The Nielsen Comapny (us), LLC

Exelate, Inc.

Exelate Media Ltd.

Kevin Charles Lyons

Matthew Fornari

Devin Jones

Elad Efraim

Patrick McCann

Alon Kaftan

Jiabo Li

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.

Methods and apparatus for managing models for classification of online users

Methods and apparatus to perform multi-level hierarchical demographic classification are disclosed. An example apparatus includes a neural network structured to process inputs at an input layer to form first outputs at a first output layer representing first possible classifications of an individual according to a demographic classification system at a first hierarchical level, and to process the first outputs to form second outputs at a second output layer representing possible combined classifications of the individual corresponding to combinations of the first possible classifications and second possible classifications of the individual according to the classification system at a second different hierarchical level; and a selector to select one of the second outputs, and associate with the individual a respective one of the first possible classifications and a respective one of the second possible classifications corresponding to a respective one of the possible combined classifications represented by the selected second output.

Methods and apparatus to perform multi-level hierarchical demographic classification

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.

Method and system using association rules to form custom lists of cookies

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.

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New York, NY, United States of America

Kevin Charles Lyons