Incorporating Domain-Specific Traits into Personality-Aware Recommendations for Financial Applications

• Published in: New generation computing Vol. 42; no. 4; pp. 635 - 649
• Main Authors: Takayanagi, Takehiro, Izumi, Kiyoshi
• Format: Journal Article
• Language: English
• Published: Tokyo Springer Japan 2024; Springer Nature B.V
• Subjects: Artificial Intelligence; Computer Hardware; Computer Science; Computer Systems Organization and Communication Networks; Personality; Personality traits; Recommender systems; Software Engineering/Programming and Operating Systems; Stock recommendation; Financial data mining; Collaborative filtering; Behavioral finance
• ISSN: 0288-3635; 1882-7055
• DOI: 10.1007/s00354-024-00241-w

The general personality traits, notably the Big-Five personality traits, have been increasingly integrated into recommendation systems. The personality-aware recommendations, which incorporate human personality into recommendation systems, have shown promising results in general recommendation areas including music, movie, and e-commerce recommendations. On the other hand, the number of research delving into the applicability of personality-aware recommendations in specialized domains such as finance and education remains limited. In addition, these domains have unique challenges in incorporating personality-aware recommendations as domain-specific psychological traits such as risk tolerance and behavioral biases play a crucial role in explaining user behavior in these domains. Addressing these challenges, this study addresses an in-depth exploration of personality-aware recommendations in the financial domain, specifically within the context of stock recommendations. First, this study investigates the benefits of deploying general personality traits in stock recommendations through the integration of personality-aware recommendations with user-based collaborative filtering approaches. Second, this study further verifies whether incorporating domain-specific psychological traits along with general personality traits enhances the performance of stock recommender systems. Thirdly, this paper introduces a personalized stock recommendation model that incorporates both general personality traits and domain-specific psychological traits as well as transaction data. The experimental results show that the proposed model outperformed baseline models in financial stock recommendations.