Facebook, Inc.

Meta Platforms, Inc.

Zipsocket, Inc.

Gianluca Iannaccone

Jacob Andrew Brill

Matthew Hural

David Townsend Martin

Francisco Leonardo Franco Gonzalez

Sarah Rotman Epps

Han Qin

Jane Francis

Ali Rahimi

Content presented to a user of an online system is presented along with an option allowing the user to view one or more reasons for presentation of the advertisement content to the user and one or more characteristics of the user satisfying targeting criteria associated with the content. For example, a description identifying one or more selected characteristics of the user satisfying the targeting criteria is presented along with the content. The online system selects the one or more characteristics presented along with the content by applying one or more rules to characteristics of the user satisfying targeting criteria included in the content. The rules may account for accuracy of models used to determine whether the user has characteristics, revenue received by the online system from presenting content associated with targeting criteria identifying various characteristics, or prevalence of characteristics among users or targeting criteria of content.

Identifying characteristics used for content selection by an online system to a user for user modification

An online system identifies seed users with a high value score to a third party system. The online system identifies features of each of the seed users. A weight for each of the identified features is identified. The identified features are divided into a plurality of buckets, each bucket indicating a property associated with one or more of the identified features. Each bucket is ranked according to the weights of the identified features in each bucket. The online system identifies an additional user that has a threshold measure of similarity the seed users. The online system transmits a content item to the additional user for presentation. Additionally, the online system transmits one or more third party-presentable factors based on the bucket having the highest rank to the third party system, the third party-presentable factors indicating a reason as to why the additional user was presented with the content item.

Computing a ranked feature list for content distribution in a first categorization stage and second ranking stage via machine learning

Advertisement content presented to a user of an online system is presented along with an option allowing the user to view one or more reasons for presentation of the advertisement content to the user. If the user selects the option, based on targeting criteria associated with the advertisement content and characteristics associated with the user, the online system selects one or more characteristics of the user satisfying the targeting criteria and presents a description to the user identifying the selected one or more characteristics satisfying the targeting criteria. The online system may apply one or more rules based on user perception of characteristics when selecting the one or more characteristics included in the description. An option for the user to prevent use of the selected one or more characteristics when the online system subsequently selects advertisement content for the user is also presented with the description.

User modification of characteristics used for content selection by an online system

An online system propagates settings from a user's client device to the online system itself for the user. If multiple users of the online system use the client device, the online system propagates settings of a primary user of the client device to the online system, but does not propagate settings of other users of the client device to the online system. The online system logs accesses to the online system each day from the client device of each user. A user having a maximum number of logged days accessing the system and accessing the online system for at least 50% of the logged days is determined to be the primary user by the online system. The online system may use determination that a user is a primary user of the client device for associating actions with the user, selecting content for the user, and identifying the user.

Determining a primary user of a client device and propagating settings of the primary user from the client device to an online system

An online system receives a content item from content providers and a target audience criterion and for targeting the received content item. The online system determines recommendations of modifications of the target audience criteria such that users of the modified target audience criteria have a higher likelihood of interacting with the content item compared to the received target audience criteria. The online system stores measures of interest of subsets of users for different topics. The online system determines topics associated with the content item and determines measures of interest of subsets of users in the content item based on topics associated with the content item. The online system provides recommendations for modifying the target audience criteria based on the determined measures of interests of users.

Determining criteria for selecting target audience for content

A social networking system receives a sponsored story request identifying a story describing an action performed by a social networking system user and generates a sponsored story accordingly. The sponsored story request may also include a bid amount and other information for generating a sponsored story from the sponsored story request. When an action is included in a sponsored story presented to a social networking system user, the social networking system notifies the user associated with the action and provides one or more settings regulating inclusion of actions associated with the user in sponsored story requests. For example, settings allow a user to prevent generation of sponsored stories including a specified type of action, a specific action, or multiple actions associated with the user. Hence, the settings allow a user to limit or prevent generation of sponsored stories describing actions associated with the user.

User-specified distribution of stories describing user actions in a social networking system

When two or more devices are near each other, they are likely to hear the same ambient sounds. To detect such proximity, each device captures a short digital audio signal from its microphone and uploads it to a proximity-detecting service along with a timestamp that indicates when the clip was captured. The proximity-detecting service maintains a history of sound clips for its clients and analyzes recently received sound clips that have similar time stamps. The devices can then be grouped according to the similarities of their sound clips. Devices that are grouped together in this way are deemed to be near each other.

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Oakland, CA, United States of America

Gianluca Iannaccone