Abstract: A computer-implemented method for annotating online content with offline interaction data and offline interaction conversion data is implemented by a content annotating computing device in communication with a memory. The method includes receiving a set of content interaction data based on an online interaction between an online user and a first online content item, identifying at least one physical location associated with the online content provider, determining a set of offline interaction conversion data based on the set of content interaction data and a set of offline interaction data, receiving a second online content item associated with the online content provider, and annotating the second online content item based at least partially on the set of offline interaction conversion data and the set of offline interaction data.

Abstract: A computer-implemented method for annotating online content with offline interaction data and offline interaction conversion data is implemented by a content annotating computing device in communication with a memory. The method includes receiving a set of content interaction data based on an online interaction between an online user and a first online content item, identifying at least one physical location associated with the online content provider, determining a set of offline interaction conversion data based on the set of content interaction data and a set of offline interaction data, receiving a second online content item associated with the online content provider, and annotating the second online content item based at least partially on the set of offline interaction conversion data and the set of offline interaction data.

Abstract: Systems and methods of matching identifiers between multiple datasets are described herein. A system can transmit a first identifier vector to a third party server. The first identifier vector can include a first identifier, first parameters, and second parameters. The system can receive, from the third party server, the first identifier vector encrypted based on a third-party encryption. The system can receive, from the third party server, a second identifier vector encrypted based on the third-party encryption associated with the third party server. The second identifier vector can include a second identifier, third parameters, and fourth parameters. The system can determine a correlation count between the first identifier vector and the second identifier vector. The system can determine that the first identifier corresponds to the second identifier based on the correlation count. The system can generate one identifier key for both the first identifier and the second identifier.

Abstract: Systems and methods of matching identifiers between multiple datasets are described herein. A system can transmit a first identifier vector to a third party server. The first identifier vector can include a first identifier, first parameters, and second parameters. The system can receive, from the third party server, the first identifier vector encrypted based on a third-party encryption. The system can receive, from the third party server, a second identifier vector encrypted based on the third-party encryption associated with the third party server. The second identifier vector can include a second identifier, third parameters, and fourth parameters. The system can determine a correlation count between the first identifier vector and the second identifier vector. The system can determine that the first identifier corresponds to the second identifier based on the correlation count. The system can generate one identifier key for both the first identifier and the second identifier.

Abstract: Methods, systems, and apparatus include computer programs encoded on a computer-readable storage medium, including a method for providing content. Data specifying a post-install activity is received from a provider of an application. An opportunity is identified to provide third-party content to a user. A likelihood is determined that the user will perform the specified post-install activity based on one or more attributes of the user and attributes of users that have previously performed the specified post-install activity in the application. A selection value is adjusted for third-party content that identifies the application based on the determined likelihood, wherein the selection value increases as the likelihood increases. The third-party content identifying the application is selected based on the adjusted selection value. The third-party content identifying the application is distributed to a client device of the user.

Abstract: Aggregated transaction data from a transaction data provider may be encrypted and exchanged with a content item selection system using commutative encryption algorithms. The transaction data provider and content item selection system may utilize a set of common identifiers that are each encrypted using a respective commutative encryption algorithm of the transaction data provider or content item selection system. The other of the transaction data provider or content item selection system encrypts the single-encrypted common identifier using a respective commutative encryption algorithm to generate double encrypted common identifiers. The double encrypted common identifiers may be used to match a set of common identifiers with transaction data. The transaction data may be encrypted and/or may include random offset values.

Abstract: Methods, systems, and apparatus include computer programs encoded on a computer-readable storage medium, including a method for providing content. Data specifying a post-install activity is received from a provider of an application. An opportunity is identified to provide third-party content to a user. A likelihood is determined that the user will perform the specified post-install activity based on one or more attributes of the user and attributes of users that have previously performed the specified post-install activity in the application. A selection value is adjusted for third-party content that identifies the application based on the determined likelihood, wherein the selection value increases as the likelihood increases. The third-party content identifying the application is selected based on the adjusted selection value. The third-party content identifying the application is distributed to a client device of the user.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for optimizing digital components. In one aspect, the system includes a digital component server that identifies a digital component to be presented in an electronic document. The system also includes a digital component distribution server that interacts with the digital component server to determine that the electronic document includes a particular item that identifies a same entity as the digital component that was identified to be presented in the electronic document. The system combines at least a portion of the digital component with content from the particular item to form an optimized digital component. The optimized digital component is integrated into the electronic document rather than presenting either of the particular item or the digital component individually.

Abstract: A computer-implemented method for annotating online content with offline interaction data and offline interaction conversion data is implemented by a content annotating computing device in communication with a memory. The method includes receiving a set of content interaction data based on an online interaction between an online user and a first online content item, identifying at least one physical location associated with the online content provider, determining a set of offline interaction conversion data based on the set of content interaction data and a set of offline interaction data, receiving a second online content item associated with the online content provider, and annotating the second online content item based at least partially on the set of offline interaction conversion data and the set of offline interaction data.

Abstract: Methods, systems, and apparatus include computer programs encoded on a computer-readable storage medium, including a method for providing content. Data specifying a post-install activity is received from a provider of an application. An opportunity is identified to provide third-party content to a user. A likelihood is determined that the user will perform the specified post-install activity based on one or more attributes of the user and attributes of users that have previously performed the specified post-install activity in the application. A selection value is adjusted for third-party content that identifies the application based on the determined likelihood, wherein the selection value increases as the likelihood increases. The third-party content identifying the application is selected based on the adjusted selection value. The third-party content identifying the application is distributed to a client device of the user.

