Single level algorithm: An improved approach for extracting user navigational patterns to improve website effectiveness

Today, Internet is playing a vital role as both an information seeking and an electronic commerce tool thus it becomes indispensable to understand different aspects of web sites and their visibility concerns. At the same time, the number of consumers is seeking ways to infer the customer\´s interests and to adapt their web sites to make the content of interest more easily accessible. Pattern mining is a promising approach in support of this goal. Assuming that past navigation behavior is an indicator of the user\´s interests, then, the records of this behavior, kept in the form of the webserver logs, can be mined to infer what the users are interested in. On that basis, recommendations can be dynamically generated, to help new web-site visitors find the information of interest faster. In this work, a new pattern mining algorithm, for efficiently extracting approximate behavior patterns so that slight navigation variations can be ignored when extracting frequently occurring patterns, is introduced. This algorithm is particularly useful with websites that have a large number of web-pages. Our particular approach is tailored to "focus" web sites that offer information on a well defined subject, such as, for example, the web site of an undergraduate course. Visitors of such focused sites exhibit similar types of navigation behavior, corresponding to the services offered by the web sites; therefore, page recommendation based on usage pattern mining can be quite effective.