Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.

Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.

Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.

Abstract: Systems, methods, and computer storage media having computer-executable instructions embodied thereon that facilitate evaluation of digital content preferences are provided. A user is presented with items of digital content and permitted to manipulate the arrangement of the digital content items in the context of a layout area. Based on the user's manipulation of the digital content items, a user preference regarding an arrangement of digital content, such as a location preference, a position preference, and/or a usage preference, is identified. In embodiments, such a user preference can be utilized to later display digital content to a user in accordance therewith.

Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.

Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.

Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.

Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.

Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.

Abstract: Methods, systems, and computer-storage media having computer-usable instructions embodied thereon for calculating event probabilities are provided. The event may be a click probability. Event probabilities are calculated using a system optimized for runtime model accuracy with an operable learning algorithm. Bin counting techniques are used to calculate event probabilities based on a count of event occurrences and non-event occurrences. Linear parameters, such and counts of clicks and non-clicks, may also be used in the system to allow for runtime adjustments.

Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.

Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.

Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.

Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.

Abstract: For a multi-party online advertising exchange including advertising and publishing entities and one or more third party participants, the disclosed systems and methods enable third party participation in arbitrage opportunities in online advertising transactions. A plurality of underlying transaction details are abstracted and provided to the third party participants without loss of generalization and while preserving relationships in the transaction data, to enable a third party share risk in advertising transactions. Various system refinements are provided and disclosed according to a host of optional embodiments.

Abstract: For a multi-party advertising exchange, including publishing entities and advertising entities from disparate advertising networks, which facilitates transactions for publishing inventory, a value add broker is provided to aggregate information from third parties having valuable information for input to the exchange or to perform services that are valuable to transactions in the exchange. The valuable information or services further facilitate the transactions for the publishing inventory automatically generating a benefit for the third parties providing the valuable information or services commensurate with the value added to the transactions.

Abstract: Methods, systems, and computer-storage media having computer-usable instructions embodied thereon for calculating event probabilities are provided. The event may be a click probability. Event probabilities are calculated using a system optimized for runtime model accuracy with an operable learning algorithm. Bin counting techniques are used to calculate event probabilities based on a count of event occurrences and non-event occurrences. Linear parameters, such and counts of clicks and non-clicks, may also be used in the system to allow for runtime adjustments.

Abstract: Computer-readable media for determining whether to accept a candidate order from a content provider, or advertiser, to display a particular number of advertisements within a specified time segment are provided. Initially, the content provider may include placement criteria that, among other things, identify a leaf node at which impressions of the advertisement are expected to be rendered. Generally, the leaf node refers to a location within a topic graph that describes inventory that is permissible to allocate to satisfy the candidate order. To perform the determination, the inventory of impressions available for accommodating the candidate order and a log of booked orders scheduled to be placed within the time segment are identified. Linear programs are then utilized to determine whether the estimated inventory that satisfies the placement criteria is available by predictively placing the booked orders at the estimated inventory. If estimated inventory remains available, the candidate order is accepted.

Abstract: Computer-readable media for determining whether to accept a candidate order from a content provider, or advertiser, to display a particular number of advertisements within a specified time segment are provided. Initially, the content provider may include placement criteria that, among other things, identify a leaf node at which impressions of the advertisement are expected to be rendered. Generally, the leaf node refers to a location within a topic graph that describes inventory that is permissible to allocate to satisfy the candidate order. To perform the determination, the inventory of impressions available for accommodating the candidate order and a log of booked orders scheduled to be placed within the time segment are identified. Linear programs are then utilized to determine whether the estimated inventory that satisfies the placement criteria is available by predictively placing the booked orders at the estimated inventory. If estimated inventory remains available, the candidate order is accepted.

Abstract: For a multi-party advertising exchange including advertising and publishing entities, each participant specifies tax rate(s), such as import tax and export tax, that apply to at least one other entity in the exchange. Since tax rate(s) can be expressed in different transactional terms by different parties, each tax rate is reduced to a common tax rate expression within the exchange for comparison. Intelligent tax rate selection and support can be provided to dynamically set tax rates that achieve utilitarian goals for the individual participants taking into account the tax rates expressed by other participants and their respective advertising goals, and dynamically adjusting tax rates over time in response to condition changes. Various refinements are provided and disclosed according to a host of optional implementations.