Abstract: Matching conversions from within applications on a client device to interactions with content items presented with resources may include receiving first data associated with an interaction with a content item displayed with a resource. The first data includes an application identifier for an application associated with the content item and an account identifier. The matching may also include receiving second data from the client device associated with a conversion using the application. The second data includes the application identifier and a device identifier for the client device. The matching may further include receiving third data from the client device that includes the account identifier and the device identifier. The conversion can be determined as associated with the interaction with the content item displayed with the resource based on matching the second data with the first data based on the third data.

Abstract: A computer-implemented method for annotating online content with offline interaction data and offline interaction conversion data is implemented by a content annotating computing device in communication with a memory. The method includes receiving a set of content interaction data based on an online interaction between an online user and a first online content item, identifying at least one physical location associated with the online content provider, determining a set of offline interaction conversion data based on the set of content interaction data and a set of offline interaction data, receiving a second online content item associated with the online content provider, and annotating the second online content item based at least partially on the set of offline interaction conversion data and the set of offline interaction data.

Abstract: Systems and methods of matching identifiers between multiple datasets are described herein. A system can transmit a first identifier vector to a third party server. The first identifier vector can include a first identifier, first parameters, and second parameters. The system can receive, from the third party server, the first identifier vector encrypted based on a third-party encryption. The system can receive, from the third party server, a second identifier vector encrypted based on the third-party encryption associated with the third party server. The second identifier vector can include a second identifier, third parameters, and fourth parameters. The system can determine a correlation count between the first identifier vector and the second identifier vector. The system can determine that the first identifier corresponds to the second identifier based on the correlation count. The system can generate one identifier key for both the first identifier and the second identifier.

Abstract: Systems and methods of matching identifiers between multiple datasets are described herein. A system can transmit a first identifier vector to a third party server. The first identifier vector can include a first identifier, first parameters, and second parameters. The system can receive, from the third party server, the first identifier vector encrypted based on a third-party encryption. The system can receive, from the third party server, a second identifier vector encrypted based on the third-party encryption associated with the third party server. The second identifier vector can include a second identifier, third parameters, and fourth parameters. The system can determine a correlation count between the first identifier vector and the second identifier vector. The system can determine that the first identifier corresponds to the second identifier based on the correlation count. The system can generate one identifier key for both the first identifier and the second identifier.

Abstract: Methods, systems, and apparatus include computer programs encoded on a computer-readable storage medium, including a method for providing content. Data specifying a post-install activity is received from a provider of an application. An opportunity is identified to provide third-party content to a user. A likelihood is determined that the user will perform the specified post-install activity based on one or more attributes of the user and attributes of users that have previously performed the specified post-install activity in the application. A selection value is adjusted for third-party content that identifies the application based on the determined likelihood, wherein the selection value increases as the likelihood increases. The third-party content identifying the application is selected based on the adjusted selection value. The third-party content identifying the application is distributed to a client device of the user.

Abstract: Methods, systems, and apparatus include computer programs encoded on a computer-readable storage medium, including a method for providing content. Data specifying a post-install activity is received from a provider of an application. An opportunity is identified to provide third-party content to a user. A likelihood is determined that the user will perform the specified post-install activity based on one or more attributes of the user and attributes of users that have previously performed the specified post-install activity in the application. A selection value is adjusted for third-party content that identifies the application based on the determined likelihood, wherein the selection value increases as the likelihood increases. The third-party content identifying the application is selected based on the adjusted selection value. The third-party content identifying the application is distributed to a client device of the user.

Abstract: The present disclosure is directed to a technique to reduce latency in feedback-based system performance determination. A system receives, from an application developer device, indications of an in-application event and a first input value for an application content delivery profile. The system receives, via an interface from an application developed by an application developer and executed by a computing device remote from the data processing system and different from the application developer device, a ping indicative of an occurrence of the in-application event on the computing device. The system merges data from the ping with internal data determined by the data processing system to generate merged data. The system determines a predicted performance for the in-application event and provides an indication of the predicted performance. The system configures, responsive to the indication of the predicted performance, the application content delivery profile with a second input value.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer-readable storage medium, and including a method for providing content. The method comprises receiving a first login request from a first device used by a user, the request being associated with a first anonymous identifier associated with the first device, and determining a user tag for the user, that does not include any personally identifiable information associated with the user. The method further comprises receiving a second login request from a second different device used by the user, the request being associated with a second different anonymous identifier associated with the second different device, and storing an association between the user tag, the first anonymous identifier and the second different anonymous identifier. The method further comprises receiving a request for content from either the first or second different device and providing content in response to the request using the association.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer-readable storage medium, and including a method for providing content. The method comprises receiving a first login request from a first device used by a user, the request being associated with a first anonymous identifier associated with the first device, and determining a user tag for the user, that does not include any personally identifiable information associated with the user. The method further comprises receiving a second login request from a second different device used by the user, the request being associated with a second different anonymous identifier associated with the second different device, and storing an association between the user tag, the first anonymous identifier and the second different anonymous identifier. The method further comprises receiving a request for content from either the first or second different device and providing content in response to the request using the association.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer-readable storage medium, and including a method for providing content. The method comprises receiving a first login request from a first device used by a user, the request being associated with a first anonymous identifier associated with the first device, and determining a user tag for the user, that does not include any personally identifiable information associated with the user. The method further comprises receiving a second login request from a second different device used by the user, the request being associated with a second different anonymous identifier associated with the second different device, and storing an association between the user tag, the first anonymous identifier and the second different anonymous identifier. The method further comprises receiving a request for content from either the first or second different device and providing content in response to the request using the association.