A Survey on Personality-Aware Recommendation Systems

TL;DR

This survey reviews the development, design choices, and challenges of personality-aware recommendation systems, highlighting their ability to address cold start and data sparsity issues in personalized AI applications.

Contribution

It is the first comprehensive survey focusing on personality-aware recommendation systems, systematically classifying their methods and discussing key challenges.

Findings

Different personality modeling methods are used in recommendation systems.

Various recommendation techniques are adapted for personality-aware systems.

Common datasets and challenges are identified and discussed.

Abstract

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.