Abstract: Systems and method can be provided for selecting advertising payloads for display in an available advertising impression location. The advertising payloads can be selected based on an auction between various types of hosted and third party campaigns, including hosted reserved advertising campaigns and hosted non-reserved advertising campaigns. The rules of the auction can be set and/or updated over time to allow hosted campaigns to meet desired goals, such as delivering a minimum number of impressions or spending an expect budget amount.

Abstract: Embodiments of the present invention provide systems and methods for ranking a result set. The method according to one embodiment comprises selecting an item from the result set, selecting a user profile from one or more user profiles and selecting one or more items of personalized information from the selected user profile. A rank is calculated for the item on the basis of the selected one or more items of personalized information and the selected item in the result set is ranked in accordance with the calculated rank.

Abstract: A method and a system are provided for large-scale behavioral targeting for advertising over a network, such as the Internet. In one example, the system receives training data that is processed raw data of user behavior. The system generates selected features by performing feature selection on the training data. The system generates feature vectors from the selected features. The system initializes weights of a behavioral targeting model by scanning the feature vectors once. The system then updates the weights of the behavioral targeting model by scanning iteratively the feature vectors using a multiplicative recurrence.

Abstract: Embodiments of the present invention provide systems and methods for ranking a result set. The method according to one embodiment comprises selecting an item from the result set, selecting a user profile from one or more user profiles and selecting one or more items of personalized information from the selected user profile. A rank is calculated for the item on the basis of the selected one or more items of personalized information and the selected item in the result set is ranked in accordance with the calculated rank.

Abstract: A method of targeting receives several granular events and preprocesses the received granular events thereby generating preprocessed data to facilitate construction of a model based on the granular events. The method generates a predictive model by using the preprocessed data. The predictive model is for determining a likelihood of a user action. The method trains the predictive model. A system for targeting includes granular events, a preprocessor for receiving the granular events, a model generator, and a model. The preprocessor has one or more modules for at least one of pruning, aggregation, clustering, and/or filtering. The model generator is for constructing a model based on the granular events, and the model is for determining a likelihood of a user action. The system of some embodiments further includes several users, a selector for selecting a particular set of users from among the several users, a trained model, and a scoring module.

Abstract: A method of targeting receives several granular events and preprocesses the received granular events thereby generating preprocessed data to facilitate construction of a model based on the granular events. The method generates a predictive model by using the pre-processed data. The predictive model is for determining a likelihood of a user action. The method trains the predictive mode. A system for targeting includes granular events, a preprocessor for receiving the granular events, a model generator, and a model. The preprocessor has one or more modules for at least one of pruning, aggregation, clustering, and/or filtering. The model generator is for constructing a model based on the granular events, and the model is for determining a likelihood of a user action. The system of some embodiments further includes several users, a selector for selecting a particular set of users from among the several users, a trained model, and a scoring module.

Abstract: A variety of techniques are described by which keyword sets and target audience profiles may be generalized in a systematic and effective way with reference to relationships between keywords, profiles, and the data of an underlying user population.

Abstract: A variety of techniques are described by which keyword sets and target audience profiles may be generalized in a systematic and effective way with reference to relationships between keywords, profiles, and the data of an underlying user population.

Abstract: A method and a system are provided for large-scale behavioral targeting for advertising over a network, such as the Internet. In one example, the system receives training data that is processed raw data of user behavior. The system generates selected features by performing feature selection on the training data. The system generates feature vectors from the selected features. The system initializes weights of a behavioral targeting model by scanning the feature vectors once. The system then updates the weights of the behavioral targeting model by scanning iteratively the feature vectors using a multiplicative recurrence.

Abstract: Techniques are described for generating an authority value of a first one of a plurality of documents. A first component of the authority value is generated with reference to outbound links associated with the first document. The outbound links enable access to a first subset of the plurality of documents. A second component of the authority value is generated with reference to a second subset of the plurality of documents. Each of the second subset of documents represents a potential starting point for a user session. A third component of the authority value is generated representing a likelihood that a user session initiated by any of a population of users will end with the first document. The first, second, and third components of the authority value are combined to generate the authority value. At least one of the first, second, and third components of the authority value is computed with reference to user data relating to at least some of the outbound links and the second subset of documents.

Abstract: A first component of an authority value is generated with reference to outbound links associated with a document and corresponding to a first subset of a plurality of documents. A second component of the authority value is generated with reference to a second subset of the plurality of documents that represent potential starting points for user sessions. A third component of the authority value is generated representing a likelihood that a user session initiated by any of a population of users will end with the document. At least one of the first, second, and third components of the authority value is computed with reference to user data relating to at least some of the outbound links and the second subset of documents.

