1. A computer-implemented method comprising: training a usefulness prediction model to generate a usefulness prediction in connection with a given universal resource locator (URL), the training of the usefulness prediction model comprising: inputting a training set of URLs, determining the quantity of negative URLs and positive URLs identified by the training set, extracting one or more features from the URLs in the training set, counting the number of times each feature occurs in said positive URLs, counting the number of times each feature occurs in said negative URLs, and using the quantities of positive and negative URLs, the number of times the features occurred in said positive and negative URLs, the one or more features extracted from the given URL and the usefulness prediction model to generate the usefulness prediction in connection with the given URL.
15. The method of claim 1 , using the one or more features extracted from the given URL and the usefulness prediction model to generate the usefulness prediction in connection with the given URL further comprising: using a decision tree to generate the usefulness prediction in connection with the given URL, the nodes of the decision tree being identified using the negative and positive URLs count and the negative and positive feature counts identified by the training set.