Personality-Based Recommendations: Evidence from Amazon.com

In this paper, we evaluate the accuracy of personality-based recommendations using a real-world data set from Amazon.com. We automatically infer the personality traits, needs, and values of users based on unstructured user-generated content in social media, rather than administering questionnaires or explicitly asking the users to self-report their characteristics. We find that personality characteristics significantly increase the performance of recommender systems, in general, while different personality models exhibit statistically significant differences in predictive performance.