Multimodal Sentiment Analysis of Online Product Information Based on Text Mining Under the Influence of Social Media

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.

Section: Conclusion and Discussionmentioning

confidence: 76%

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

“…Attention mechanism automatically identifies and focuses on critical financial information within the data, facilitating the extraction of important temporal and spatial features. It enhances the model's understanding and modeling capabilities, allowing for more accurate capture of correlations and trends (Zeng & Zhong, 2022).…”

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

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.

“…This analysis helps financial institutions better understand customers' credit behavior, develop more precise credit strategies, and improve the efficiency of risk management. At the same time, time series analysis also provides an important data mining tool for the field of credit evaluation, which is expected to improve the performance and prediction capabilities of credit evaluation models (Zhao & Chen, 2022;Zeng & Zhong, 2022).…”

Section: Research On Time Series Analysis In Credit Evaluationmentioning

confidence: 99%

Application of AdaBound-Optimized XGBoost-LSTM Model for Consumer Credit Assessment in Banking Industries

Fan,

Wang,

2024

Consumer credit assessment has always been a crucial concern in the financial industry. It involves evaluating an individual's credit history and their ability to repay loans, playing a pivotal role in the risk management and lending decisions made by credit institutions. In the present landscape, traditional credit assessment methods confront various shortcomings. Firstly, they typically only consider static features and are unable to capture the dynamic changes in an individual's credit profile over time. Secondly, traditional methods struggle with processing complex time series data, failing to fully exploit the importance of time-related information. To address these challenges, we propose an innovative solution – the XGBoost-LSTM model optimized with the AdaBound algorithm. This hybrid model combines two powerful machine learning techniques, XGBoost and LSTM, to leverage both static and dynamic features effectively.