Google Inc.

Sebastian Dörner

Matthew Nicholas Stuttle

Alexandra Gherghina

Mat Balez

Hassan Mansoor

Tyrone Nicholas

Christian Brunschen

Federico Asara

Evelyn Kim

Samreen Dhillon

Mathieu Balez

Joanna Franciszka Swietlicka

Richard Michael Farrell

Provided are methods and computer-readable media for providing recommended entities based on a user's external social graph, such as asymmetric social graph of a social networking service. In some embodiments, entities responsive to a search query or other request may be obtained. Each entity may be evaluated to determine if the entity is associated with a contact from a user's social graph. The association may include an evaluation (e.g., a rating, review, other evaluation or combination thereof) of the entity by the contact. Additionally, the contacts having associations with an entity may be ranked based on a relationship score with a user. The entities having associations with the contacts from a user's social graph may be provided as recommended entities to the user, and the association may be annotated to the recommended entity for viewing by the user.

Method and computer-readable media for providing recommended entities based on a user's social graph

A group recommendation provides end users in a social group a set of recommended destinations based on the combined personal preferences of the members of the social group. Members of a social group are identified using a combination of location based signals and social graph information in response to receiving a recommendation request. The group recommendation may be determined by combining the personal preferences associated with each member of the group into a master preference profile. Alternatively, the group recommendation may be determined by first calculating an individual recommendation list for each member of the social group and then calculating a composite score for each recommendation on the individual recommendation lists.

Social where next suggestion

A system and method for providing destination recommendations to a user based on mining of a location history. The location history provides information on locations visited by users of the system over time. The system mines the location history to identify location pairs. Each location pair comprises an originating location visited by a user and a next location that was visited by the user within a defined time threshold of visiting the originating location. The system aggregates location pairs by common originating location. The system then takes into account the current location of a requesting user. The system uses the current location information to identify a corresponding originating location in a location history index. The system provides recommendations based at least in part on how frequently certain next locations were visited after visiting the originating location.

Where to go next via mined trails

Methods and computer-readable media for providing recommended entities based on a user's social graph

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London, United Kingdom

Sebastian Dörner