Dichotomic Pattern Mining with Applications to Intent Prediction from Semi-Structured Clickstream Datasets
This work addresses the challenge of knowledge extraction and predictive modeling from semi-structured datasets, specifically for customer intent prediction, representing an incremental advancement in pattern mining techniques.
The paper tackles the problem of predicting customer intent from semi-structured clickstream data by introducing a pattern mining framework that creates novel pattern embeddings, resulting in improved performance and retained interpretability for downstream machine learning models.
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.