Machine learning-based approach to demographic attribute inference using time-sensitive features

Patent metadata

Field	Value
Publication number	US-2019373332-A1
Application number	US-201916359684-A
Country	US
Kind code	A1
Filing date	Mar 20, 2019
Priority date	Jun 4, 2018
Publication date	Dec 5, 2019
Grant date	—

Abstract

A system and method for predicting user demographics based on a user's television, or media, viewing habits using machine learning algorithms is provided. A method of predicting a user's demographics comprises acquiring training data including one or more household data, person identification data, program title data, or watch time data. The method includes assessing a set of features. In addition, the method includes training one or more models based on the training data and set of features. The method includes acquiring viewing history data associated with at least one user. The method further includes determining one or more attributes associated with the at least one user based on inputting the viewing history data into the one or more models.

First claim

What is claimed is:

1 . A method for determining user attributes based on television viewing habits comprising:
acquiring training data including one or more of household data, person identification data, program title data, or watch time data, assessing a set of features including a first set of features associated with one or more specific time slices, training one or more models based on the training data and set of features, acquiring viewing history data associated with at least one user, and determining one or more attributes associated with the at least one user based on inputting the viewing history data into the one or more models.

2 . The method of claim 1 , wherein the one or more specific time slices include at least one of: 1) 48 time units of 30 minutes each, 2) 24 time units of one hour each, 3) parts of a day, 4) days of a week 5) a weekday, or 6) a weekend.

3 . The method of claim 1 , wherein the set of features includes a second set of features associated with one or more keywords for one or more television programs.

4 . The method of claim 1 , wherein the set of features includes a third set of features associated with the one or more television program titles.

5 . The method of claim 1 , wherein the viewing history data comprises at least one of electronic programing guide (EPG) data or automatic content recognition (ACR) data.

6 . The method of claim 5 , wherein the viewing history data is transformed into user-centric smart data based on sessionization or hierarchical sessionization.

7 . The method of claim 1 , wherein the attributes associated with the at least one user comprises at least one of gender, age, or household type.

8 . A computing system comprising:
at least one processor configured to:
acquire training data including one or more of household data, person identification data, program title data, or watch time data,
assess a set of features including a first set of features associated with one or more specific time slices,
train one or more models based on utilizing machine learning with the training data and set of features,
acquire viewing history data associated with at least one user, and
determine one or more attributes associated with the at least one user based on inputting the viewing history data into the one or more models.

9 . The computing system of claim 8 , wherein the one or more specific time slices include at least one of: 1) 48 time units of 30 minutes each, 2) 24 time units of one hour each, 3) parts of a day, 4) days of a week, 5) a weekday, or 6) a weekend.

10 . The method of claim 8 , wherein the set of features includes a second set of features associated with one or more keywords for one or more television programs.

11 . The method of claim 8 , wherein the set of features includes a third set of features associated with the one or more television program titles.

12 . The method of claim 8 , wherein the viewing history data comprises at least one of electronic programing guide (EPG) data or automatic content recognition (ACR) data.

13 . The method of claim 12 , wherein the at least one processor transforms the viewing history data into user-centric smart data based on sessionization or hierarchical sessionization.

14 . The method of claim 8 , wherein the attributes associated with the at least one user comprises at least one of gender, age, or household type.

15 . A non-transitory computer readable medium configured to store a plurality of instructions that, when executed by at least one processor, is configured to cause the at least one processor to:
acquire training data including one or more of household data, person identification data, program title data, or watch time data, assess a set of features including a first set of features associated with one or more specific time slices, train one or more models based on utilizing machine learning with the training data and set of features, acquire viewing history data associated with at least one user, and determine one or more attributes associated with the at least one user based on inputting the viewing history data into the one or more models.

16 . The non-transitory computer readable medium of claim 15 , wherein the one or more specific time slices include at least one of: 1) 48 time units of 30 minutes each, 2) 24 time units of one hour each, 3) parts of a day, 4) days of a week, 5) a weekday, or 6) a weekend.

17 . The non-transitory computer readable medium of claim 15 , wherein the set of features includes a second set of features associated with one or more keywords for one or more television programs.

18 . The non-transitory computer readable medium of claim 15 , wherein the set of features includes a third set of features associated with the one or more television program titles.

19 . The non-transitory computer readable medium of claim 15 , wherein the viewing history data comprises at least one of electronic programing guide (EPG) data or automatic content recognition (ACR) data.

20 . The non-transitory computer readable medium of claim 19 , wherein plurality of instructions is further configured to cause the processor to transform the viewing history data into user-centric smart data based on sessionization or hierarchical sessionization.

Assignees

• Samsung Electronics Co Ltd

Inventors

• Kim Hyun-Woo

Classifications

• H04N21/466 Primary

  Learning process for intelligent management, e.g. learning user preferences for recommending movies {(services using the results of monitoring in broadcast systems H04H60/61)} · CPC title

• H04N21/44213

  Monitoring of end-user related data (arrangements for monitoring the users' behaviour or opinions in broadcast systems H04H60/33) · CPC title

• H04N21/44204

  Monitoring of content usage, e.g. the number of times a movie has been viewed, copied or the amount which has been watched (monitoring of user activities for profile generation for accessing a video database G06F16/739; protecting generic digital content where the protection is independent of the precise nature of the content G06F21/10; arrangements for monitoring the use made of the broadcast services in broadcast systems H04H60/31) · CPC title

• G06N20/00

  Machine learning · CPC title

• H04N21/4826 Primary

  using recommendation lists, e.g. of programmes or channels sorted out according to their score · CPC title