A Utility-Mining-Driven Active Learning Approach for Analyzing Clickstream Sequences
<|reference_start|>A Utility-Mining-Driven Active Learning Approach for Analyzing Clickstream Sequences: 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.<|reference_end|>
@article{wang2024a,
    title={A Utility-Mining-Driven Active Learning Approach for Analyzing
  Clickstream Sequences},
    author={Danny Y. C. Wang, Lars Arne Jordanger, Jerry Chun-Wei Lin},
    journal={arXiv preprint arXiv:2410.07282},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.07282},
    primaryClass={cs.LG}
}