Mining Web Logs for Personalized Site Maps

Navigating through a large Web site can be a frustrating exercise. Many sites employ Site Maps to help visitors understand the overall structure of the site. However, by their very nature, unpersonalized Site Maps show most visitors large amounts of irrelevant content. We propose techniques based on Web usage mining to deliver Personalized Site Maps that are specialized to the interests of each individual visitor. The key challenge is to resolve the tension between simplicity (showing just relevant content), and comprehensibility (showing sufficient context so that the visitors can understand how the content is related to the overall structure of the site). We develop two baseline algorithms (one that displays just shortest paths, and one that mines the server log for popular paths), and compare them to a novel approach that mines the server log for popular path fragments that can be dynamically assembled to reconstruct popular paths. Our experiments with two large Web sites confirm that the mined path fragments provide much better coverage of visitors sessions than the baseline approach of mining entire paths.