Abstract: A method of classifying documents includes: specifying multiple documents and classes, wherein each document includes a plurality of features and each document corresponds to one of the classes; determining reduced document vectors for the classes from the documents, wherein the reduced document vectors include features that satisfy threshold conditions corresponding to the classes; determining reduced weight vectors for relating the documents to the classes by comparing combinations of the reduced weight vectors and the reduced document vectors and separating the corresponding classes; and saving one or more values for the reduced weight vectors and the classes. Specific embodiments are directed to formulations for determining the reduced weight vectors including one-versus-rest classifiers, maximum entropy classifiers, and direct multiclass Support Vector Machines.

Abstract: Methods and apparatus are described for identifying newsworthy search queries employing a machine learning approach which combines offline and online modeling to achieve a high level of accuracy as well as timeliness and scalability.

Abstract: Methods and apparatus are described for storing a plurality of objects in a plurality of storage options. An importance index is generated for each of the plurality of objects with reference to importance data associated with each object. At least a portion of the importance data represents relevance of the associated object relative to a population of users interacting with the plurality of objects. Each of the objects is stored in a selected one of the storage options with reference to the corresponding importance index and a hierarchy of the storage options. The hierarchy of storage options represents at least partial ordering of the storage options with reference to economic costs and efficiency of retrieval.

Abstract: Embodiments of the invention relate to methods of presenting personalized search results pages to users, and to search engine systems and servers configured to implement such methods. For example, a method of presenting such a page to a user of a search engine includes steps of computing an engagement index of the user based on the distribution in time of that user's interactions with the search engine then presenting, in response to a query by the user, a personalized search results page to the user.

Abstract: A computer implemented method of ranking search hits in a search result set. The computer-implemented method includes receiving a query from a user and generating a list of hits related to the query, where each of the hits has a relevance to the query, where the hits have one or more boosting linked documents pointing to the hits, and where the boosting linked documents affect the relevance of the hits to the query. The method associates a metric to each of at least a subset of the hits, the metric being representative of the number of boosting linked documents that point to each of at least a subset of the hits and which artificially inflate the relevance of the hits. The method then compares the metric, which is representative of the size of a spam farm pointing to the hit, with a threshold value, processes the list of hits to form a modified list based in part on the comparison, and transmits the modified list to the user.

Abstract: Methods and apparatus are described for presenting sponsored search results. A user is enabled to initiate a search from a context. The sponsored search results and organic search results are presented in a search results page in response to the search, an order of the sponsored search results and placement of subsets of the sponsored search results relative to the organic search results in the search results page having been determined with reference to contextual information relating to the context.

Abstract: A method of targeting receives several granular events and preprocesses the received granular events thereby generating preprocessed data to facilitate construction of a model based on the granular events. The method generates a predictive model by using the pre-processed data. The predictive model is for determining a likelihood of a user action. The method trains the predictive mode. A system for targeting includes granular events, a preprocessor for receiving the granular events, a model generator, and a model. The preprocessor has one or more modules for at least one of pruning, aggregation, clustering, and/or filtering. The model generator is for constructing a model based on the granular events, and the model is for determining a likelihood of a user action. The system of some embodiments further includes several users, a selector for selecting a particular set of users from among the several users, a trained model, and a scoring module.

Abstract: The present invention is directed towards systems and methods for ranking and providing advertisements in a position auction. The method of the present invention comprises receiving a search query and selecting at least one keyword based upon the search query. A list containing at least one keyword based upon the search query is returned and a list comprising at least one bid corresponding to the returned list of keywords is retrieved. A priority score corresponding to each bid is computed and used to rank the list of bids. Advertisements are then provided corresponding to a plurality of the highest ranking bids.

Abstract: Methods and apparatus are described for evaluating a binary classification system operable to classify each of a plurality of events as a first event type or a second event type. At least some of the events of the first event type are independently verifiable with reference to verification data. The binary classification system is susceptible to a first error type in which events of the first event type are classified as the second event type, and a second error type in which events of the second event type are classified as the first event type. Operation of a first configuration of the binary classification system is evaluated with reference to an objective function. The objective function is derived by expressing a number of errors of the second error type in terms of a number of errors of the first error type with reference to the verification data, and by assuming relative proportions of the first and second event types within the plurality of events.