A Utility-Mining-Driven Active Learning Approach for Analyzing Clickstream Sequences
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866917799851458560 |
|---|---|
| author | Wang, Danny Y. C. Jordanger, Lars Arne Lin, Jerry Chun-Wei |
| author_facet | Wang, Danny Y. C. Jordanger, Lars Arne Lin, Jerry Chun-Wei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_07282 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Utility-Mining-Driven Active Learning Approach for Analyzing Clickstream Sequences Wang, Danny Y. C. Jordanger, Lars Arne Lin, Jerry Chun-Wei Machine Learning 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. |
| title | A Utility-Mining-Driven Active Learning Approach for Analyzing Clickstream Sequences |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2410.07282 |