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The explosive growth of online data and the diversity of goals that may be pursued over the Web have significantly increased the monetary value of the Web traffic. To tap into this accelerating market, Web site operators try to increase their traffic by customizing their sites to the needs of specific users. Web site customization involves two great challenges: the effective identification of the user interests and the encapsulation of those interests into the sites' presentation and content. In this paper, we study how we can effectively detect the user interests that are hidden behind navigational patterns and we introduce a novel recommendation mechanism that employs Web mining techniques for correlating the identified interests to the sitespsila semantic content, in order to customize them to specific users. Our experimental evaluation shows that the user interests can be accurately detected from their navigational behavior and that our recommendation mechanism, which uses the identified interests, yields significant improvements in the sites' usability.