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While the World Wide Web (WWW) contains a vast
quantity of information, it is often difficult for
Web users to find the information they seek. There
are many recommender systems that are designed to
help users find relevant information on the Web;
however, as many of these systems are server-side,
they can only provide information about one specific
Web site and they are typically based only on
correlations amongst the pages that the various users
visit. Unfortunately, there is no reason to believe
that these correlated pages will necessarily contain
useful information.
Here, a passive Goal-Directed Complete-Web (GCW)
recommender system, which recommends relevant pages
from anywhere on the Web without any explicit
additional input, has been developed. After
identifying the search strategy that is employed by
actual users while they browse the Web, the model
attempts to locate the pages that satisfy the current
information need based on the content of the pages
the user has visited, and the actions the user has
applied to these pages.

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