The paths more taken: matching DOM trees to search logs for accurate webpage clustering

• Authors: Deepayan Chakrabarti;Rupesh Mehta
• Venue: Proceedings of the 19th international conference on World wide web
• Year: 2010

An unsupervised clustering of the webpages on a website is a primary requirement for most wrapper induction and automated data extraction methods. Since page content can vary drastically across pages of one cluster (e.g., all product pages on amazon.com), traditional clustering methods typically use some distance function between the DOM trees representing a pair of webpages. However, without knowing which portions of the DOM tree are "important," such distance functions might discriminate between similar pages based on trivial features (e.g., differing number of reviews on two product pages), or club together distinct types of pages based on superficial features present in the DOM trees of both (e.g., matching footer/copyright), leading to poor clustering performance. We propose using search logs to automatically find paths in the DOM trees that mark out important portions of pages, e.g., the product title in a product page. Such paths are identified via a global analysis of the entire website, whereby search data for popular pages can be used to infer good paths even for other pages that receive little or no search traffic. The webpages on the website are then clustered using these "key" paths. Our algorithm only requires information on search queries, and the webpages clicked in response to them; there is no need for human input, and it does not need to be told which portion of a webpage the user found interesting. The resulting clusterings achieve an adjusted RAND score of over 0.9 on half of the websites (a score of 1 indicating a perfect clustering), and 59% better scores on average than competing algorithms. Besides leading to refined clusterings, these key paths can be useful in the wrapper induction process itself, as shown by the high degree of match between the key paths and the manually identified paths used in existing wrappers for these sites (90% average precision).