Collaborative Driving Mode of Sustainable Marketing and Supply Chain Management Supported by Metaverse Technology

Corporate financial risk prediction is a critical task for ensuring the stability and success of businesses in today's dynamic economic landscape. However, existing models often fall short in accurately assessing and managing these risks. They often rely on historical financial data alone, which fails to account for sudden market fluctuations or unforeseen external events, leading to suboptimal risk assessments. Recognizing the paramount importance of time series analysis in financial risk prediction, we introduce a novel approach to the ABC-Attention-GRU combination model. This innovative model leverages the strengths of Artificial Bee Colony (ABC), the attention mechanism, and Gated Recurrent Unit (GRU) to enhance predictive accuracy and robustness. In our experiments, the ABC-Attention-GRU model consistently outperformed state-of-the-art methods across various financial datasets. It effectively captured complex temporal dependencies, resulting in superior Precision, Recall, F1 Score, and AUC metrics.

Section: Overview Of Our Networkmentioning

confidence: 99%

Predicting Corporate Financial Risk Using Artificial Bee Colony-Attention-Gated Recurrent Unit Model

Huang,

Bi,

Chang

et al. 2024

“…This review provides a comprehensive understanding of different recommendation system classifications, adopted methods, and performance evaluations. In the study (Zhang, Yao, Sun, & Tay, 2019), deep learning has demonstrated outstanding performance in various fields, and this literature focuses on the application of deep learning in recommendation systems. The paper provides a classification system for deep learning in recommendation systems and offers a comprehensive summary of existing research.…”

Section: Relevant Workmentioning

confidence: 99%

Deep Learning-Driven E-Commerce Marketing Communication for Recommending Shopping System and Optimizing User Experience

Liu,

Tang,

et al. 2024

As competition in the realm of e-commerce escalates, the provision of personalized and precise shopping recommendations emerges as a pivotal strategy for e-commerce platforms striving to engage users effectively. Traditional recommendation systems often grapple with challenges such as the inability to capture intricate relationships, limited personalization, and issues concerning diversity. In response to these challenges, this study introduces cutting-edge deep learning techniques, namely Transformer models, Generative Adversarial Networks (GANs), and reinforcement learning, with the aim of bolstering the recommendation accuracy and user experience within e-commerce shopping systems.Initially, we harness Transformer models, capitalizing on their exceptional performance in processing sequential data to adeptly extract and learn representations of both product and user features. This facilitates a more profound understanding of the correlations between products and user shopping behaviors, thus empowering the system to offer more tailored recommendations.

“…This dataset holds significant value for this study, allowing the authors to explore product recommendation issues, analyze user ratings and reviews, build effective recommendation models, evaluate recommendation performance, and propose improvement strategies (Zhong & Zhao, 2024). It enables a multi-faceted examination of the problem, incorporating approaches like rating-based collaborative filtering, sentiment analysis based on reviews, content-based filtering, and deep learning methods.…”

Section: Amazon-ratings (Beauty) Datasetmentioning

confidence: 99%

Deep Learning-Powered Financial Product Recommendation System in Banks

Li,

Song

2024

With the rapid evolution of financial technology, the recommendation system for financial products, as a crucial technology to enhance user experience and reduce information search costs, is increasingly becoming the focus of the financial services sector. As market competition intensifies, the diversity of user demands, coupled with the continuous expansion of financial product types, has exposed limitations in traditional recommendation systems regarding accuracy and personalized services. Therefore, this study aims to explore the application of deep learning technology in the field of financial product recommendations, aiming to construct a more intelligent and precise financial product recommendation system. The metrics we focus on include precision, recall, and F1-score, comprehensively evaluating the effectiveness of the proposed methods. In terms of methodology, we first employ a Transformer model, leveraging its powerful self-attention mechanism to capture the complex relationships between user behavior sequences and financial product information.