Beyond the Big Five Personality Traits for Music Recommendation Systems - dataset
The aim of the paper "Beyond the Big Five Personality Traits for Music Recommendation Systems" 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.
In our paper, we used this data set to test several hypotheses about the influence of personality traits and the audio features on music recommendation error. The experiments have 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 allows condensing the
personality questionnaire to only the most essential questions.
The EXCEL file contains 5278 entries created for 279 participants. Each entry includes the preferences (expressed using the 5-point Likert scale) that refer to listening to music's cognitive aspect are denoted as Q1. The motivational and interpersonal aspects are denoted as Q2 and Q3, respectively. The following 20 variables (columns) contain 20 dimensional, extended Big Five personality traits values. The last 29 columns contain the values of low-level audio features, including emotions extracted from the audio files. The EXCEL file is ready to be saved in CSV and imported into memory using a suitable programming language (e.g. Python, R, Java, Matlab and others) for further processing, i.e. for creating user-item matrixes for collaborating filtering and evaluating its performance with the usage of proposed new rating types (motivational and interpersonal ones) described the article.
The usage of the data set requires citing the paper.
