  Extensive research has been conducted on query log analysis. A query log is
generally represented as a bipartite graph on a query set and a URL set. Most
of the traditional methods used the raw click frequency to weigh the link
between a query and a URL on the click graph. In order to address the
disadvantages of raw click frequency, researchers proposed the entropy-biased
model, which incorporates raw click frequency with inverse query frequency of
the URL as the weighting scheme for query representation. In this paper, we
observe that the inverse query frequency can be considered a global property of
the URL on the click graph, which is more informative than raw click frequency,
which can be considered a local property of the URL. Based on this insight, we
develop the global consistency model for query representation, which utilizes
the click frequency and the inverse query frequency of a URL in a consistent
manner. Furthermore, we propose a new scheme called inverse URL frequency as an
effective way to capture the global property of a URL. Experiments have been
conducted on the AOL search engine log data. The result shows that our global
consistency model achieved better performance than the current models.
