Abstract: Webpage or other resource accesses by client systems may be recorded, and those accesses may be analyzed to develop audience measurement reports. At times, it may be desirable to segment those reports according to classes of client systems (e.g., work vs. home client systems). A given client system can be classed into one of the reporting classes based on one or more classes of network service providers that provide the client with access to a network. The recorded resource accesses and classes of the client systems can then be used to generate audience measurement reports that are segmented according to one or more of the client system classes.

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: Webpage or other resource accesses by client systems may be recorded, and those accesses may be analyzed to develop audience measurement reports. At times, it may be desirable to segment those reports according to classes of client systems (e.g., work vs. home client systems). A given client system can be classed into one of the reporting classes based on one or more classes of network service providers that provide the client with access to a network. The recorded resource accesses and classes of the client systems can then be used to generate audience measurement reports that are segmented according to one or more of the client system classes.

Abstract: In general, webpage or other resource accesses by client systems may be recorded, and those accesses may be analyzed to develop audience measurement reports. At times, it may be desirable to segment those reports according to classes of client systems (e.g., work vs. home client systems). A given client system can be classed into one of the reporting classes based on one or more classes of network service providers that provide the client with access to a network. The recorded resource accesses and classes of the client systems can then be used to generate audience measurement reports that are segmented according to one or more of the client system classes.

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, systems, and media for determining an index of a segment are disclosed. A first value representing one or more first users in a segment that have performed one or more key events may be determined. A second value representing one or more second users of a population that have performed the one or more key events may be determined. A third value representing one or more third users in the segment that have not performed the one or more key events may be determined. A fourth value representing one or more fourth users of the population that have not performed the one or more key events may be determined. The index for the segment may be calculated based on the first value, the second value, the third value, and the fourth value. The index may be transmitted.

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.

Abstract: In general, webpage or other resource accesses by client systems may be recorded, and those accesses may be analyzed to develop audience measurement reports. At times, it may be desirable to segment those reports according to classes of client systems (e.g., work vs. home client systems). A given client system can be classed into one of the reporting classes based on one or more classes of network service providers that provide the client with access to a network. The recorded resource accesses and classes of the client systems can then be used to generate audience measurement reports that are segmented according to one or more of the client system classes.