18. The computer-readable medium of claim 16 , the program code to configure the one or more processors to use 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 program code to cause the one or more processors to: generate a positive usefulness prediction value in connection with the given URL using the one or more features extracted from the given URL and the usefulness prediction model; generate a negative usefulness prediction value in connection with the given URL using the one or more features extracted from the given URL and the usefulness prediction model; and compare the positive usefulness prediction value with the negative usefulness prediction value to generate the usefulness prediction in connection with the given URL.
22. The computer-readable medium of claim 18 , the program code to configure the one or more processors to generate a negative usefulness prediction value further comprising program code to configure the one or more processors to: determine the negative usefulness prediction value to be a ratio of a negative probability to a sum of the negative probability and a positive probability, the positive probability being a product of a positive URL probability and a positive feature probability for each feature extracted from the given URL, the negative probability being a product of a negative URL probability and a negative feature probability for each feature extracted from the given URL, the positive URL probability being a ratio of a number of URLs classified as positive in the training set to a total number of URLs in the training set, the negative URL probability being a ratio of a number of URLs classified as negative in the training set to the total number of URLs in the training set, the positive feature probability for each feature being a ratio of a number of URLs in the training set that are classified as positive that include the feature to the number of URLs classified as positive in the training set, and the negative feature probability for each feature being a number of URLs in the training set that are classified as negative that include the feature to the number of URLs classified as negative in the training set.