An Event Frequency-Inverse Session Frequency Based Weighting Mechanism for Data Embedding Methodologies

Abstract

ContextWith the rapid increase in data complexity and volume, analyzing and understanding large-scale datasets has become a critical challenge, especially in domains like e-commerce. This paper addresses this challenge by introducing a novel business process that utilizes an event frequency-inverse session frequency (EF-ISF) based weighting mechanism for embedding user navigational behavior in e-commerce websites.ObjectiveThe primary research problem tackled in this study is the detection of patterns and distributions within user behavior data, which is often complicated by dynamic page content and session sparsity. We aim to enhance the interpretability and accuracy of user behavior analysis by integrating EF-ISF with various embedding techniques, including Word2Vec, Node2Vec, DeepWalk, Struc2Vec, and LSTM Autoencoder.MethodologyOur approach is particularly effective in datasets where page refreshes lead to event repetitions, where sequential events with temporal dependencies are prevalent, and where event distributions across sessions are sparse. By applying this methodology across multiple e-commerce datasets, we observe consistent performance in clustering quality, successfully detecting underlying patterns and groups within the data.ResultsA prototype implementation demonstrates the practical utility of the proposed method, with evaluation metrics confirming that EF-ISF weighting not only enhances the embeddings but also facilitates a more nuanced and significant segmentation of users' browsing behaviors.ConclusionThese findings provide a valuable contribution to the field of e-commerce data analytics, offering businesses a refined approach to optimizing user experience and engagement through a deeper understanding of user behavior patterns. This methodology can significantly improve the quality of insights derived from user behavior data, ultimately leading to better decision-making and enhanced user satisfaction.