A Utility-Mining-Driven Active Learning Approach for Analyzing Clickstream Sequences

• URL: http://arxiv.org/abs/2410.07282v1
• Date: Wed, 9 Oct 2024 10:44:02 GMT
• Title: A Utility-Mining-Driven Active Learning Approach for Analyzing Clickstream Sequences
• Authors: Danny Y. C. Wang, Lars Arne Jordanger, Jerry Chun-Wei Lin,
• Abstract summary: This study introduces the High-Utility Sequential Pattern Mining using SHAP values (HUSPM-SHAP) model. Our findings demonstrate the model's capability to refine e-commerce data processing, steering towards more streamlined, cost-effective prediction modeling.
• Score: 21.38368444137596
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In rapidly evolving e-commerce industry, the capability of selecting high-quality data for model training is essential. This study introduces the High-Utility Sequential Pattern Mining using SHAP values (HUSPM-SHAP) model, a utility mining-based active learning strategy to tackle this challenge. We found that the parameter settings for positive and negative SHAP values impact the model's mining outcomes, introducing a key consideration into the active learning framework. Through extensive experiments aimed at predicting behaviors that do lead to purchases or not, the designed HUSPM-SHAP model demonstrates its superiority across diverse scenarios. The model's ability to mitigate labeling needs while maintaining high predictive performance is highlighted. Our findings demonstrate the model's capability to refine e-commerce data processing, steering towards more streamlined, cost-effective prediction modeling.

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