Abstract: The system and method of the present invention allows for the determination of the relevance of a content item to a query through the use of a machine learned relevance function that incorporate query differentiation. A method for selecting a relevance function to determine a relevance of a query-content item pair comprises generating a training set comprising one or more content item-query pairs. Content item-query pairs in the training set are collectively used to determine the relevance function by minimizing a loss function according to a relevance score adjustment function that accounts for query differentiation. The monotocity of relevance score adjustment function allows the trained relevance function to be directly applied to new queries.

Abstract: The system and method of the present invention allows for the determination of the relevance of a content item to a query through the use of a machine learned relevance function that incorporate query differentiation. A method for selecting a relevance function to determine a relevance of a query-content item pair comprises generating a training set comprising one or more content item-query pairs. Content item-query pairs in the training set are collectively used to determine the relevance function by minimizing a loss function according to a relevance score adjustment function that accounts for query differentiation. The monotocity of relevance score adjustment function allows the trained relevance function to be directly applied to new queries.

Abstract: The system and method of the present invention allows for the determination of the relevance of a content item to a query through the use of a machine learned relevance function that incorporate query differentiation. A method for selecting a relevance function to determine a relevance of a query-content item pair comprises generating a training set comprising one or more content item-query pairs. Content item-query pairs in the training set are collectively used to determine the relevance function by minimizing a loss function according to a relevance score adjustment function that accounts for query differentiation. The monotocity of relevance score adjustment function allows the trained relevance function to be directly applied to new queries.

Abstract: Embodiments of the present invention provide for methods, systems and computer program products for learning ranking functions to determine the ranking of one or more content items that are responsive to a query. The present invention includes generating one or more training sets comprising one or more content item-query pairs, determining preference data for the one or more query-content item pairs of the one or more training sets and determining labeled data for the one or more query-content item pairs of the one or more training sets. A ranking function is determined based upon the preference data and the labeled data for the one or more content-item query pairs of the one or more training sets. The ranking function is then stored for application to query-content item pairs not contained in the one or more training sets.

Abstract: Embodiments of the present invention provide for methods, systems and computer program products for learning ranking functions to determine the ranking of one or more content items that are responsive to a query. The present invention includes generating one or more training sets comprising one or more content item-query pairs and determining one or more contradicting pairs in a given training sets. An optimization function to minimize the number of contradicting pairs in the training set is formulated, and modified by incorporating a grade difference between one or more content items corresponding to the query in the training set and applied to each query in the training set. A ranking function is determined based on the application of regression trees on the queries of the training set minimized by the optimization function and stored for application to content item-query pairs not contained in the one or more training sets.

Abstract: A method and system for blending ranking for an output display includes receiving a first list of content items having a first ranking determined by first ranking parameters, the first ranking providing for a sequential ordering of the content items of the first list. A second list of content items having a second ranking determined by second ranking parameters are received, the first ranking is incompatible with the second ranking because ranking parameters are different. The first list of content items is transformed to a modified first list that maintains the order of the content items and makes the first ranking of the modified first list compatible with the second ranking of the second list. The second list and the modified first list are merged to generate a blended list for an output display utilizing the blended list.

Abstract: The present invention relates to systems and methods for determining a content item relevance function. The method comprises collecting user preference data at a search provider for storage in a user preference data store and collecting expert-judgment data at the search provider for storage in an expert sample data store. A modeling module trains a base model through the use of the expert-judgment data and tunes the base model through the use of the user preference data to learn a set of one or more tuned models. A measure (B measure) is designed to evaluate the balanced performance of tuned model over expert judgment and user preference. The modeling module generates or selects the content item relevance function from the tuned models with B measure as the selection criterion.

Abstract: System and method for the determination of the relevance of a content item to a query through the use of a machine learned relevance function that incorporates click-through features of the content items. A method for selecting a relevance function to determine a relevance of a query-content item pair comprises generating training set having one or more query-URL pairs labeled for relevance based on their click-through features. The labeled query-URL pairs are used to determine the relevance function by minimizing a loss function that accounts for click-through features of the content item. The computed relevance function is then applied to the click-through features of unlabeled content items to assign relevance scores thereto. An inverted click-through index of query-score pairs is formed and combined with the content index to improve relevance of search results.

Abstract: Embodiments of the present invention provide for methods, systems and computer program products for learning ranking functions to determine the ranking of one or more content items that are responsive to a query. The present invention includes generating one or more training sets comprising one or more content item-query pairs and determining one or more contradicting pairs in a given training sets. An optimization function to minimize the number of contradicting pairs in the training set is formulated. and modified by incorporating a grade difference between one or more content items corresponding to the query in the training set and applied to each query in the training set. A ranking function is determined based on the application of regression trees on the queries of the training set minimized by the optimization function and stored for application to content item-query pairs not contained in the one or more training sets.

Abstract: Embodiments of the present invention provide for methods, systems and computer program products for learning ranking functions to determine the ranking of one or more content items that are responsive to a query. The present invention includes generating one or more training sets comprising one or more content item-query pairs, determining preference data for the one or more query-content item pairs of the one or more training sets and determining labeled data for the one or more query-content item pairs of the one or more training sets. A ranking function is determined based upon the preference data and the labeled data for the one or more content-item query pairs of the one or more training sets. The ranking function is then stored for application to query-content item pairs not contained in the one or more training sets.

Abstract: The present invention relates to systems and methods for determining a content item relevance function. The method comprises collecting user preference data at a search provider for storage in a user preference data store and collecting expert-judgment data at the search provider for storage in an expert sample data store. A modeling module trains a base model through the use of the expert-judgment data and tunes the base model through the use of the user preference data to learn a set of one or more tuned models. A measure (B measure) is designed to evaluate the balanced performance of tuned model over expert judgment and user preference. The modeling module generates or selects the content item relevance function from the tuned models with B measure as the selection criterion.

Abstract: A method and apparatus for determining a ranking function by regression using relative preference data. A number of iterations are performed in which to following is performed. The current ranking function is used to compare pairs of elements. The comparisons are checked against actual preference data to determine for which pairs the ranking function mis-predicted (contradicting pairs). A regression function is fitted to a set of training data that is based on contradicting pairs and a target value for each element. The target value for each element may be based on the value that the ranking function predicted for the other element in the pair. The ranking function for the next iteration is determined based, at least in part, on the regression function. The final ranking function is established based on the regression functions. For example, the final ranking function may be based on a linear combination of regression functions.

Abstract: The system and method of the present invention allows for the determination of the relevance of a content item to a query through the use of a machine learned relevance function that incorporates click-through features of the content items. A method for selecting a relevance function to determine a relevance of a query-content item pair comprises generating training set having one or more query-URL pairs labeled for relevance based on their click-through features. The labeled query-URL pairs are used to determine the relevance function by minimizing a loss function that accounts for click-through features of the content item. The computed relevance function is then applied to the click-though features of unlabeled content items to assign relevance scores thereto. An inverted click-through index of query-score pairs is formed and combined with the content index to improve relevance of search results.

Abstract: The system and method of the present invention allows for the determination of the relevance of a content item to a query through the use of a machine learned relevance function that incorporate query differentiation. A method for selecting a relevance function to determine a relevance of a query-content item pair comprises generating a training set comprising one or more content item-query pairs. Content item-query pairs in the training set are collectively used to determine the relevance function by minimizing a loss function according to a relevance score adjustment function that accounts for query differentiation. The monotocity of relevance score adjustment function allows the trained relevance function to be directly applied to new queries.