Comparisons Instead of Ratings: Towards More Stable Preferences

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.