Abstract: A method for distributed processing involves receiving a graph (G) of targets and of influencers, with each influencer related to at least one target, receiving an action graph of actions performed by one or more of the influencers, and key partitioning G across shards. The method further involves transposing the first graph (G) to obtain a first transposed graph (GT), value partitioning GT across the shards, storing the action graph on multiple shards, issuing, to a shard, a request specifying an influencer, to perform an intersection, receiving a response to the request of a set of influencers each of which is related to a target, and determining whether to send a recommendation to the target based on the response.

Abstract: A method for distributed processing involves receiving a graph (G) of targets and of influencers, with each influencer related to at least one target, receiving an action graph of actions performed by one or more of the influencers, and key partitioning G across shards. The method further involves transposing the first graph (G) to obtain a first transposed graph (GT), value partitioning GT across the shards, storing the action graph on multiple shards, issuing, to a shard, a request specifying an influencer, to perform an intersection, receiving a response to the request of a set of influencers each of which is related to a target, and determining whether to send a recommendation to the target based on the response.

Abstract: Systems and methods for providing content associated with trending topics relevant to a user are described. An interest detection module identifies topics trending in one or more real-time content sources that are relevant to a user. The real-time content source(s) may include, for example, a source of microblog posts or other user-generated data, a news feed, or the like. The topics trending that are relevant to the user are identified by comparing a search history of the user with one or more trending topics stored in a database. A content retriever module is configured to return one or more documents to the user in response to identifying the one or more topics trending that are relevant to the user.

Abstract: Each user is represented by a mixture of topics, e.g., one or more topics, and a probability of interest in each topic in the mixture, and given the target user, one or more other users can be recommended, each user that is recommended to the target user is determined to have a topical interest similarity with the target user, e.g., the target user's interest in one or more topics of the mixtures of topics is determined to be similar to a recommended interest in the one or more topics of the mixture of topics. The target user and the one or more recommended users can be said to have similar topical interests. The target user can use the user recommendation to establish an interactive dialogue, for example, with one or more users identified in the user recommendation.

Abstract: A method for distributed processing involves receiving a graph (G) of targets and of influencers, with each influencer related to at least one target, receiving an action graph of actions performed by one or more of the influencers, and key partitioning G across shards. The method further involves transposing the first graph (G) to obtain a first transposed graph (GT), valuing partitioning GT across the shards, storing the action graph on multiple shards, issuing, to a shard, a request specifying an influencer, to perform an intersection, receiving a response to the request of a set of influencers each of which is related to a target, and determining whether to send a recommendation to the target based on the response.

Abstract: A method for distributed processing involves receiving a graph (G) of targets and of influencers, with each influencer related to at least one target, receiving an action graph of actions performed by one or more of the influencers, and key partitioning G across shards. The method further involves transposing the first graph (G) to obtain a first transposed graph (GT), valuing partitioning GT across the shards, storing the action graph on multiple shards, issuing, to a shard, a request specifying an influencer, to perform an intersection, receiving a response to the request of a set of influencers each of which is related to a target, and determining whether to send a recommendation to the target based on the response.

Abstract: A method for distributed processing involves receiving a graph (G) of targets and of influencers, with each influencer related to at least one target, receiving an action graph of actions performed by one or more of the influencers, and key partitioning G across shards. The method further involves transposing the first graph (G) to obtain a first transposed graph (GT), valuing partitioning GT across the shards, storing the action graph on multiple shards, issuing, to a shard, a request specifying an influencer, to perform an intersection, receiving a response to the request of a set of influencers each of which is related to a target, and determining whether to send a recommendation to the target based on the response.

Abstract: Disclosed herein is use of a preview of content from a target document, as provided by a content preview source such as a Really Simple Syndication (RSS) feed, by a search engine. The content preview source includes the preview of the target document's content and a reference, e.g., a Universal Resource Locator (URL) or other link. A content preview document is generated using data extracted from the content preview source. The content preview document is made available in a searchable index used by a search engine to respond to a search query. A fetch operation is scheduled to fetch the target document using the reference provided in the content preview source. Once fetched, the data extracted from the content preview source can be associated with the target document, and can be used in presenting the target document in search results.

Abstract: A method for distributed processing involves receiving a graph (G) of targets and of influencers, with each influencer related to at least one target, receiving an action graph of actions performed by one or more of the influencers, and key partitioning G across shards. The method further involves transposing the first graph (G) to obtain a first transposed graph (GT), valuing partitioning GT across the shards, storing the action graph on multiple shards, issuing, to a shard, a request specifying an influencer, to perform an intersection, receiving a response to the request of a set of influencers each of which is related to a target, and determining whether to send a recommendation to the target based on the response.

