  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.
