Abstract: One or more techniques and/or systems are provided for implementing a pipeline used to generate, train, test, and implement a document scoring model for assigning document scores to documents. Features from various sources are combined to create a joined page level feature set, a joined domain level feature set, and a host level feature set. Numerical features and content features are extracted from ground truth documents and random documents. The numerical features are joined with the joined feature sets to create a set of joined features. The document scoring model is trained using the set of joined features and a training technique. A document is scored with a document score using the document scoring model based upon the content features and the set of joined features with document scores obtained during training.

Abstract: One or more techniques and/or systems are provided for implementing a pipeline used to generate, train, test, and implement a document scoring model for assigning document scores to documents. Features from various sources are combined to create a joined page level feature set, a joined domain level feature set, and a host level feature set. Numerical features and content features are extracted from ground truth documents and random documents. The numerical features are joined with the joined feature sets to create a set of joined features. The document scoring model is trained using the set of joined features and a training technique. A document is scored with a document score using the document scoring model based upon the content features and the set of joined features with document scores obtained during training.

Abstract: One or more techniques and/or systems are provided for implementing a pipeline used to generate, train, test, and implement a document scoring model for assigning document scores to documents. Features from various sources are combined to create a joined page level feature set, a joined domain level feature set, and a host level feature set. Numerical features and content features are extracted from ground truth documents and random documents. The numerical features are joined with the joined feature sets to create a set of joined features. The document scoring model is trained using the set of joined features and a training technique. A document is scored with a document score using the document scoring model based upon the content features and the set of joined features with document scores obtained during training.

Abstract: A search engine for finding objects that correspond to a search request, including an input module for receiving a keyword query from a user, and a search module being configured to map the keyword query to the identifiers of objects that semantically match the keyword or the plurality of keywords contained in the keyword query, and to generate a search result that contains a listing of matching object identifiers, is characterized in that the search module is further configured to generate the search result by considering network layer information about the user within the process of mapping the keyword query to identifiers of matching objects, wherein the network layer information include sophisticated information the search module receives from a dedicated entity.

Abstract: Disclosed herein is parallel processing of a query, which uses inter-query parallelism in posting list intersections. A plurality of tasks, e.g., posting list intersection tasks, are identified for processing in parallel by a plurality of processing units, e.g., a plurality of processing cores of a multi-core system.

Abstract: A search engine for finding objects that correspond to a search request, including an input module for receiving a keyword query from a user, and a search module being configured to map the keyword query to the identifiers of objects that semantically match the keyword or the plurality of keywords contained in the keyword query, and to generate a search result that contains a listing of matching object identifiers, is characterized in that the search module is further configured to generate the search result by considering network layer information about the user within the process of mapping the keyword query to identifiers of matching objects, wherein the network layer information include sophisticated information the search module receives from a dedicated entity.

Abstract: Disclosed herein is parallel processing of a query, which uses inter-query parallelism in posting list intersections. A plurality of tasks, e.g., posting list intersection tasks, are identified for processing in parallel by a plurality of processing units, e.g., a plurality of processing cores of a multi-core system.