Initial profile generation in recommender systems using pairwise comparison

Most recommender systems, such as collaborative filtering, cannot provide personalized recommendations until a user profile has been created. This is known as the new user cold-start problem. Several systems try to learn the new users' profiles as part of the sign up process by asking them to provide feedback regarding several items. We present a new, anytime preferences elicitation method that uses the idea of pairwise comparison between items. Our method uses a lazy decision tree, with pairwise comparisons at the decision nodes. Based on the user's response to a certain comparison, we select on-the-fly what pairwise comparison should next be asked. A comparative field study has been conducted to examine the suitability of the proposed method for eliciting the user's initial profile. The results indicate that the proposed pairwise approach provides more accurate recommendations than existing methods and requires less effort when signing up newcomers.