Abstract:
A social networking system receives information describing locations associated with a plurality of its users. Based on information identifying each user and a location associated with each user, the social networking system generates and stores hash values. For example, the social networking system maintains various geo-tiles that each identify geographic areas and generates a hash value based on a user identifier and an identifier of a geo-tile including the location associated with the user. Based on the hash values and locations associated with one or more users, the online system determines a number of unique users associated with locations included in a geographic region. When determining the number of unique users, the online system accounts for a rate at which the online system updates location information associated with various users.
Abstract:
An advertiser determines an attribution assigned to an online publisher for providing advertisement impressions to a user that purchased the product associated with the advertisement impressions. An event chain that resulted in a conversion by a user is received and a probability that the event chain would result in a conversion is determined. A probability that a second event chain that includes the events of the received event chain except for a target event, would result in a conversion is determined. A score for the target event is determined based on the probability that the received event chain would result in a conversion and the probability that the second event chain would result in a conversion.
Abstract:
An online system selects advertisements for a user based on characteristics of the user. The online system presents advertisements to the user having targeting criteria satisfied by the characteristics of the user. To increase the number of users eligible to be presented with an advertisement, the online system increases the users eligible to be presented with the advertisement to include users that do not meet targeting criteria included in the advertisement. The online system obtains a percentile of users based on a performance metric associated with the advertisement and determines a cutoff measure of affinity based on the percentile and measures of affinity between various users and the advertisement. A user is eligible to be presented with the advertisement if a measure of affinity between the user and the advertisement is greater than the cutoff measure of affinity for the advertisement.
Abstract:
An online system selects advertisements for presentation a user based on characteristics of the user. The online system monitors performance of advertisements based on a goal for the advertisement and a time interval for achieving the goal. During a time period within the time interval, the online system determines an actual performance of the advertisement and compares the actual performance to a portion of the goal associated with the time period. If the actual performance does not satisfy the portion of the goal associated with the time period, the online system expands targeting criteria of the advertisement to increase a number of users eligible to be presented with the advertisement.
Abstract:
An online system selects advertisements for a user based on characteristics of the user. The online system presents advertisements to the user having targeting criteria satisfied by the characteristics of the user. To increase the number of users eligible to be presented with an advertisement, the online system increases the users eligible to be presented with the advertisement to include users that do not meet targeting criteria included in the advertisement. The online system obtains a percentile of users based on a performance metric associated with the advertisement and determines a cutoff measure of affinity based on the percentile and measures of affinity between various users and the advertisement. A user is eligible to be presented with the advertisement if a measure of affinity between the user and the advertisement is greater than the cutoff measure of affinity for the advertisement.
Abstract:
A social networking system receives information describing locations associated with a plurality of its users. Based on information identifying each user and a location associated with each user, the social networking system generates and stores hash values. For example, the social networking system maintains various geo-tiles that each identify geographic areas and generates a hash value based on a user identifier and an identifier of a geo-tile including the location associated with the user. Based on the hash values and locations associated with one or more users, the online system determines a number of unique users associated with locations included in a geographic region. When determining the number of unique users, the online system accounts for a rate at which the online system updates location information associated with various users.
Abstract:
An online system selects advertisements for presentation a user based on characteristics of the user. The online system monitors performance of advertisements based on a goal for the advertisement and a time interval for achieving the goal. During a time period within the time interval, the online system determines an actual performance of the advertisement and compares the actual performance to a portion of the goal associated with the time period. If the actual performance does not satisfy the portion of the goal associated with the time period, the online system expands targeting criteria of the advertisement to increase a number of users eligible to be presented with the advertisement.
Abstract:
The disclosure is related to a framework that enables server-side controlling of data sampling at client devices. An application executing on a client device samples data related to various aspects of the application, generates a log file containing the sample data and transmits the log file to the server. The application samples the data based on specified criteria, e.g., specified events, specified actions of a user of the client device, at a specified sampling rate. The framework enables controlling the sampling of such data from the server. The framework can be used to configure various parameters of the sampling, including a number of users to be sampled, a set of events to be sampled, a sampling rate for the events, etc. After the configuration is determined, the server transmits a configuration file to the client device, which performs the sampling based on the configuration in the configuration file.
Abstract:
The disclosure is related to a framework that enables server-side controlling of data sampling at client devices. An application executing on a client device samples data related to various aspects of the application, generates a log file containing the sample data and transmits the log file to the server. The application samples the data based on specified criteria, e.g., specified events, specified actions of a user of the client device, at a specified sampling rate. The framework enables controlling the sampling of such data from the server. The framework can be used to configure various parameters of the sampling, including a number of users to be sampled, a set of events to be sampled, a sampling rate for the events, etc. After the configuration is determined, the server transmits a configuration file to the client device, which performs the sampling based on the configuration in the configuration file.
Abstract:
A social networking system receives information describing locations associated with a plurality of its users. Based on information identifying each user and a location associated with each user, the social networking system generates and stores hash values. For example, the social networking system maintains various geo-tiles that each identify geographic areas and generates a hash value based on a user identifier and an identifier of a geo-tile including the location associated with the user. Based on the hash values and locations associated with one or more users, the online system determines a number of unique users associated with locations included in a geographic region. When determining the number of unique users, the online system accounts for a rate at which the online system updates location information associated with various users.