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.