A Survey on Personality-Aware Recommendation Systems
It provides a foundational overview for researchers and practitioners in AI and recommendation systems, but it is incremental as a survey paper.
This survey tackles the problem of systematically classifying and analyzing personality-aware recommendation systems, which address issues like cold start and data sparsity, by exploring design choices, personality modeling methods, and recommendation techniques.
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.