A Survey on Personality-Aware Recommendation Systems

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 personalityaware 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.

FFM-SVD: A Novel Approach for Personality-aware Recommender Systems

“…This study used the Linguistic Inquiry and Word Count (LIWC) dictionary text analysis method. LIWC is a form of text-based automatic personality recognition (APR) which has achieved an acceptable accuracy that is generally higher than multimedia or behaviour-based APRs [20]. LIWC's sensitivity to identifying emotion expression has been validated in numerous studies, e.g., [21], and the confidence in the results produced by LIWC is high.…”

“…Alternatively, personality data can be obtained explicitly via questionnaire-based instruments. These approaches have the potential to be more accurate than APR methods [20], however, APRs are easier to apply to existing data. They also do not induce the psychological factors (e.g., social desirability bias [22]) associated with explicit personality acquisition methods.…”

“…Preference-and personality-based user similarities for a collaborative filtering recommender were studied in [27]. However, a comparison of personality-aware RSs across domains was seldom addressed, as in, e.g., the related work review by [20] and [28]. Moreover, very few studies have implemented a multi-domain approach and compared the effectiveness of personality inclusion in each.…”