Abstract: A system and method for analyzing media requests and evaluating the authenticity of geographic data associated with the media requests. A mobile device may use media requests to retrieve media items that are displayed to a user. A media request may include geographic data with latitude and longitude information and entity data identifying a user, application, mobile device, or combination thereof. The system may analyze the geographic data to identify locations and may analyze the entity information to identify user activity. The user activity may be internet activity of an application running on another device (e.g., user's desktop). The system may then correlate the user activity (e.g., accessing a venue web site) with the geographic locations (e.g., venue locations) to evaluate the authenticity of the geographic data associated with the mobile requests.

Abstract: A system and method for analyzing media requests and evaluating the authenticity of geographic data associated with the media requests. A mobile device may use media requests to retrieve media items that are displayed to a user. A media request may include geographic data with latitude and longitude information and entity data identifying a user, application, mobile device, or combination thereof. The system may analyze the geographic data to identify locations and may analyze the entity information to identify user activity. The user activity may be internet activity of an application running on another device (e.g., user's desktop). The system may then correlate the user activity (e.g., accessing a venue web site) with the geographic locations (e.g., venue locations) to evaluate the authenticity of the geographic data associated with the mobile requests.

Abstract: A complete end-to-end modeling system is provided that includes data sampling, feature engineering, action labeling, and model learning or learning from models built based on collected data. The end-to-end modeling process is performed via an automatic mechanism with minimal or reduced human intervention. A processor-readable medium is disclosed, storing processor-executable instructions to instantiate an automated data sampling and prediction structure training component, the automated data sampling and prediction structure training component being configured to automatically collect user event data samples, and use the collected user event data samples to train multiple prediction structures in parallel.

Abstract: Some embodiments described herein relate to receiving obfuscated location data associated with a first compute device, such as a mobile phone. A database including records of multiple compute devices, including a second compute device, associated with multiple obfuscated locations can be accessed and a link between the first compute device and the second compute device can be defined based, at least in part on the obfuscated location data associated with the first compute device matching a record for the second compute device. In some embodiments, the first compute device and/or the second compute device can receive content based, at least in part on the link.

Abstract: A complete end-to-end modeling system is provided that includes data sampling, feature engineering, action labeling, and model learning or learning from models built based on collected data. The end-to-end modeling process is performed via an automatic mechanism with minimal or reduced human intervention. A processor-readable medium is disclosed, storing processor-executable instructions to instantiate an automated data sampling and prediction structure training component, the automated data sampling and prediction structure training component being configured to automatically collect user event data samples, and use the collected user event data samples to train multiple prediction structures in parallel.

Abstract: A method for mobile advertising conversion attribution can include receiving, for example, at a conversion tracking server, a signal from a user device indicating that a conversion tag has been received. The signal can include a segment identifier, and a user agent (UA) and/or an internet protocol (IP) address of the user device. A conversion identifier can be defined that is associated with the segment identifier, the UA, and/or the IP address. The conversion identifier can be stored in a first database along with an indication of when the conversion identifier was received. A second database can be searched for an advertisement identifier that matches the conversion identifier and was received within a predetermined period of time before the conversion identifier. If a match between the conversion identifier and an advertisement identifier is located, a signal indicating that an advertisement was converted can be sent to an advertiser.