Abstract: Disclosed herein is use of a preview of content from a target document, as provided by a content preview source such as a Really Simple Syndication (RSS) feed, by a search engine. The content preview source includes the preview of the target document's content and a reference, e.g., a Universal Resource Locator (URL) or other link. A content preview document is generated using data extracted from the content preview source. The content preview document is made available in a searchable index used by a search engine to respond to a search query. A fetch operation is scheduled to fetch the target document using the reference provided in the content preview source. Once fetched, the data extracted from the content preview source can be associated with the target document, and can be used in presenting the target document in search results.

Abstract: Each user is represented by a mixture of topics, e.g., one or more topics, and a probability of interest in each topic in the mixture, and given the target user, one or more other users can be recommended, each user that is recommended to the target user is determined to have a topical interest similarity with the target user, e.g., the target user's interest in one or more topics of the mixtures of topics is determined to be similar to a recommended interest in the one or more topics of the mixture of topics. The target user and the one or more recommended users can be said to have similar topical interests. The target user can use the user recommendation to establish an interactive dialogue, for example, with one or more users identified in the user recommendation.

Abstract: Disclosed herein is use of a preview of content from a target document, as provided by a content preview source such as a Really Simple Syndication (RSS) feed, by a search engine. The content preview source includes the preview of the target document's content and a reference, e.g., a Universal Resource Locator (URL) or other link. A content preview document is generated using data extracted from the content preview source. The content preview document is made available in a searchable index used by a search engine to respond to a search query. A fetch operation is scheduled to fetch the target document using the reference provided in the content preview source. Once fetched, the data extracted from the content preview source can be associated with the target document, and can be used in presenting the target document in search results.

Abstract: Each user is represented by a mixture of topics, e.g., one or more topics, and a probability of interest in each topic in the mixture, and given the target user, one or more other users can be recommended, each user that is recommended to the target user is determined to have a topical interest similarity with the target user, e.g., the target user's interest in one or more topics of the mixtures of topics is determined to be similar to a recommended interest in the one or more topics of the mixture of topics. The target user and the one or more recommended users can be said to have similar topical interests. The target user can use the user recommendation to establish an interactive dialogue, for example, with one or more users identified in the user recommendation.

Abstract: A method for distributed processing involves receiving a graph (G) of targets and of influencers, with each influencer related to at least one target, receiving an action graph of actions performed by one or more of the influencers, and key partitioning G across shards. The method further involves transposing the first graph (G) to obtain a first transposed graph (GT), valuing partitioning GT across the shards, storing the action graph on multiple shards, issuing, to a shard, a request specifying an influencer, to perform an intersection, receiving a response to the request of a set of influencers each of which is related to a target, and determining whether to send a recommendation to the target based on the response.

Abstract: Systems and methods for identifying candidate queries related to a trending topic based on a user query are described. A trending topic identification module identifies topics trending in one or more real-time content sources. The real-time content source(s) may include, for example, a source of microblog posts or other user-generated data, a news feed, or the like. A query recommendation module suggests at least one candidate query in response to receiving a user query. The query recommendation module obtains the at least one candidate query by comparing words and named entities of the user query with words and named entities associated with the trending topics identified by the trending topic identification module.

Abstract: Each user is represented by a mixture of topics, e.g., one or more topics, and a probability of interest in each topic in the mixture, and given the target user, one or more other users can be recommended, each user that is recommended to the target user is determined to have a topical interest similarity with the target user, e.g., the target user's interest in one or more topics of the mixtures of topics is determined to be similar to a recommended interest in the one or more topics of the mixture of topics. The target user and the one or more recommended users can be said to have similar topical interests. The target user can use the user recommendation to establish an interactive dialogue, for example, with one or more users identified in the user recommendation.

Abstract: Systems and methods for providing content associated with trending topics relevant to a user are described. An interest detection module identifies topics trending in one or more real-time content sources that are relevant to a user. The real-time content source(s) may include, for example, a source of microblog posts or other user-generated data, a news feed, or the like. The topics trending that are relevant to the user are identified by comparing a search history of the user with one or more trending topics stored in a database. A content retriever module is configured to return one or more documents to the user in response to identifying the one or more topics trending that are relevant to the user.

Abstract: Systems and methods for identifying candidate queries related to a trending topic based on a user query are described. A trending topic identification module identifies topics trending in one or more real-time content sources. The real-time content source(s) may include, for example, a source of microblog posts or other user-generated data, a news feed, or the like. A query recommendation module suggests at least one candidate query in response to receiving a user query. The query recommendation module obtains the at least one candidate query by comparing words and named entities of the user query with words and named entities associated with the trending topics identified by the trending topic identification module.

Abstract: Disclosed herein is use of a preview of content from a target document, as provided by a content preview source such as a Really Simple Syndication (RSS) feed, by a search engine. The content preview source includes the preview of the target document's content and a reference, e.g., a Universal Resource Locator (URL) or other link. A content preview document is generated using data extracted from the content preview source. The content preview document is made available in a searchable index used by a search engine to respond to a search query. A fetch operation is scheduled to fetch the target document using the reference provided in the content preview source. Once fetched, the data extracted from the content preview source can be associated with the target document, and can be used in presenting the target document in search results.