论文标题
二分法模式挖掘和应用程序从半结构clickstream数据集中进行预测
Dichotomic Pattern Mining with Applications to Intent Prediction from Semi-Structured Clickstream Datasets
论文作者
论文摘要
我们介绍了一个模式挖掘框架，该框架在半结构化数据集上运行，并利用结果之间的二分法。我们的方法利用约束推理找到经常发生的顺序模式并表现出所需的特性。这允许创建用于知识提取和预测建模有用的新型模式嵌入。最后，我们介绍了来自数字点击屏数据的客户意图预测的应用程序。总体而言，我们表明模式嵌入在半结构化数据和机器学习模型之间起着积分器的作用，提高下游任务的性能并保持可解释性。
We introduce a pattern mining framework that operates on semi-structured datasets and exploits the dichotomy between outcomes. Our approach takes advantage of constraint reasoning to find sequential patterns that occur frequently and exhibit desired properties. This allows the creation of novel pattern embeddings that are useful for knowledge extraction and predictive modeling. Finally, we present an application on customer intent prediction from digital clickstream data. Overall, we show that pattern embeddings play an integrator role between semi-structured data and machine learning models, improve the performance of the downstream task and retain interpretability.
