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Predicting Content Change on the Web

Kira Radinsky and Paul N. Bennett

Abstract

Accurate prediction of changing web page content improves a variety of retrieval and web related components. For example, given such a prediction algorithm one can both design a better crawling strategy that only recrawls pages when necessary as well as a proactive mechanism for personalization that pushes content associated with user revisitation directly to the user. While many techniques for modeling change have focused simply on past change frequency, our work goes beyond that by additionally studying the usefulness in page change prediction of: the page's content; the degree and relationship among the prediction page's observed changes; the relatedness to other pages and the similarity in the types of changes they undergo. We present an expert prediction framework that incorporates the information from these other signals more effectively than standard ensemble or basic relational learning techniques. In an empirical analysis, we find that using page content as well as related pages significantly improves prediction accuracy and compare it to common approaches. We present numerous similarity metrics to identify related pages and focus specifically on measures of temporal content similarity. We observe that the different metrics yield related pages that are qualitatively different in nature and have different effects on the prediction performance.

Details

Publication typeInproceedings
Published inProceedings of the 6th ACM International Conference on Web Search and Data Mining (WSDM '13)
URLhttp://research.microsoft.com/en-us/um/people/pauben/papers/wsdm2013-change-radinsky-bennett.pdf
PublisherACM
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