, the user iteratively makes a critique on that feature until reaching the preference. In this paper we propose a new recommendation algorithm that couple critiquing and direct selection of preferred products as feedback in a conversational recommender. Additionally, we describe how the user model is updated and we report on a simulator and a user study its advantage for reducing session length.
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