A survey on personality-aware recommendation systems

• DOI: 10.1007/s10462-021-10063-7
• Publication Date: 2021-09-18T23:04:45Z
• Authors: Sahraoui Dhelim; Nyothiri Aung; Mohammed Amine Bo...; Huansheng Ning; Erik Cambria
• Keywords: Social and Information Networks (cs.SI); FOS: Computer and information sciences; Computer Science - Computers and Society; Artificial Intelligence (cs.AI); Computer Science - Artificial Intelligence; Computers and Society (cs.CY); 0202 electrical engineering, electronic engineering, information engineering; Computer Science - Social and Information Networks; 02 engineering and technology; Information Retrieval (cs.IR); Computer Science - Information Retrieval

The final version is published in Artificial Intelligence Review (2021) https://link.springer.com/article/10.1007/s10462-021-10063-7<br/>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.<br/>