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Big-Five, MPTI, Eysenck or HEXACO: The Ideal Personality Model for Personality-aware Recommendation Systems
arXiv - CS - Computers and Society Pub Date : 2021-06-06 , DOI: arxiv-2106.03060
Sahraoui Dhelim, Liming Luke Chen, Nyothiri Aung, Wenyin Zhang, Huansheng Ning

Personality-aware recommendation systems have been proven to achieve high accuracy compared to conventional recommendation systems. In addition to that, personality-aware recommendation systems could help alleviate cold start and data sparsity problems. Most of the existing works use Big-Five personality model to represent the user's personality, this is due to the popularity of Big-Five model in the literature of psychology. However, from personality computing perspective, the choice of the most suitable personality model that satisfy the requirements of the recommendation application and the recommended content type still needs further investigation. In this paper, we study and compare four personality-aware recommendation systems based on different personality models, namely Big-Five, Eysenck and HEXACO from the personality traits theory, and Myers-Briggs Type Indicator (MPTI) from the personality types theory. Following that, we propose a hybrid personality model for recommendation that takes advantage of the personality traits models, as well as the personality types models. Through extensive experiments on recommendation dataset, we prove the efficiency of the proposed model, especially in cold start settings.

中文翻译:

Big-Five、MPTI、Eysenck 或 HEXACO:个性感知推荐系统的理想个性模型

与传统推荐系统相比,个性感知推荐系统已被证明具有较高的准确性。除此之外,个性感知推荐系统可以帮助缓解冷启动和数据稀疏问题。现有的作品大多使用Big-Five人格模型来表示用户的个性,这是由于Big-Five模型在心理学文献中的流行。然而,从个性计算的角度,选择最适合推荐应用和推荐内容类型的个性模型还有待进一步研究。在本文中,我们从人格特质理论研究和比较了四种基于不同人格模型的人格感知推荐系统,即 Big-Five、Eysenck 和 HEXACO,和 Myers-Briggs Type Indicator (MPTI) 来自人格类型理论。之后,我们提出了一种混合个性模型进行推荐,它利用了个性特征模型和个性类型模型。通过对推荐数据集的大量实验,我们证明了所提出模型的效率,尤其是在冷启动设置中。
更新日期:2021-06-08
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