With the emergence of personality computing as a new research field related to artificial intelligence and personality psychology, we have witnessed an unprecedented proliferation of personality-aware recommendation systems. Unlike conventional recommendation systems, these new systems solve traditional problems such as the cold start and data sparsity problems. This survey aims to study and systematically classify personality-aware recommendation systems. To the best of our knowledge, this survey is the first that focuses on personality-aware recommendation systems. We explore the different design choices of personality-aware recommendation systems, by comparing their personality modeling methods, as well as their recommendation techniques. Furthermore, we present the commonly used datasets and point out some of the challenges of personality-aware recommendation systems.
翻译:随着人格计算作为一个与人工智能和人格心理学有关的新的研究领域的出现,我们目睹了个性意识推荐系统空前的激增。与传统的推荐系统不同,这些新系统解决了诸如寒冷开始和数据宽度问题等传统问题。这项调查旨在研究和系统地分类个性意识推荐系统。据我们所知,这项调查是第一个侧重于个性意识推荐系统的调查。我们通过比较个性模型方法及其推荐技术,探索个性意识推荐系统的不同设计选择。此外,我们介绍了常用的数据集,并指出了个性意识推荐系统的一些挑战。