Facebook, Inc.

Pinterest, Inc.

Microsoft Technology Licensing, LLC

Mukund Narasimhan

Pierre Moreels

Xiaoliang Wei

Lance Alan Riedel

Paul A Viola

Cameron Marlow

Jonathan D Chang

Michael Dudley Johnson

Jeffrey Scott Dunn

Robert William Cathcart

Herry Sutanto

Venkataramanan Nandagopal

Joshua A Redstone

Yan Yu

Yuntao Jia

Sonja Sabina Knoll

Chad E Little

Yuankai Ge

Vitaliy Kulikov

Yingyi Liang

Itamar Rosenn

Yu Liu

Alex Bain

Chao Wang

Weizhe Shi

Siyang Xie

Todd Krabach

John Milinovich

Andrew Look

Andrew Liu Chen

Haomin Yu

Eric Ruth

Lior Berry

Cole Timothy Rottweiler

Hsueh-Fang Cheng

Raymond Hsu

Jiacheng Hong

Ita Pai

Bo Huai Victor Loh

Zheng Liu

Sara Motiee

Jackson Gabbard

Michael Shilman

Brian W Johnson

Brian Johnson

Gregory Druck

Stephanie deWet

Jiafan Ou

Jian Huang

According to aspects of the disclosed subject matter, a taste graph comprising likely content collection nodes with corresponding likely digital content items is generated through one or more analyses of a corpus of content collections that is maintained by the online content service. As should be understood, this corpus of content collections is comprised of a plurality of curated content collections, with each content collection comprising a plurality of digital content items. With this taste graph available, as a user generates (or in response to a user generating) a content collection of digital content items, reference can be made to the taste graph to identify one or more digital content items that may be added to the content collection, where the one or more digital content items have a probabilistic likelihood of being complimentary and/or compatible with the other digital content items of the content collection.

Probabilistic determination of compatible content

The described implementations are operable to determine potential objects of interest to a user based on a blend of the user's long-term behavior and short-term interests. Long term user behavior may be determined for the user over a period of time and represented as continuous data. Short-term interest may be determined based on objects with which the user has recently interacted, and attributes of those objects may be represented together as continuous data corresponding to the short-term user interest. The continuous data of the short-term user interest and long-term user behavior may be blended to produce a user embedding. The user embedding may then be compared with objects to determine objects that are of potential interest to the user.

Object identification based on long-term user behavior and short-term interest

Systems and methods are presented for carrying out a task to an expanded group of users on behalf of a third party. In operation, an online service receives task information corresponding to a task from a third party. Additionally, the online service receives a seed group of users from the third party. The online service determines an intersection between the seed group of users and the online service's corpus of users. This intersection of users is then used to identify an expanded set of users of the online service's corpus of users. The online service then carries out the task on behalf of the third party, targeting the expanded set of users.

Audience expansion according to user behaviors

The described implementations are operable to determine potential objects of interest to a user based on a blend of the user's long-term behavior and short-term interests. Long term user behavior may be determined for the user over a period of time and represented as continuous data. Short-term interest may be determined based on objects with which the user has recently interacted and attributes of those objects may be represented together as continuous data corresponding to the short-term user interest. The continuous data of the short-term interest and long-term user behavior may be blended to produce a user embedding. The user embedding may then be compared with objects to determine objects that are of potential interest to the user.

In one embodiment, a method includes sending a first request comprising an http-header that includes one or more http-header fields for a webpage from a first user to a computing server, receiving a second request for access to a first information associated with the mobile-client system from the computing server in response to the first request, wherein the first information comprises one or more user-profile fields associated with a user profile of the first user, sending the first information responsive to the second request to the computing server, and receiving the requested webpage customized based on one or more determined attributes of the mobile-client system from the computing server, the one or more attributes being determined based on a comparison between the one or more http-header fields and the one or more user-profile fields with a database comprising attributes of a plurality of types of mobile-client systems.

Detecting mobile device attributes

In one embodiment, one or more computing systems receive a request for a location prediction for a user from a service. The computing systems access one or more real-time location signals and one or more aggregated location signals. The aggregated location signals may comprise one or more previous location signals. The computing systems may then generate one or more location predictions from the one or more real-time location signals and the one or more aggregated location signals, and calculate a single location prediction for the user from the one or more location predictions. The computing systems may then send, in response to the request, the single location prediction for the user to the requesting service.

Implicit geolocation of social networking users

In one embodiment, a computing device accesses a social graph comprising nodes and edges connecting the nodes. Each of the edges between two of the nodes represents a single degree of separation between them. The nodes include user nodes corresponding to users of an online social network, and concept nodes corresponding to places. A particular place corresponds to a particular concept node, and corresponds to an associated location and a perimeter. A number of check-ins are received, each check-in being associated with the particular place and having a geographic location. Each check-in corresponds to an edge of the social graph between a user node and the particular concept node. Based on the received check-ins the computing device determines whether to update the associated location and the perimeter.

Determining a location and area of a place

In one embodiment, a method includes, by one or more server computing devices, receiving state data of a client computing device. The state data includes event data indicating events generated by or occurring at the client computing device and context data associated with the event data. The context data indicates device states of the client computing device that each coincide with one or more of the events and indicate a context of the one or more of the events. The method also includes, by one or more server computing devices, ordering the events and the device states in the event and context data into a state-data-review structure and analyzing the state-data-review structure to generate one or more recommendations on operation of the client computing device.

Device state capture and analysis

A social networking system leverages user's social information to evaluate content submitted for inclusion in objects. If the evaluated submission is accepted, the submission is added to the content of an object. Accepted submissions are also used to predict associations between metadata and objects. Metadata is used to predict which objects will match user searches for information. The social networking system also provides a user interface configured to prompt users to submit information to objects. When a user completes a submission to an object, the user is provided with other options for groups of objects to contribute to. The objects offered are chosen to increase the likelihood that the user will choose to provide submissions to one of the provided objects.

Metadata prediction of objects in a social networking system using crowd sourcing

In one embodiment, a method includes receiving a request for a webpage from a mobile-client system of a user, where the request includes an http-header, accessing information describing the user, determining the attributes of the mobile-client system based on the http-header and the information describing the user, and transmitting the webpage to the mobile-client system in response to the request, where the webpage has been customized based on the determined attributes of the mobile-client system.

In one embodiment, a method includes calculating a first mean of check-in locations associated with a place; selecting a subset of the check-in locations based on distances between the first mean and the check-in locations; and determining a central location and at least a portion of a perimeter of the place based on the subset of the check-in locations.

Determining a location and area of a place based on distances between the first mean and check in locations

Growing community of inventors

Bellevue, WA, United States of America

Mukund Narasimhan