TARGETING USERS BASED ON PREVIOUS ADVERTISING CAMPAIGNS
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United States of America Patent
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app pub date -
Oct 7, 2013
filing date -
Jun 28, 2013
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Abandoned
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Abstract
During a targeting technique, a machine-learning model is generated based on information about previous advertising campaigns and attributes in profiles of users of a social network (which facilitates interactions among the users). The information about the previous advertising campaigns includes specified target groups and associated feedback metrics obtained from individuals, such as impressions served, clicks and/or conversions. This machine-learning model is then used to calculate scores for the users based on attributes in their profiles and/or user behaviors (such as online activities) that indicate probabilities of their responding to a future advertising campaign for a target group. Moreover, based on the calculated scores, a subset of the users is associated with the target group. For example, the users may be ranked based on their calculated scores, and the subset may be those users having scores exceeding a threshold or a predefined value.
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