Preference-based search with adaptive recommendations

Abstract

Conversational recommenders can help users find their most preferred item among a large range of options, a task that we call preference-based search.

Motivated by studies in the field of behavioral decision theory, we take a user centric design perspective, focusing on the trade-off between decision accuracy and user effort. We consider example-critiquing, a methodology based on showing examples to the user and acquiring preferences in the form of critiques. In our approach critiques are volunteered in a mixed-initiative interaction. Some recommendations are suggestions specifically aimed at stimulating preference expression to acquire an accurate preference model.

We propose a method to adapt the suggestions according to observations of the user's behavior. We evaluate the decision accuracy of our approach with both simulations exploiting logs of previous users of the system (in order to see how adaptive suggestions improve the process of preference elicitation) and surveys with real users where we compare our approach of example critiquing with an interface based on question-answering.