Beyond the Big Five personality traits for music recommendation systems

Abstract

Abstract The aim of this paper is to investigate the influence of personality traits, characterized by the BFI (Big Five Inventory) and its significant revision called BFI-2, on music recommendation error. The BFI-2 describes the lower-order facets of the Big Five personality traits. We performed experiments with 279 participants, using an application (called Music Master) we developed for music listening and ranking, and for collecting personality profiles of the users. Additionally, 29-dimensional vectors of audio features were extracted to describe the music files. The data obtained from our experiments were used to test several hypotheses about the influence of personality traits and the audio features on music recommendation error. The performed analyses take into account three types of ratings that refer to the cognitive-emotional, motivational, and social components of the attitude towards the song. The experiments showed that every combination of Big Five personality traits produces worse results than using lower-order personality facets. Additionally, we found a small subset of personality facets that yielded the lowest recommendation error. This finding can condense the personality questionnaire to only the most essential questions. The collected data set is publicly available and ready to be used by other researchers.

Keywords

• Music recommendation systems
• Personality traits
• The Big Five Inventory-2
• Collaborative filtering