Summary of An Explainable Machine Learning-based Approach For Analyzing Customers’ Online Data to Identify the Importance Of Product Attributes, by Aigin Karimzadeh et al.
An explainable machine learning-based approach for analyzing customers’ online data to identify the importance of product attributes
by Aigin Karimzadeh, Amir Zakery, Mohammadreza Mohammadi, Ali Yavari
First submitted to arxiv on: 3 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Science and Game Theory (cs.GT)
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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 A novel game theory machine learning (ML) method is proposed for extracting comprehensive design implications from online customer data for product development. The approach combines a genetic algorithm to select features that maximize customer satisfaction based on online ratings with SHAP (SHapley Additive exPlanations), which assigns values to each feature based on its contribution to prediction. This method outperforms benchmark methods and can help product designers and marketers better understand customer preferences with less data and effort. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers created a new way to use artificial intelligence (AI) to design products that customers will like. They used special algorithms and math to analyze what people say online about different products, like laptops. This helps product designers make good choices without needing as much information or doing so much work. The results show that this approach is better than other methods for understanding what customers want. |
Keywords
* Artificial intelligence * Machine learning
