Personalized Stock Recommendation with Investors' Attention and Contextual Information

Takehiro Takayanagi, Kiyoshi Izumi, Atsuo Kato, Naoyuki Tsunedomi, Yukina Abe

Abstract: The personalized stock recommendation is a task to recommend suitable stocks for each investor. The personalized recommendations are valuable, especially in investment decision making as the objective of building a portfolio varies by each retail investor. In this paper, we propose a Personalized Stock Recommendation with Investors' Attention and Contextual Information (PSRIC). PSRIC aims to incorporate investors' financial decision-making process into a stock recommendation, and it consists of an investor modeling module and a context module. The investor modeling module models the investor's attention toward various stock information. The context module incorporates stock dynamics and investor profiles. The result shows that the proposed model outperforms the baseline models and verifies the usefulness of both modules in ablation studies.