A Study on User Perception of Personality-Based Recommender Systems

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Abstract

Our previous research indicates that using personality quizzes is a viable and promising way to build user profiles to recommend entertainment products. Based on these findings, our current research further investigates the feasibility of using personality quizzes to build user profiles not only for an active user but also his or her friends. We first propose a general method that infers users’ music preferences in terms of their personalities. Our in-depth user studies show that while active users perceive the recommended items to be more accurate for their friends, they enjoy more using personality quiz based recommenders for finding items for themselves. Additionally, we explore if domain knowledge has an influence on users’ perception of the system. We found that novice users, who are less knowledgeable about music, generally appreciated more personality-based recommenders. Finally, we propose some design issues for recommender systems using personality quizzes.