人工知能学会

The use of general personality traits, specifically the Big-Five personality traits, in recommendation systems has been widely explored and adopted in various fields such as music, film, and literature. However, research on personality-aware recommendations in specific domains, such as finance and education, where domain-specific psychological traits such as risk tolerance and behavioral biases play a crucial role in explaining user behavior, remains limited.
To bridge this gap, this study investigates the effectiveness of personality-aware recommendations in financial stock recommendation tasks. Firstly, the paper demonstrates the utility of general personality traits in financial stock recommendations. Secondly, this paper shows that incorporating domain-specific psychological traits along with general personality traits enhances the performance of the recommendation system. Thirdly, we propose a personalized stock recommendation model that incorporates both general personality traits and domain-specific psychological traits as well as interaction data, resulting in superior performance compared to baseline models.