Summary of A Utility-mining-driven Active Learning Approach For Analyzing Clickstream Sequences, by Danny Y. C. Wang et al.
A Utility-Mining-Driven Active Learning Approach for Analyzing Clickstream Sequences
by Danny Y. C. Wang, Lars Arne Jordanger, Jerry Chun-Wei Lin
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The High-Utility Sequential Pattern Mining using SHAP values (HUSPM-SHAP) model is an active learning strategy designed for selecting high-quality training data in the rapidly evolving e-commerce industry. This study shows that the HUSPM-SHAP model outperforms other approaches in predicting behaviors leading to purchases or not, while reducing labeling needs and maintaining predictive performance. The model’s superiority is demonstrated across various scenarios, highlighting its ability to refine e-commerce data processing and facilitate cost-effective prediction modeling. By leveraging SHAP values and utility mining, the HUSPM-SHAP model tackles the challenge of selecting high-quality training data, achieving better outcomes than traditional methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to predict what people will buy online. This paper introduces a new way to help make those predictions more accurate by choosing the right data to train the models. They developed an active learning strategy called HUSPM-SHAP that helps find the most important patterns in the data. By using this approach, they were able to make better predictions about what people will buy or not. This means companies can save time and money by getting the right data for their models. |
Keywords
» Artificial intelligence » Active learning
