Affinity profile system and method
A computer-implemented method of using affinity profiles to determine at least one audience category that is expected to consume a selected item of content at one or more selected time periods, the method comprises: obtaining affinity profiles for a plurality of users of a content distribution system, wherein each affinity profile comprises at least one selected affinity category or affinity category score, and the affinity categories are selected from a stored set of affinity categories, each representing a user's affinity for a respective subject area; monitoring consumption of items of content or other activity of the users for different time periods; determine the likelihood of, or an expected audience measure for, the selected item of content being consumed during the or each selected time period based on the affinity profiles and the monitored consumption of content or other activity for the different time periods.
1 . A computer-implemented method of using affinity profiles to determine at least one audience category that is expected to consume content, the method comprising:
obtaining, by one or more processors, affinity profiles for a plurality of users of a content distribution system, the affinity profiles comprising an affinity profile for each user of a plurality of users, wherein each affinity profile comprises a plurality of selected affinity categories and an affinity category score for each of the a plurality of selected affinity categories, and the affinity categories are selected from a stored set of affinity categories, each representing a user's affinity for a respective subject area;
monitoring, by the one or more processors and over a network, consumption of items of content or other activity of the plurality of users for past time periods;
selecting, by the one or more processors, one or more future time periods;
after selecting the one or more future time periods, determining, by the one or more processors, an expected audience measure for the selected one or more future time periods, the expected audience measure being which users of the plurality of users are expected to consume television content of any type during the one or more future time periods or during each of the one or more future time periods, the expected audience measure being determined based on the affinity profiles and the monitored consumption of content or other activity for the past time periods; and
categorizing, by the one or more processors, each user into one or more of a plurality of user segments based on their affinity profile,
wherein the determining the expected audience measure is based on the segments to which the users belong and on the monitored consumption for past time periods, and
wherein the determining the expected audience measure comprises determining a likelihood of users belonging to a user segment consuming television content of any type during a selected time of day, the selected time of day corresponding to the selected one or more future time periods.
2 . The method of claim 1 , comprising:
for each of a plurality of user devices, monitoring, by the one or more processors and over the network, user activity including identifying content selected for viewing by the users of the user devices;
obtaining, by the one or more processors, metadata concerning the selected items of content, the metadata representing at least some properties of the selected items of content;
generating or updating, by the one or more processors, user records for the users, the user records comprising or representing the user activity or the associated content metadata;
processing, by the one or more processors, the user records to generate the affinity profiles for the users based on the user records.
3 . The method of claim 1 , wherein the item of content is provided to user devices of the users using a content distribution system, wherein each user device displays an electronic programme guide (EPG) or other user interface that is operable by a user to select one or more items of television or other content, and in response to the selections the distribution system distributes the selected items of television content or other content to the user devices for viewing by the users during content viewing sessions.
4 . The method of claim 3 , wherein the user interface is operable by a user to select one or more items of content of content types other than television content,
the obtaining of metadata, the generating or updating of the user record, and the processing of the user record to generate the affinity profile are performed using the content metadata obtained for both selected items of television content and selected items of content of the other content types, and
the other content types comprises at least one of computer games, books, music, spoken word content, other audio content, newspapers, or magazines.
5 . The method of claim 3 , wherein the monitoring of consumption of items of content or other activity of the users for past time periods is performed using the user records of the users.
6 . The method of claim 1 , wherein the determining the expected audience measure comprises providing the affinity profiles, user records or the monitored consumption to a machine learning model that is trained to output the expected audience measure.
7 . The method of claim 1 , further comprising determining, by the one or more processors, an optimal one or more of the time periods to provide the content.
8 . The method of claim 7 , wherein the optimal one or more of the time periods comprise the time period or time periods having a maximum expected audience size or a maximum expected audience of users with a desired affinity profile or belonging to desired user segment.
9 . The method of claim 1 , wherein the time periods are time windows during a day or days of the week.
10 . The method of claim 1 , wherein the generating of the affinity profile is independent of, or is performed without taking into account, data representing socio-economic or demographic status of the users.
11 . The method of claim 2 , wherein at least one of a), b) or c):
a) the user record for each user does not include any data representing socio-economic or demographic status of the user;
b) the user record for each user is anonymous; or
c) the obtaining of metadata, the generating or updating of user records, and the processing of the user records to generate affinity profiles are performed by processing circuitry that does not have access to at least one of or all of data representing socio-economic or demographic status of the users or identity of the users.
12 . The method of claim 1 , wherein at least one of:
the stored set of affinity categories comprises at least 100 affinity categories or sub-categories; or
the metadata concerning the content is obtained from a stored ontology that includes at least 10,000 features that can be used as meta data to represent items of content.
13 . The method of claim 1 , wherein the user record comprises or represent user activity for different time windows during a day or week, and the method comprises generating different affinity profiles for the user for the different time windows.
14 . The method of claim 1 , wherein the affinity profile for each user comprises a set of scores, each score being for a respective one of the affinity categories.
15 . The method of claim 1 , wherein each user is a user account, a plurality of individuals may have access to the user account or wherein a plurality of user devices may be associated with the user account, and wherein the affinity profile for the user account is based on selection of content by the plurality of individuals or using the plurality of user devices.
16 . The method according to claim 1 , further comprising selecting, by the one or more processors, additional television content or other content to push to the user based on the determined affinity profile for the user.
17 . The method according to claim 1 , further comprising outputting, by the one or more processors and over the network, the affinity profiles for the plurality of users or the audience category, via an API or operator interface thereby making the affinity profiles available to a third party external to the content distribution system.
18 . A system comprising processing circuitry with one or more processors configured to:
obtain, by the one or more processors, affinity profiles for a plurality of users of a content distribution system, the affinity profiles comprising an affinity profile for each user of a plurality of users, wherein each affinity profile comprises a plurality of selected affinity categories and an affinity category score for each of the a plurality of selected affinity categories, and the affinity categories are selected from a stored set of affinity categories, each representing a user's affinity for a respective subject area;
monitor, by the one or more processors and over a network, consumption of items of content or other activity of the plurality of users for past time periods;
select, by the one or more processors, one or more future time periods;
after selecting the one or more future time periods, determine, by the one or more processors, an expected audience measure for the selected one or more future time periods, the expected audience measure being which users of the plurality of users are expected to consume television content of any type during the one or more future time periods or during each of the one or more future time periods, the expected audience measure being determined based on the affinity profiles and the monitored consumption of content or other activity for the past time periods; and
categorize, by the one or more processors, each user into one or more of a plurality of user segments based on their affinity profile,
wherein the determining the expected audience measure is based on the segments to which the users belong and on the monitored consumption for past time periods, and wherein the determining the expected audience measure comprises determining a likelihood of users belonging to a user segment consuming television content of any type during a selected time of day, the selected time of day corresponding to the selected one or more future time periods.
19 . A non-transitory computer-readable medium that comprises computer-readable instructions that are executable to perform a method according to claim 1 .