Deducing shadow user profiles for ad campaigns

A system for deducing one or more user profile attributes, the system comprising:

• a computer system configured for:
• extracting tagged data from source data using a shadow profile, wherein the tagged data comprises label information associated with an actual profile for a user, and wherein the tagged data is associated with the user;
• preparing the tagged data by splitting the tagged data into datasets, including at least training data and test data;
• generating one or more individual models based on the tagged data, wherein the one or more individual models provide the ability to deduce attributes of a profile for the user;
• testing the one or more individual models using the test data to determine a confidence level representing the predicted accuracy of the individual model;
• generating a composite model based on the individual models, wherein the composite model comprises a combination of the individual models that are associated with the user and achieve a confidence level which is greater than a predefined threshold value;
• generating a plurality of values associated with a user profile attribute using the composite model;
• storing the plurality of values associated with a user profile attribute in a shadow profile associated with the user, wherein the shadow profile comprises a plurality of attributes, each attribute comprising at least one value and at least one confidence level;
• updating the at least one confidence level of the shadow profile based on new or modified label information associated with the actual profile;
• and
• exposing at least one attribute associated with a user profile to facilitate the selection of an advertisement to present to the user.

A non-transitory computer readable storage medium comprising one or more instructions for deducing one or more user profile attributes, wherein the one or more instructions are configured for causing one or more processors to perform the steps of:

• extracting tagged data from source data, wherein the tagged data comprises label information associated with an actual profile for a user, and wherein the tagged data is associated with the user;
• preparing the tagged data by splitting the tagged data into datasets, including at least training data and test data;
• generating one or more individual models based on the tagged data, wherein the one or more individual models provide the ability to deduce attributes of a profile for the user;
• testing the one or more individual models using the test data to determine a confidence level representing the predicted accuracy of the individual model;
• generating a composite model based on the individual models, wherein the composite model comprises a combination of the individual models that are associated with the user and achieve a confidence level which is greater than a predefined threshold value;
• generating a plurality of values associated with a user profile attribute using the composite model;
• storing the plurality of values associated with a user profile attribute in a shadow profile associated with the user, wherein the shadow profile comprises a plurality of attributes, each attribute comprising at least one value and at least one confidence level;
• updating the at least one confidence level of the shadow profile based on new or modified label information associated with the actual profile;
• and
• exposing at least one attribute associated with a user profile to facilitate the selection of an advertisement to present to the user.