<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>How to quickly and accurately retrieve the products that users are interested in from a huge amount of financial products has become a business pain point that financial institutions must solve. In this study, an interpretable EPRSA model for personalized financial service recommendations based on self-attention mechanisms is proposed by combining an LSTM model and an LDA topic model with AI technology support. A customized recommendation system for financial services is constructed by introducing the Nginx server into the Flask framework, and the design of the database and personalized recommendation module is interpreted. For the financial service personalized recommendation system proposed in this paper, its recommendation performance and system performance are tested, and the stock financial products are selected as the recommendation objects to explore its recommendation effect. It is found that the DNCG index of personalized recommendation of financial products of the EPRSA model is improved by 40.18%, the average response time of the system when the number of concurrent users is 1,000 is 1.96 s. The average quality of the personalized recommendation of the collection of stocks reaches 0.153. The customized recommendation of financial services using AI technology can select financial products based on the investor’s preference, help investors better understand the product returns, and improve the service quality of the financial industry.</p>
</abstract>

How to quickly and accurately retrieve the products that users are interested in from a huge amount of financial products has become a business pain point that financial institutions must solve. In this study, an interpretable EPRSA model for personalized financial service recommendations based on self-attention mechanisms is proposed by combining an LSTM model and an LDA topic model with AI technology support. A customized recommendation system for financial services is constructed by introducing the Nginx server into the Flask framework, and the design of the database and personalized recommendation module is interpreted. For the financial service personalized recommendation system proposed in this paper, its recommendation performance and system performance are tested, and the stock financial products are selected as the recommendation objects to explore its recommendation effect. It is found that the DNCG index of personalized recommendation of financial products of the EPRSA model is improved by 40.18%, the average response time of the system when the number of concurrent users is 1,000 is 1.96 s. The average quality of the personalized recommendation of the collection of stocks reaches 0.153. The customized recommendation of financial services using AI technology can select financial products based on the investor’s preference, help investors better understand the product returns, and improve the service quality of the financial industry.

Application of AI technology in personalized recommendation system for financial services

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

How to quickly and accurately retrieve the products that users are interested in from a huge amount of financial products has become a business pain point that...

Application of AI technology in personalized recommendation system for financial services

Online veröffentlicht: 03. Juni 2024

Seitenbereich: -

Eingereicht: 09. Feb. 2024

Akzeptiert: 15. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1349

Schlüsselwörter
LSTM model, Self-attention mechanism, Flask framework, Nginx server, Personalized recommendation, Financial services

© 2024 Xiaowen Yue, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Application of AI technology in personalized recommendation system for financial services

Online veröffentlicht: 03. Juni 2024

Seitenbereich: -

Eingereicht: 09. Feb. 2024

Akzeptiert: 15. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1349

SchlüsselwörterLSTM model, Self-attention mechanism, Flask framework, Nginx server, Personalized recommendation, Financial services

© 2024 Xiaowen Yue, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
LSTM model, Self-attention mechanism, Flask framework, Nginx server, Personalized recommendation, Financial services