Advertisement Selection for Ad-Supported Video
A method of assigning advertisements to slots in video channels of a bundle of channels provided to an end user. The method includes managing credits, for each specific channel of the channels, indicative of a difference between a number of advertisements provided by an owner of the specific channel that were displayed on other channels and a number of advertisements provided by owners of other channels displayed on the specific channel In addition, scores indicative of a predicted success of the advertisement with the end user are calculated for a plurality of advertisements. An advertisement to be displayed to the end user is selected responsive to a function of both the calculated scores and the managed credits.
1 . A method of assigning advertisements to slots in video channels of a bundle of channels provided to an end user, comprising:
managing credits, for each specific channel of the channels, indicative of a difference between a number of advertisements provided by an owner of the specific channel that were displayed on other channels and a number of advertisements provided by owners of other channels displayed on the specific channel;
calculating, for a plurality of advertisements, scores indicative of a predicted success of the advertisement with the end user; and
selecting an advertisement to be displayed to the end user, responsive to a function of both the calculated scores and the managed credits.
2 . The method according to claim 1 , wherein managing the credits comprises assigning different numbers of credits for displaying advertisements, responsive to attributes of a viewer to which the advertisements are displayed.
3 . The method according to claim 1 , wherein managing the credits comprises assigning different numbers of credits for displaying advertisements, responsive to attributes of a device on which the advertisements are displayed.
4 . The method according to claim 1 , wherein calculating the scores comprises calculating by a machine learning engine.
5 . The method according to claim 4 , wherein calculating the scores comprises training the machine learning engine to identify end users having features similar to users who following the viewing of a promotional advertisement, viewed the channel promoted by the promotional advertisement.
6 . The method according to claim 4 , wherein calculating the scores comprises training the machine learning engine based on tracking viewership of similar users.
7 . The method according to claim 4 , wherein calculating the scores comprises training the machine learning engine based on features of the viewership of the video channels by a plurality of end users, and conversion information on the end users.
8 . The method according to claim 4 , wherein calculating the scores comprises calculating Pearson correlation based recommendations.
9 . The method according to claim 1 , wherein calculating the scores comprises calculating a prediction of a duration the end user will view a channel promoted by the advertisement, following viewing the advertisement.
10 . The method according to claim 1 , wherein calculating the scores comprises calculating a prediction of an increase in a time the user will spend in viewing the channels of the bundle, following viewing the advertisement.
11 . The method according to claim 1 , wherein selecting the advertisement to be displayed comprises selecting responsive to a weighted sum of the calculated scores and the managed credits.
12 . The method according to claim 11 , and comprising periodically updating weights of the weighted sum.
13 . The method according to claim 1 , wherein selecting the advertisement to be displayed comprises selecting for slots that were not filled in a bidding based on payment.
14 . The method according to claim 1 , further comprising setting for each of the channels a cap on the number of credits it may consume per a given period, and wherein selecting the advertisement to be displayed is performed additionally responsive to the caps of the channels.
15 . The method according to claim 1 , wherein setting the cap of each channel comprises calculating an average rate of credit generation by the channel and setting the cap responsive to the set calculated average.
16 . A method of controlling broadcast of video channels, comprising:
filing bids for a plurality of slots in a plurality of broadcast channels transmitted to end users, wherein the bids are filed with low price offers;
receiving bid logs, by a processor, responsive to the filed bids;
grouping entries of the received bid logs into single-session groups, by the processor;
estimating a session duration for each of the single-session groups; and
controlling, by the processor, supply of broadcast channels to one or more of the end users, responsive to the estimated session durations.
17 . The method of claim 16 , wherein filing the bids comprises filing on channels of a plurality of different service providers.
18 . The method of claim 16 , wherein grouping the entries comprises grouping entries into groups of the same user ID, service provider ID and content identifier.
19 . The method of claim 16 , wherein grouping the entries comprises grouping the entries, such that entries separated by a time longer than a threshold are considered as belonging to different groups.
20 . An apparatus for assigning advertisements to slots in video channels of a bundle of channels provided to an end user, comprising:
a memory which stores:
credits, which indicate for each specific channel of the channels, a difference between a number of advertisements provided by an owner of the specific channel that were displayed on other channels and a number of advertisements provided by owners of other channels displayed on the specific channel; and
scores indicative of a predicted success of respective advertisements with the end user; and
a processor configured to select advertisements to be displayed to the end user, responsive to a function of both the scores and the credits.
21 . The apparatus of claim 20 , wherein the scores represent an estimated average number of hours that the end user will spend watching the channel promoted by the advertisement following viewing the advertisement.
22 . The apparatus of claim 20 , wherein the scores represent an increase in time the user will view the channels of the bundle following viewing the advertisement.
23 . The apparatus of claim 20 , further comprising a cache memory which stores for each end-user, a list of channels that the end-user has not viewed in the recent period, ordered according to a priority of providing promotion advertisements for the channel
24 . The apparatus of claim 23 , wherein the processor is configured to select advertisements to be displayed to an end-user, from the list of channels of the end-user, if the list has not expired, and to select advertisements to be displayed from a default list, if the list of channels of the end-user has expired.
25 . The apparatus of claim 24 , wherein the processor is configured to determine the list of channels of the end-user when determined that the list of channels of the end-user has expired.