The need for recommendation systems to ease user navigations has become evident by growth of information on the Web. There exist many approaches of learning for Web usage-based recommendation systems. In hybrid recommendation systems, other knowledge resources, like content, semantics, and hyperlink structure of the Web site, have been utilized to enhance usage-based personalization systems. In this study, we introduce a new structure-based similarity measure for user sessions. We also apply two clustering algorithms on this similarity measure to compare it to cosine and another structure-based similarity measures. Our experiments exhibit that adding structure information, leveraging the proposed similarity measure, enhances the quality of recommendations in both methods.