HG-PerCon: Cross-view contrastive learning for personality prediction.
Neural Netw
; 169: 542-554, 2024 Jan.
Article
em En
| MEDLINE
| ID: mdl-37952390
ABSTRACT
Personality prediction task not only helps us to better understand personal needs and preferences but also is essential for many fields such as psychology and behavioral economics. Current personality prediction primarily focuses on discovering personality traits through user posts. Additionally, there are also methods that utilize psychological information to uncover certain underlying personality traits. Although significant progress has been made in personality prediction, we believe that current solutions still overlook the long-term sustainability of personality and are constrained by the challenge of capturing consistent personality-related clues across different views in a simple and efficient manner. To this end, we propose HG-PerCon, which utilizes user representations based on historical semantic information and psychological knowledge for cross-view contrastive learning. Specifically, we design a transformer-based module to obtain user representations with long-lasting personality-related information from their historical posts. We leverage a psychological knowledge graph which incorporates language styles to generate user representations guided by psychological knowledge. Additionally, we employ contrastive learning to capture the consistency of user personality-related clues across views. To evaluate the effectiveness of our model, and our approach achieved a reduction of 2%, 4%, and 6% in RMSE compared to the second-best baseline method.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Personalidade
/
Aprendizagem
Idioma:
En
Revista:
Neural Netw
Ano de publicação:
2024
Tipo de documento:
Article