COHORTNEY: Non-Parametric Clustering of Event Sequences
This work addresses the lack of academic discussion on cohort analysis for evaluating user behavior online, providing a machine learning-based solution for web analytics.
The paper tackled the problem of grouping Internet users based on their activities by introducing COHORTNEY, an unsupervised non-parametric clustering algorithm for event sequences, which outperforms canonical marketing and engineering-based methods and is the first with a robust theoretical explanation.
Cohort analysis is a pervasive activity in web analytics. One divides users into groups according to specific criteria and tracks their behavior over time. Despite its extensive use, academic circles do not discuss cohort analysis to evaluate user behavior online. This work introduces an unsupervised non-parametric approach to group Internet users based on their activities. In comparison, canonical methods in marketing and engineering-based techniques underperform. COHORTNEY is the first machine learning-based cohort analysis algorithm with a robust theoretical explanation.