Making the Most of Preference Feedback by Modeling Feature Dependencies

Abstract

Conversational recommender systems help users navigate through the product space by exploiting feedback. In conversational systems based on preference-based feedback, the user selects the most preferred item from a list of recommended products. Modelling user's preferences then becomes important in order to recommend relevant items. Several existing recommender systems accomplish this by assuming the features to be independent. Here we will attempt to forego this assumption and exploit the dependencies between the features to build a robust user preference model.