Pattern2Vec: Representation of clickstream data sequences for learning user navigational behavior

Abstract

Word embedding approaches represent data sequences to handle their contextual meaning in the NLP tasks. Nowadays, there is an emerging need to understand the user behavior patterns over navigational clickstream data. However, representing the URL data sequences utilizing existing embedding approaches to cluster users' behavior with unsupervised machine learning tasks is a challenging task. This study introduces the Patter2Vec embedding approach using a representation vector to construct contextual, precise, and interpretable clusters over the hidden and popular navigational patterns. To test the usability of the proposed representation in clustering tasks, we conduct an experimental study, which indicates that Pattern2Vec outperforms existing embedding approaches.