Harnessing Behavioral Traits to Enhance Financial Stock Recommender Systems: Tackling the User Cold Start Problem

Recommender systems often struggle with the user cold start problem, which arises when there is a lack of interaction data for new users. This issue is particularly important in financial stock recommendations, as novice investors often lack investment experience and require personalized advice more than experienced users. Behavioral finance offers valuable insights into investor preferences and highlights the impact of psychological factors on investor behavior. In this paper, we present a novel framework that integrates behavioral finance with financial stock recommendations to effectively tackle the user cold start problem. To that end, we first conduct a survey involving 964 Japanese individual investors to gather investment-related behavioral traits while collecting their transaction data in a trading platform. Then, we introduce the Investor Risk-tolerance Aware DropoutNet (IRAD) and show its improved performance over baseline models, demonstrating its effectiveness for stock recommendations in cold start settings. Finally, we offer an example of how incorporating investors’ behavioral traits can result in more interpretable stock recommendations.