Context-Aware Stock Recommendations with Stocks' Characteristics and Investors' Traits
Personalized stock recommendations aim to suggest stocks tailored to individual investor needs, significantly aiding the financial decision making of an investor. This study shows the advantages of incorporating context into personalized stock recommendation systems. We embed item contextual informa...
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| Veröffentlicht in: | IEICE Transactions on Information and Systems 2023/10/01, Vol.E106.D(10), pp.1732-1741 |
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| container_title | IEICE Transactions on Information and Systems |
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| creator | TAKAYANAGI, Takehiro IZUMI, Kiyoshi |
| description | Personalized stock recommendations aim to suggest stocks tailored to individual investor needs, significantly aiding the financial decision making of an investor. This study shows the advantages of incorporating context into personalized stock recommendation systems. We embed item contextual information such as technical indicators, fundamental factors, and business activities of individual stocks. Simultaneously, we consider user contextual information such as investors' personality traits, behavioral characteristics, and attributes to create a comprehensive investor profile. Our model incorporating contextual information, validated on novel stock recommendation tasks, demonstrated a notable improvement over baseline models when incorporating these contextual features. Consistent outperformance across various hyperparameters further underscores the robustness and utility of our model in integrating stocks' features and investors' traits into personalized stock recommendations. |
| doi_str_mv | 10.1587/transinf.2023EDP7017 |
| format | Article |
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| subjects | behavioral economics Context Customization graph neural network investor modeling natural language processing recommendation Recommender systems stock recommendation |
| title | Context-Aware Stock Recommendations with Stocks' Characteristics and Investors' Traits |
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