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 nd that personality characteristics signicantly increase the performance of recommender systems, in general, while dierent personality models exhibit statistically signicant dierences in predictive performance.
['Panagiotis Adamopoulos', 'Vilma Todri']
Personality-Based Recommendations: Evidence from Amazon.com