用用户点击行为预测电商页面挫败感，提前发现体验问题。
Machine Learning to Predict Digital Frustration from Clickstream Data
- 基于点击流构建特征，用XGBoost和LSTM模型识别挫败会话。
- LSTM模型达91%准确率，前30次操作即可可靠预测挫败。
- 适用于优化网页交互、提升用户体验的工程与产品团队。
众多企业依赖移动应用和网站，用户在完成任务时的挫败感可能导致销售流失和投诉。本研究使用真实电商平台的点击流数据，通过特定规则定义挫败会话：包括愤怒爆发、来回导航（U型回转）、购物车放弃、搜索挣扎及长时间漫游。分析了540万条原始点击事件（共304,881个会话）。从每个会话中提取表格特征，训练标准分类器；同时利用完整事件序列训练判别性LSTM分类器。XGBoost模型达到约90%准确率，ROC AUC为0.9579；LSTM表现最佳，准确率达约91%，ROC AUC为0.9705。研究还发现，仅需前20至30次交互，LSTM即可实现可靠的挫败预测。
原文摘要 · Abstract (English)
Many businesses depend on their mobile apps and websites, so user frustration while trying to complete a task on these channels can cause lost sales and complaints. In this research, I use clickstream data from a real e-commerce site to predict whether a session is frustrated or not. Frustration is defined using certain rules based on rage bursts, back and forth navigation (U turns), cart churn, search struggle, and long wandering sessions, and applies these rules to 5.4 million raw clickstream events (304,881 sessions). From each session, I build tabular features and train standard classifier models. I also use the full event sequence to train a discriminative LSTM classifier. XGBoost reaches about 90% accuracy, ROC AUC of 0.9579, while the LSTM performs best with about 91% accuracy and a ROC AUC of 0.9705. Finally, the research shows that with only the first 20 to 30 interactions, the LSTM already predicts frustration reliably.
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