Hierarchical topic segmentation of websites

In this paper, we consider the problem of identifying and segmenting topically cohesive regions in the URL tree of a large website. Each page of the website is assumed to have a topic label or a distribution on topic labels generated using a standard classier. We develop a set of cost measures characterizing the benet accrued by introducing a segmentation of the site based on the topic labels. We propose a general framework to use these measures for describing the quality of a segmentation; we also provide an ecient algorithm to nd the best segmentation in this framework. Extensive experiments on human-labeled data conrm the soundness of our framework and suggest that a judicious choice of cost measures allows the algorithm to perform surprisingly accurate topical segmentations. Categories and Subject Descriptors H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval General Terms Algorithms, Experimentation, Measurements Keywords Website Hierarchy, Website Segmen...

• Type: Conference
• Year: 2006
• Where: KDD
• Authors: Ravi Kumar, Kunal Punera, Andrew Tomkins
• Keywords: Cost Measures, Data Mining, KDD 2006, Topic Labels, Website Segmentation