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

TL;DR

This paper presents HUSPM-SHAP, a utility mining-based active learning method that improves clickstream sequence analysis in e-commerce by reducing labeling efforts while maintaining high prediction accuracy.

Contribution

The study introduces a novel active learning approach using utility mining and SHAP values, addressing parameter sensitivity and demonstrating superior performance in e-commerce behavior prediction.

Findings

HUSPM-SHAP effectively reduces labeling requirements.

The model outperforms existing methods in diverse scenarios.

Parameter settings for SHAP values significantly influence outcomes.

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.