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Amidst the rapid development of financial technology, personalized financial product recommendation systems have emerged as a pivotal technology within the financial services sector (Behera et al., 2020). This system aims to offer users personalized recommendations of financial products by analyzing their financial behaviors, preferences, and needs, thereby enhancing user experience and the efficiency of financial services. However, due to the complexity of financial products and the diversity of users, constructing an accurate and efficient recommendation system remains a challenging problem.
To gain a comprehensive understanding of the current status and future development trends of financial product recommendation systems, this study will review pertinent literature addressing the authors’ research questions. It will analyze the strengths of these studies and assess the value they contribute to the research.
In the article by Garg and Singh (2018), the focus is on assessing the financial literacy level of the global youth population. By analyzing socio-economic and demographic factors, the authors identify their significant impact on the financial literacy level of the youth. Understanding the interrelationships between youth financial knowledge, attitudes, and behaviors provides a valuable theoretical foundation for the research.
Gomber et al. (2018) conducted a deep exploration of the technological innovation and processes within the financial services industry. By elucidating the forces driving the fintech revolution, the authors present the urgent need for the financial services industry to transform its business models, customer experiences, and services. This serves as inspiration for the research on innovative financial product recommendation systems.
In the article by Zhang et al. (2019), a comprehensive review of deep learning’s research progress in the field of recommendation systems is presented. Through categorizing deep learning models based on recommendation systems and summarizing the current technological landscape, it provides a clear direction for the authors’ selection of deep learning methods. This is enlightening for constructing a more efficient financial product recommendation system.
Naumov et al. (2019) focused on the development of deep learning recommendation models, particularly in the context of personalized and recommendation system tasks. By offering implementations and performance evaluations of deep learning recommendation models, they provide practical experience for the model selection, aiding in system design and performance optimization.
Portugal et al. (2018) systematically reviewed the application of machine learning algorithms in recommendation systems. By analyzing existing recommendation system categories, adopted machine learning methods, the use of big data technologies, and key performance metrics, it provides important references for the selection of machine learning algorithms in recommendation systems, helping in understanding the strengths and weaknesses of different algorithms.
Greenquist et al. (2019) introduced a prediction analytics-based online product recommendation framework. Through practical cases, it showcases the end-to-end process of building a complete recommendation system, providing a practical reference framework for the research, especially in the context of real-time recommendation services.
Additionally, from the perspective of information disclosure, Wang et al. (2023) proposed a text-based competitive network model. Through the analysis of textual data, the model reveals the impact of information disclosure on participants within a competitive environment, offering a more comprehensive perspective for the research.
While the aforementioned studies provide valuable insights, they exhibit certain limitations. First, some research may not have fully considered the specificity of financial products, thereby challenging the adaptability of recommendation systems in the financial domain. Second, existing studies might not have adequately addressed user privacy and security issues, which are particularly crucial in the financial sector. Lastly, some research may lack sufficient empirical studies, making it difficult to validate the effectiveness of their methods in real financial environments.