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. Categories and Subject Descriptors H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval - Information filtering Keywords Recommendations; Personality traits; Big Five; Values; Needs

• Type: Journal
• Year: 2015
• Where: RECSYS
• Authors: Panagiotis Adamopoulos, Vilma Todri
• Keywords: Control Systems, RECSYS 2015