Sequence rule models for Web usage mining

Every time a user links tip to a web site, the server keeps track of all the actions accomplished in a log file. What is captured is the "click flow" (clickstream) of the mouse and the keys used by the user during the navigation inside the site. Usually every click of the mouse corresponds to the viewing of a web page. Therefore, we can define the clickstream as the sequence of the web pages requested. The objective of this chapter is to show how web clickstream data can be used to understand the most likely paths of navigation in a web site, with the aim of predicting, possibly on-line, which pages will be seen, having seen a specific path of other pages before. Such analysis can be very useful to understand, for instance, what is the probability of seeing a page of interest (such as the buying page in an e-commerce site) coming from another page. Or what is the probability of entering (or exiting) the web site from any particular page. From a methodological viewpoint, our aim is to present new associative models, obtained by means of statistical graphical Markov models, and compare them with classical association rules, direct or embodied in classification tree models. More specifically, as web pages are ordered in time, we shall consider sequence rules.