Methods and apparatus to generate audience metrics using matrix analysis

Patent metadata

Field	Value
Publication number	US-12008587-B2
Application number	US-202117406878-A
Country	US
Kind code	B2
Filing date	Aug 19, 2021
Priority date	Aug 21, 2020
Publication date	Jun 11, 2024
Grant date	Jun 11, 2024

Abstract

An example apparatus includes audience metrics collecting circuitry to access first audience metrics from a server, and access second audience metrics from the server, matrix building circuitry to build a matrix of the first audience metrics and the second audience metrics, missing values of the matrix corresponding to the second audience metrics, data transforming circuitry to transform the first audience metrics and the second audience metrics in the matrix, missing value calculating circuitry to determine imputed transformed values of the missing values using a recommender system, and the data transforming circuitry to recover imputed values of the missing values based on the imputed transformed values.

First claim

What is claimed is:

1. An audience measurement system comprising:
a panel monitor server configured to:
receive network communications identifying media items presented at client devices of panelists of an audience measurement entity, wherein the media items are tagged with beacon instructions that are downloaded to the client devices when the client devices access the media items, the beacon instructions to cause the client devices to transmit the network communications to the panel monitor server,
based on the network communications received from the client devices in accordance with the beacon instructions, log demographic impressions of the media items in association with known demographic data for the panelists, and
generate panelist audience metrics based on the demographic impressions;

a computing system comprising at least on processor and a memory, the computing system configured to:
access the panelist audience metrics from the panel monitor server,
access, from a census monitor server, census audience metrics generated based on census impressions of the media items logged by the census monitor server, wherein demographic data for users that accessed the media items was not logged by the census monitor server in association with the census impressions,
build a sparse matrix of the panelist audience metrics and the census audience metrics, wherein the sparse matrix comprises missing audience metrics values for a portion of demographic groups and for a portion of the media items, and wherein the sparse matrix comprises computer-generated audience metrics data bias represented by the missing audience metrics values,
normalize the panelist audience metrics and the census audience metrics in the sparse matrix to generate a sparse transformed matrix,
apply a recommender model to the sparse transformed matrix to predict audience metrics values corresponding to the missing audience metrics,
generate a supplemented transformed matrix comprising the predicted audience metrics values and audience metrics values from the sparse transformed matrix,
perform reverse transformations of the supplemented transformed matrix to generate a completed audience metrics matrix, and
transmit audience data to the panel monitor server and the census monitor server to cause the panel monitor server and the census monitor server to update respective audience metrics databases.

2. The audience measurement system of claim 1 , wherein the panelist audience metrics include a first audience size, a first impression count, and first duration data, and the census audience metrics include a second audience size, a second impression count, and second duration data.

3. The audience measurement system of claim 1 , wherein normalizing the panelist audience metrics and the census audience metrics in the sparse matrix to generate a sparse transformed matrix comprises:
normalizing the panelist audience metrics and the census audience metrics to a universe estimate;
logically transforming the normalized metrics to generate logically transformed metrics; and
performing a logarithmic transformation of the logically transformed metrics to generate the sparse transformed matrix.

4. The audience measurement system of claim 1 , wherein the recommender model comprises a deep factorization machine.

5. The audience measurement system of claim 1 , wherein the recommender model comprises a multilayer neural network.

6. The audience measurement system of claim 1 , wherein the media items are streaming media items.

7. The audience measurement system of claim 1 , further comprising the census monitor server.

8. At least one non-transitory computer readable storage medium comprising instructions that, when executed, cause at least one processor to perform operations comprising:
receiving, at a panel monitor server, network communications identifying media items presented at client devices of panelists of an audience measurement entity, wherein the media items are tagged with beacon instructions that are downloaded to the client devices when the client devices access the media items, the beacon instructions to cause the client devices to transmit the network communications to the panel monitor server;
based on the network communications received from the client devices in accordance with the beacon instructions, logging demographic impressions of the media items in association with known demographic data for the panelists;
generating panelist audience metrics based on the demographic impressions;
accessing the panelist audience metrics from the panel monitor server;
accessing, from a census monitor server, census audience metrics generated based on census impressions of the media items logged by the census monitor server, wherein demographic data for users that accessed the media items was not logged by the census monitor server in association with the census impressions;
building a sparse matrix of the panelist audience metrics and the census audience metrics, wherein the spare matrix comprises missing audience metrics values for a portion of demographic groups and or a portion of the media items, and wherein the sparse matrix comprises computer-generated audience metrics data bias represented by the missing audience metrics values;
normalizing the panelist audience metrics and the census audience metrics in the sparse matrix to generate a sparse transformed matrix;
applying a recommender model to the sparse transformed matrix to predict audience metrics values corresponding to the missing audience metrics;
generating a supplemented transformed matrix comprising the predicted audience metrics values and audience metrics values from the sparse transformed matrix;
performing reverse transformations of the supplemented transformed matrix to generate a completed audience metrics matrix; and
transmitting audience metrics data to the panel monitor server and the census monitor server to cause the panel monitor server and the census monitor server to update respective audience metrics databases.

9. The at least one non-transitory computer readable storage medium of claim 8 , wherein the panelist audience metrics include a first audience size, a first impression count, and first duration data, and the census audience metrics include a second audience size, a second impression count, and second duration data.

10. The at least one non-transitory computer readable storage medium of claim 8 , wherein normalizing the panelist audience metrics and the census audience metrics in the sparse matrix to generate a sparse transformed matrix comprises:
normalizing the panelist audience metrics and the census audience metrics to a universe estimate;
logically transforming the normalized metrics to generate logically transformed metrics; and
performing a logarithmic transformation of the logically transformed metrics to generate the sparse transformed matrix.

11. The at least one non-transitory computer readable storage medium of claim 8 , wherein the recommender model comprises a deep factorization machine.

12. The at least one non-transitory computer readable storage medium of claim 8 , wherein the recommender model comprises a multilayer neural network.

13. The at least one non-transitory computer readable storage medium of claim 8 , wherein the media items are streaming media items.

14. The at least one non-transitory computer readable storage medium of claim 8 , wherein the client devices comprise smartphones.

15. A method comprising:
receiving, by a panel monitor server, network communications identifying media items presented at client devices of p

Assignees

• Nielsen Co Us Llc

Inventors

• Cui Dongbo
• Murphy Edward
• Sheppard Michael Richard
• Forteguerre David
• White Jessica Lynn
• Yi Pengfei
• Sullivan Jonathan

Classifications

• G06F18/10

  Pre-processing; Data cleansing · CPC title

• G06F17/16

  Matrix or vector computation {, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization (matrix transposition G06F7/78)} · CPC title

• G06V10/764

  using classification, e.g. of video objects · CPC title

• G06Q30/0201 Primary

  Market modelling; Market analysis; Collecting market data · CPC title