Discovering critical edge sequences in E-commerce catalogs

Web sites allow the collection of vast amounts of navigational data -- clickstreams of user traversals through the site. These massive data stores offer the tantalizing possibility of uncovering interesting patterns within the dataset. For e-businesses, always looking for an edge in the hyper-competitive online marketplace, this possibility is of particular interest. Of significant particular interest to e-businesses is the discovery of Critical Edge Sequences (CES), which denote frequently traversed subpaths in the catalog. CESs can be used to improve site performance and site management, increase the effectiveness of advertising on the site, and gather additional knowledge of customer interest patterns on the site.Using traditional graph-based and web mining strategies to find CESs could turn out to be expensive in both space and time. In this paper, we propose a method to compute the most popular paths bewteen node pairs in a catalog, which are then used to discover CESs. Our method is both space-efficient and accurate, providing a vast reduction in the storage requirement with a minimum impact on accuracy. This algorithm, executed off-line in batch mode, is also practical with respect to running time. As a variant of single-source shortest-path, it runs in log linear time