| Patent application number | Description | Published |
|---|
| 20090164502 | SYSTEMS AND METHODS OF UNIVERSAL RESOURCE LOCATOR NORMALIZATION - Disclosed herein are method, systems and architectures for normalizing identifiers corresponding to resources using normalization rules that can be generalized for use with different resources. By way of a non-limiting example, an identifier can be a uniform resource locator (URL), and a normalization rule can be used to normalize URLs that correspond to different resources, e.g., content. A normalization rule can be generated by generalizing two or more normalization rules corresponding to different resources, such that a content determinative component is generalized. A normalization rule can be defined to include a context portion used to determine the rule's applicability to an identifier, and a transformation portion that identifies the transformations to be applied to an applicable identifier to yield a normalized form of the URL. A generalization of two or more normalization rules can include a normalization of one or both of the context and transformation portions. | 06-25-2009 |
| 20090171870 | System and method of feature selection for text classification using subspace sampling - An improved system and method is provided for feature selection for text classification using subspace sampling. A text classifier generator may be provided for selecting a small set of features using subspace sampling from the corpus of training data to train a text classifier for using the small set of features for classification of texts. To select the small set of features, a subspace of features from the corpus of training data may be randomly sampled according to a probability distribution over the set of features where a probability may be assigned to each of the features that is proportional to the square of the Euclidean norms of the rows of left singular vectors of a matrix of the features representing the corpus of training texts. The small set of features may classify texts using only the relevant features among a very large number of training features. | 07-02-2009 |
| 20090327168 | PLAYFUL INCENTIVE FOR LABELING CONTENT - Embodiments are directed towards employing a playful incentive to encourage users to provide feedback that is useable to train a classifier. The classifier being associated with any of a variety of different settings, including but not limited to classifying: messages as ham/spam, images, advertising, bookmarking, music, videos, photographs, shopping, or the like. An animated image, such as a pet, provides an interface to the classifier that encourages and responds to user feedback. Users may share their classifiers or aspects thereof with other users to enable a community of knowledge to be applied to a classification task, while preserving privacy of the user feedback. One form of sharing may be within the context of a competitive game. Various evaluations may be performed on a classifier to indicate user feedback consistency, or quality. Classifiers may also be used to provide users with advertisements, products, or services based on the user's feedback. | 12-31-2009 |
| 20100063881 | ALGORITHM FOR STORYBOARDING IN DISPLAY ADVERTISING - Methods and system for optimally allocating ad space to advertisers on a webpage viewed by a user in a single browsing session includes identifying a plurality of advertisement stories that match the content of the webpage. An advertisement pool is generated using the identified ad stories. Each ad story in the advertisement pool includes one or more advertisement pages and is associated with corresponding ad value. An ad story from the pool of ad stories is chosen by dynamically evaluating ad value associated with each ad story in the pool based on continued surfing by the user such that the identified ad story provides the maximum ad value when rendered on the webpage. The identified ad story is scheduled for rendering on the webpage while providing relevant ad content at the webpage. | 03-11-2010 |
