Comparisons Instead of Ratings: Towards More Stable Preferences

Abstract

More and more personalization systems are emerging to reduce the information overload of the Web. As a result, it has become vital to model users' preferences accurately. Our focus lies in the quality of users' expressed preferences, in terms of reliability and stability through time. Today, users are often brought to express their preferences through ratings on a multi-point scale. However, several studies have highlighted problems with ratings. We propose a new preference modality whereby users compare items two-by two ("I prefer x to y").This initial work on comparisons shows that users are in favor of this new preference mechanism and that comparisons are almost 20% more stable over time than those conveyed through ratings, thus more reliable. These encouraging findings let us think that comparisons may lead to a better user modeling and an increase in the quality of personalization services, such as recommender systems.