Graphical relationships among web pages have been leveraged as sources of information in methods for ranking search results. To date, speci\ufb01c graphical properties have been used in these analyses. We introduce web projections, a methodology that generalizes prior e\ufb00orts on exploiting graphical relationships of the web in several ways. With the approach, we create subgraphs by projecting sets of pages and domains onto the larger web graph, and then use machine learning to construct predictive models that consider graphical properties as evidence. We describe the method and present experiments that illustrate the construction of predictive models of search result quality and user query reformulation.<\/p>\n","protected":false},"excerpt":{"rendered":"
Graphical relationships among web pages have been leveraged as sources of information in methods for ranking search results. To date, speci\ufb01c graphical properties have been used in these analyses. We introduce web projections, a methodology that generalizes prior e\ufb00orts on exploiting graphical relationships of the web in several ways. 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