Abstract: Methods and apparatus for managing models for classification of online users are disclosed. An example method includes determining a segment associated with a received impression, determining whether the received impression includes an indication that the received impression is the result of a conversion associated with the segment, determining a current ratio of converted to non-converted impressions associated with a classification model associated with the segment, determining a difference between the current ratio and a target ratio, and storing an indication of the received impression for use in at least one of training or updating a classification model associated with the segment when: (a) the difference does not meet a threshold or (b) counting the received impression in the ratio would reduce the difference.

Abstract: Methods and apparatus for managing models for classification of online users are disclosed. An example method includes determining a segment associated with a received impression, determining whether the received impression includes an indication that the received impression is the result of a conversion associated with the segment, determining a current ratio of converted to non-converted impressions associated with a classification model associated with the segment, determining a difference between the current ratio and a target ratio, and storing an indication of the received impression for use in at least one of training or updating a classification model associated with the segment when: (a) the difference does not meet a threshold or (b) counting the received impression in the ratio would reduce the difference.

Abstract: A targeting rule set of association rules may be created. A plurality of association rules may be selected. A plurality of candidate rule sets may be constructed based on the plurality of association rules. The plurality of candidate rule sets may be evaluated to produce metrics. One of the plurality of candidate rule sets may be assigned as the targeting rule set based on the metrics. A custom list of user may be formed using association rules. A user may be determined to belong to a segment by applying one or more rules of a targeting rule set to user attribute data. A custom list of cookies to show advertising may be formed using combinations of association rules.

Abstract: Methods and apparatus for managing models for classification of online users are disclosed. An example method includes determining a segment associated with a received impression, determining whether the received impression includes an indication that the received impression is the result of a conversion associated with the segment, determining a current ratio of converted to non-converted impressions associated with a classification model associated with the segment, determining a difference between the current ratio and a target ratio, and storing an indication of the received impression for use in at least one of training or updating a classification model associated with the segment when: (a) the difference does not meet a threshold or (b) counting the received impression in the ratio would reduce the difference.

Abstract: Methods, system, and media for scaling a panel of users with known attributes to determine an unknown attribute of a user are disclosed. An unclassified attribute vector of the user may be received. The unclassified attribute vector may comprise first attributes and first attribute values. A reduced attribute vector may be produced using the unclassified attribute vector and a projection matrix. The reduced attribute vector may have fewer attributes than the unclassified attribute vector. A plurality of reduced panel attribute vectors may be produced using a plurality of classified panel attribute vectors of users from the panel and the projection matrix. The reduced panel attribute vectors may have fewer attributes than the classified panel attribute vectors. The unknown attribute of the user may be determined based on the reduced attribute vector, the plurality of reduced panel attribute vectors, and the known attributes.

Abstract: Methods, system, and media for scaling a panel of users with known attributes to determine an unknown attribute of a user are disclosed. An unclassified attribute vector of the user may be received. The unclassified attribute vector may comprise first attributes and first attribute values. A reduced attribute vector may be produced using the unclassified attribute vector and a projection matrix. The reduced attribute vector may have fewer attributes than the unclassified attribute vector. A plurality of reduced panel attribute vectors may be produced using a plurality of classified panel attribute vectors of users from the panel and the projection matrix. The reduced panel attribute vectors may have fewer attributes than the classified panel attribute vectors. The unknown attribute of the user may be determined based on the reduced attribute vector, the plurality of reduced panel attribute vectors, and the known attributes.

Abstract: A targeting rule set of association rules may be created. A plurality of association rules may be selected. A plurality of candidate rule sets may be constructed based on the plurality of association rules. The plurality of candidate rule sets may be evaluated to produce metrics. One of the plurality of candidate rule sets may be assigned as the targeting rule set based on the metrics. A custom list of user may be formed using association rules. A user may be determined to belong to a segment by applying one or more rules of a targeting rule set to user attribute data. A custom list of cookies to show advertising may be formed using combinations of association rules.