Personality-Based User Modeling for Music Recommender Systems

• Published in: Machine Learning and Knowledge Discovery in Databases Vol. 9853; pp. 254 - 257
• Main Authors: Ferwerda, Bruce, Schedl, Markus
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2016; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Music recommender systems; Personalization
• ISBN: 9783319461304; 3319461303
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-46131-1_29

Applications are getting increasingly interconnected. Al-though the interconnectedness provide new ways to gather information about the user, not all user information is ready to be directly implemented in order to provide a personalized experience to the user. Therefore, a general model is needed to which users’ behavior, preferences, and needs can be connected to. In this paper we present our works on a personality-based music recommender system in which we use users’ personality traits as a general model. We identified relationships between users’ personality and their behavior, preferences, and needs, and also investigated different ways to infer users’ personality traits from user-generated data of social networking sites (i.e., Facebook, Twitter, and Instagram). Our work contributes to new ways to mine and infer personality-based user models, and show how these models can be implemented in a music recommender system to positively contribute to the user experience.