IEICE TRANSACTIONS on Information
Context-Aware Stock Recommendations with Stocks' Characteristics and Investors' Traits
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E106-D No.10 pp.1732-1741
- Publication Date
- 2023/10/01
- Publicized
- 2023/07/20
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2023EDP7017
- Type of Manuscript
- PAPER
- Category
- Natural Language Processing
Authors
Takehiro TAKAYANAGI
The University of Tokyo
Kiyoshi IZUMI
The University of Tokyo
Keyword
Latest Issue
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Takehiro TAKAYANAGI, Kiyoshi IZUMI, "Context-Aware Stock Recommendations with Stocks' Characteristics and Investors' Traits" in IEICE TRANSACTIONS on Information,
vol. E106-D, no. 10, pp. 1732-1741, October 2023, doi: 10.1587/transinf.2023EDP7017.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2023EDP7017/_p
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@ARTICLE{e106-d_10_1732,
author={Takehiro TAKAYANAGI, Kiyoshi IZUMI, },
journal={IEICE TRANSACTIONS on Information},
title={Context-Aware Stock Recommendations with Stocks' Characteristics and Investors' Traits},
year={2023},
volume={E106-D},
number={10},
pages={1732-1741},
abstract={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.},
keywords={},
doi={10.1587/transinf.2023EDP7017},
ISSN={1745-1361},
month={October},}
Copy
TY - JOUR
TI - Context-Aware Stock Recommendations with Stocks' Characteristics and Investors' Traits
T2 - IEICE TRANSACTIONS on Information
SP - 1732
EP - 1741
AU - Takehiro TAKAYANAGI
AU - Kiyoshi IZUMI
PY - 2023
DO - 10.1587/transinf.2023EDP7017
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E106-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2023
AB - 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.
ER -
English
