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<br> 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<br> audio features were extracted to describe the music files.<br> 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<br> personality questionnaire to only the most essential questions.<br> 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. <br> The usage of the data set requires citing the paper. <br> <br>论文《超越大五人格特质的音乐推荐系统研究》旨在探究以大五人格量表（Big Five Inventory，BFI）及其重要修订版BFI-2所表征的人格特质对音乐推荐误差的影响。BFI-2用于刻画大五人格特质的下位特质侧面。 本研究依托自研的音乐聆听与评分应用Music Master，面向279名受试者开展实验，同时采集用户的人格特征画像。此外，研究人员还提取了29维音频特征向量，用于表征音乐文件属性。 本研究利用该数据集，针对人格特质与音频特征对音乐推荐误差的影响提出多项假设并开展验证。实验结果表明，相较于使用下位特质侧面，仅采用大五人格特质的任意组合均会得到更差的推荐效果。此外，研究团队还发现了一小部分特质侧面组合，可实现最低的推荐误差。该发现可将人格问卷精简至仅保留最核心的题目。 该Excel文件包含为279名受试者生成的5278条数据条目。每条条目包含以5级李克特量表（5-point Likert scale）表述的音乐聆听偏好：其中涉及音乐认知维度的偏好记为Q1，涉及动机维度与人际维度的偏好分别记为Q2与Q3。后续20个变量（列）存储20维扩展大五人格特质的取值。最后29列则存储低阶音频特征的取值，其中包含从音频文件中提取的情绪特征。 该Excel文件可导出为CSV格式，通过合适的编程语言（如Python、R、Java、Matlab等）读取至内存以开展后续处理，例如构建用于协同过滤（collaborative filtering）的用户-物品矩阵，并基于本文提出的新型评分维度（动机维度与人际维度）评估推荐系统性能。 使用该数据集需引用本论文。