Incremental Critiquing

Conversational recommender systems guide users through a product space, alternatively making concrete product suggestions and eliciting the user’s feedback. Critiquing is a common form of user feedback, where users provide limited feedback at the feature-level by constraining a feature’s value-space. For example, a user may request a cheaper product, thus critiquing the price feature. Usually, when critiquing is used in conversational recommender systems, there is little or no attempt to monitor successive critiques within a given recommendation session. In our experience this can lead to inefficiencies, on the part of the recommender system, and confusion on the part of the user. In this paper we describe an approach to critiquing that attempts to consider a user’s critiquing history, as well as their current critique, when making new recommendations. We provide experimental evidence to show that this has the potential to significantly improve recommendation efficiency.