Summary of Risk Analysis in Customer Relationship Management Via Quantile Region Convolutional Neural Network-long Short-term Memory and Cross-attention Mechanism, by Yaowen Huang and Jun Der Leu and Baoli Lu and Yan Zhou
Risk Analysis in Customer Relationship Management via Quantile Region Convolutional Neural Network-Long Short-Term Memory and Cross-Attention Mechanism
by Yaowen Huang, Jun Der Leu, Baoli Lu, Yan Zhou
First submitted to arxiv on: 22 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to enhance risk analysis in customer relationship management (CRM) by combining the strengths of quantile region convolutional neural network-long short-term memory (QRCNN-LSTM) and cross-attention mechanisms. The QRCNN-LSTM model leverages sequence modeling and deep learning architectures to capture local and global dependencies in sequence data, while the cross-attention mechanism enables the model to focus on specific areas or features relevant to CRM risk analysis. Empirical evidence demonstrates that this approach can effectively identify potential risks and provide data-driven support for business decisions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps businesses make better decisions by analyzing potential risks in customer relationships. It uses a special kind of AI called QRCNN-LSTM, which is good at understanding patterns in data. The researchers added another tool called cross-attention to help the model focus on important details. By combining these two tools, they showed that it’s possible to identify potential risks and make more informed business decisions. |
Keywords
» Artificial intelligence » Cross attention » Deep learning » Lstm » Neural network
