1. A method for network resource classification, comprising: obtaining a hierarchy of categories for classifying a plurality of network resources, where each category is assigned a text item describing the category; obtaining a plurality of resource description data collections corresponding to the plurality of network resources, wherein the plurality of resource description data collections comprise a first resource description data collection corresponding to a first network resource of the plurality of network resources; generating, by a computer processor and using a semantic correlation algorithm, a first category score vector of the first network resource by comparing the first resource description data collection to the text item assigned to each category in the hierarchy of categories, wherein the first category score vector comprises a category score for each category in the hierarchy of categories, wherein the category score is determined based on at least a semantic correlation measure between the first resource description data collection and the text item assigned to a corresponding category, wherein the plurality of network resources are classified based at least on the category score; analyzing a network trace associated with a user to identify the plurality of network resources accessed by the user; generating, based on a pre-determined criterion, a relationship graph comprising: a plurality of nodes representing the plurality of network resources, and a plurality of edges representing a measure of cross-references between the plurality of resource description data collections; and adjusting, based on the relationship graph, the first category score vector to generate a first adjusted category score vector using at least another category score vector of another network resource of the plurality of network resources.
7. The method of claim 1 , further comprising: analyzing the first category score vector to determine a first score differentiation measure representing variations among category scores in the first category score vector; and analyzing the first adjusted category score vector to determine a first adjusted score differentiation measure representing variations among adjusted category scores in the first adjusted category score vector; wherein adjusting the first category score vector is in response to the first score differentiation measure failing to meet a first pre-determined criterion, and wherein the interest level of the user is inferred in response to the first adjusted score differentiation measure meeting the first pre-determined criterion.