Abstract:
A method for retrieving information includes receiving a search query within an information corpus. Search results for the search query may be identified. A score for each of a plurality of data items identified in the search results may be generated. The score for a corresponding one of the plurality of data items may be based on a score dependent on the search query within the information corpus. The score may be also based on at least one score independent of the search query. The at least one score independent of the search query may include a ranking signal associated with a World Wide Web (WWW) search of the corresponding one of the plurality of data items using a second information corpus. The search results may be ranked based on the generated score.
Abstract:
A method for retrieving information may include receiving, in a non-World Wide Web (WWW) corpus, a search query for a media author. Search results for the search query may be identified within the non-WWW corpus. A score for each of a plurality of media authors identified in the search results may be generated. The score for a corresponding one of the plurality of media authors may be based on a combined media popularity score for a plurality of media items authored by the corresponding one of the plurality of media authors. The search results may be ranked based on the generated score for each of the plurality of media authors.
Abstract:
Generating and selecting recommendation explanations for personalized recommendations may include retrieving in response to at least one recommendation query, a document from a corpora of available documents for consumption by a user. The at least one recommendation query may be associated with a corresponding plurality of candidate recommendation explanations. The plurality of recommendation explanations for the document may be ranked based on popularity of at least one of the plurality of recommendation explanations when previously provided to the user and/or popularity of the document among a plurality of users under each of the plurality of recommendation explanations. The popularity of at least one of the plurality of recommendation explanations previously provided to the user may be based on document engagement history associated with the user when the at least one of the plurality of recommendation explanations were previously provided to the user.
Abstract:
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for referenced access control lists. In one aspect, a method includes accessing an object hierarchy for a plurality of objects, each object being representative of one of a storage location or a file. The object hierarchy includes for each object, a respective node, for each object that is a parent object having a child object, a directed edge connecting the node representing the parent object. In addition, for each object, including metadata that includes an access control list identifier that identifies an access control list for the object and that is owned by an access control list root object. The method including receiving updates to an access control list for particular objects, generating a new access control list, and storing the new access control list identifier in metadata for each object that descends from the particular object.
Abstract:
Generating and selecting recommendation explanations for personalized recommendations may include retrieving in response to at least one recommendation query, a document from a corpora of available documents for consumption by a user. The at least one recommendation query may be associated with a corresponding plurality of candidate recommendation explanations. The plurality of recommendation explanations for the document may be ranked based on popularity of at least one of the plurality of recommendation explanations when previously provided to the user and/or popularity of the document among a plurality of users under each of the plurality of recommendation explanations. The popularity of at least one of the plurality of recommendation explanations previously provided to the user may be based on document engagement history associated with the user when the at least one of the plurality of recommendation explanations were previously provided to the user.
Abstract:
Systems and method are disclosed personalizing search results. An example method for personalizing search results may include receiving from a user, a search query for a media item, identifying search results for the search query, and generating a score for each of a plurality of media items identified in the search results. The score for a corresponding one of the plurality of media items may be based on the search query and one or both of a personalized query independent score and/or a personalized query dependent score. The at least one personalized query independent and query dependent scores may be based on at least one media preference signal associated with the user. The search results may be ranked based on the generated score for each of the plurality of media items.
Abstract:
A method for retrieving information includes receiving a search query within an information corpus. Search results for the search query may be identified. A score for each of a plurality of data items identified in the search results may be generated. The score for a corresponding one of the plurality of data items may be based on a score dependent on the search query within the information corpus. The score may be also based on at least one score independent of the search query. The at least one score independent of the search query may include a ranking signal associated with a World Wide Web (WWW) search of the corresponding one of the plurality of data items using a second information corpus. The search results may be ranked based on the generated score.
Abstract:
A system and/or method is provided for using a scatter gather information retrieval system for live recommendation generation. The method may include retrieving user information classified in a plurality of categories. For at least one of the plurality of categories, a document recommendation query may be generated based on the user information classified in a corresponding one of the plurality of categories. For each generated recommendation query, a plurality of documents satisfying the recommendation query may be retrieved from a corpus of documents. The corpus may classify a plurality of documents of a determined type available for consumption by the user. Each retrieved plurality of documents may be ranked to generate a final list of recommendations for the user. Each of the plurality of documents may include identifying information for a book, a song, a video, a movie, a music album, an application, and/or a TV show.
Abstract:
A method for retrieving information may include receiving, in a non-World Wide Web (WWW) corpus, a search query for a media author. Search results for the search query may be identified within the non-WWW corpus. A score for each of a plurality of media authors identified in the search results may be generated. The score for a corresponding one of the plurality of media authors may be based on a combined media popularity score for a plurality of media items authored by the corresponding one of the plurality of media authors. The search results may be ranked based on the generated score for each of the plurality of media authors.