SYSTEMS AND METHODS OF PREDICTING RESOURCE USEFULNESS USING UNIVERSAL RESOURCE LOCATORS
First Claim
1. A 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 being based on a training set of URLs, a count of negative URLs and a count of positive URLs identified by the training set, and for each feature extracted from the URLs in the training set, a count of the positive URLs in the training set that include the feature and a count of the negative URLs in the training set that include the feature;
extracting one or more features of the given URL; and
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.
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Abstract
A method, system and apparatus are provided to train 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 being based on a training set of URLs and a count of negative URLs and a count of positive URLs identified by the training set, and for each feature extacted from the URLs in the training set, a count of the positive URLs in the training set that include the feature and a count of the negative URLs in the training set that include the feature. One or more features of the given URL are extracted, and the extracted features are used together with the usefulness prediction model to generate a usefulness prediction for the given URL.
36 Citations
34 Claims
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1. A method comprising:
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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 being based on a training set of URLs, a count of negative URLs and a count of positive URLs identified by the training set, and for each feature extracted from the URLs in the training set, a count of the positive URLs in the training set that include the feature and a count of the negative URLs in the training set that include the feature; extracting one or more features of the given URL; and 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. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 25, 26, 33)
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16. A computer-readable medium storing computer-executable program code comprising code to configure at least one processor to:
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train 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 being based on a training set of URLs, a count of negative URLs and a count of positive URLs identified by the training sets and for each feature extracted from the URLs in the training set, a count of the positive URLs in the training set that include the feature and a count of the negative URLs in the training set that include the feature; extract one or more features of the given URL; and 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. - View Dependent Claims (17, 18, 19, 20, 21, 22, 23, 24, 27, 28, 29, 30)
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31. A system comprising:
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a search engine configured to use a usefulness prediction generated from a given universal resource locator (URL) as a part of an operation performed by the search engine involving the given URL; a usefulness predictor configured to generate a usefulness predictor configured to generate the usefulness prediction from the given URL using a usefulness model by; obtaining 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; obtaining 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 comparing the positive usefulness prediction value with the negative usefulness prediction value to generate the usefulness prediction in connection with the given URL. - View Dependent Claims (32, 34)
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Specification