AWS-EP: A Multi-Task Prediction Approach for MBTI/Big5 Personality Tests

Fahed Elourajini, Esma Aïmeur
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引用次数: 1

Abstract

Personality and preferences are essential variables in computational sociology and social science. They describe differences between people at both individual and group levels. In recent years, automated approaches that detect personality traits have received much attention due to the massive availability of individuals' digital footprints. Furthermore, researchers have demonstrated a strong link between personality traits and various downstream tasks such as personalized filtering, profile categorization, and profile embedding. Therefore, the detection of individuals' preferences has become a critical process for improving the performance of different tasks. In this paper, we build on the importance of the individual's behaviour and propose a novel multitask modeling approach that understands and models the users' personalities based on their textual posts and comments within a multimedia framework. The novelties of our work compared to state-of-the-art personality prediction models are: improving the performance of the Big five-factor model (Big5) personality test using shared information from the Myers Briggs Type Indicator (MBTI) test, and proposing a one personality detection framework that accurately predicts both MBTI and Big5 tests simultaneously. Predicting both tests simultaneously improves the personality detection framework's flexibility to be used for different goals instead of being used only for a unique purpose (whether for the MBTI test or for the Big5 test separately). Experiments and results demonstrate that our solution outperforms state-of-the-art models across multiple famous personality datasets.
AWS-EP: MBTI/大五人格测试的多任务预测方法
个性和偏好是计算社会学和社会科学的基本变量。它们描述了个人和群体层面上人与人之间的差异。近年来,由于个人数字足迹的大量可用性,检测个性特征的自动化方法受到了广泛关注。此外,研究人员还证明了人格特质与个性化过滤、档案分类和档案嵌入等下游任务之间的密切联系。因此,个体偏好的检测已成为提高不同任务绩效的关键过程。在本文中,我们以个人行为的重要性为基础,提出了一种新的多任务建模方法,该方法基于用户在多媒体框架内的文本帖子和评论来理解和建模用户的个性。与最先进的人格预测模型相比,我们的工作的新颖之处在于:利用迈尔斯布里格斯类型指标(MBTI)测试的共享信息改进了大五因素模型(Big5)人格测试的性能,并提出了一个同时准确预测MBTI和Big5测试的单一人格检测框架。同时预测这两个测试提高了人格检测框架的灵活性,可以用于不同的目标,而不是只用于一个单一的目的(无论是MBTI测试还是Big5测试)。实验和结果表明,我们的解决方案在多个著名的个性数据集上优于最先进的模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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