Google Inc.

Mikhail Lopyrev

Zoltan Gyongyi

Maureen Heymans

Matthew E Kulick

Hein Roehrig

Viswanath Subramanian

Jeffrey Korn

Hyung-Jin Kim

Andrei Lopatenko

Sandor Dornbush

Timothy Kilbourn

Zachary Paul Maier

Radhika Lakshmanan

Leonard Wei

David Weissman

Richard Oliver Frankel

Doug DeCarlo

Julia Kodysh

Catherine Ye

Systems and methods for generating a viewer-specific visitor history for a location are provided. In particular, a visitor history for a particular viewer and location can indicate a total number of users who have visited the location and can specifically identify one or more of a set of users using a facepile. The set of specifically identified users can be those users that have an associative link with the viewer and that are also associated with at least one location signal for the given location and for which the particular viewer is a permitted viewer. Thus, location signals having varying scopes of visibility information can be aggregated across a plurality of different web-services to provide a single, unified visitor history for a particular location and viewer, all while respecting user-specified visibility information.

Systems and methods for generating a viewer-specific visitor history for a location

This specification describes technologies relating to searching. In general, aspects of the subject matter described in this specification can be embodied in methods that include the actions of receiving, at a computing device, a search query from a user, receiving, at the computing device, search results responsive to the search query, the search results each associated with digital content stored in one or more computer-readable storage media, determining that a first search result of the search results is associated with a first endorsement generated by a first user, determining that the first user is a member of a social graph of the user, and transmitting instructions to display the search results to the user, the instructions including instructions to display a first endorsement annotation associated with the first search result.

Social search endorsements

Techniques include: identifying one or more first entities who have connections to more than a threshold number of other entities; identifying first content in the search index that is authored by the first entities; and associating author restricts with the first content, where the author restricts include data identifying entities who authored corresponding content. The techniques may also include: identifying one or more second entities who have social connections to one or more entities who authored second content, where the one or more second entities have connections to less than the threshold number other entities; associating searcher restricts with the second content; and providing the search index updated with searcher and author restricts.

Searching using access controls

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for social identity clustering. In one aspect, a method includes receiving a connection graph representing public social data, where the nodes represent social identities and each edge is either a 'me' edge between identities claimed to belong to the same person or a 'friend' edge between identities claimed to belong to different identities. The method further includes converting the graph to a cluster graph in which each node initially corresponds to a single node of the connection graph. The method further includes updating the cluster graph by iteratively merging cluster nodes based on an analysis of the weight of the &#x201c;me&#x201d; edges connecting them, and then replacing the merged cluster nodes within the graph with a new cluster node containing the merged cluster nodes, where the edges of the new cluster node are the aggregated edges of the merged cluster nodes.

Social identity clustering

In general, in one aspect, embodiments of the invention feature a computer-implemented method for providing input to a document ranking process for ranking a plurality of documents, the document ranking process taking as input a quality of result statistic for an individual document in the plurality of documents, the plurality of documents having been identified in response to a query, the quality of results statistic being for the query and the individual document. The method for providing input can include, for a first document identified as a search result of a user-submitted query, scoring one or more other queries based upon a similarity to the user-submitted query, each of the one or more other queries being different from the user-submitted query.

Ranking search results based on similar queries

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San Francisco, CA, United States of America

Mikhail Lopyrev