Tell me what you Like: introducing natural language preference elicitation strategies in a virtual assistant for the movie domain

Abstract

Preference elicitation is a crucial step for every recommendation algorithm. In this paper, we present a strategy that allows users to express their preferences and needs through natural language statements. In particular, our natural language preference elicitation pipeline allows users to express preferences on objective movie features (e.g., actors, directors, etc.) as well as on subjective features that are collected by mining user-written movie reviews. To validate our claims, we carried out a user study in the movie domain (N=114). The main finding of our experiment is that users tend to express their preferences by using objective features, whose usage largely overcomes that of subjective features, which are more complicated to be expressed. However, when the users are able to express their preferences also in terms of subjective features, they obtain better recommendations in a lower number of conversation turns. We have also identified the main challenges that arise when users talk to the virtual assistant by using subjective features, and this paves the way for future developments of our methodology.