Abstract: During a targeting technique, a machine model is generated based on content-interaction data that specifies interactions of users of a social network, with categorical content corresponding to predefined interest segments. The content-interaction data may include viewing of the categorical content and sharing of the categorical content with other users of the social network. This machine-learning model is then used to calculate scores for the users based on the attributes in their profiles that indicate probabilities of their interest in additional categorical content. Moreover, based on the calculated scores, a subset of the users is associated with an interest segment. 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. Furthermore, advertisements may be targeted to the users in the subset based on the association with the interest segment.

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.