ARTIFICIAL INTELLIGENCE-DRIVEN DIGITAL MEDIA CREATIVE PERSONALIZATION AND GENERATION USING NATURAL LANGUAGE MODELS
An advertising method includes: receiving, from a mobile device with a consumer in a retail store, a location indication, matching a mobile device identifier with a frequent shopper identification for the consumer at the retail store, selecting a list of product items that are likely to be purchased by the consumer based on a purchasing history of the consumer in the retail store associated with the frequent shopper identification, identifying a keyword indicative of a semantic context associated with one or more product items from the list of product items and with a consumer attribute, selecting a media file based on the keyword indicative of the semantic context, and providing an advertisement payload for a one or more product items, including the media file, within a streaming feed for the mobile device with the consumer. A system configured to perform the above method is also provided.
1 . A computer-implemented method for generating an advertisement payload for a user, the method comprising:
receiving, from a mobile device with a consumer in a retail store, a location indication;
matching a mobile device identifier with a frequent shopper identification for the consumer at the retail store;
selecting a list of product items that are likely to be purchased by the consumer based on a purchasing history of the consumer in the retail store associated with the frequent shopper identification;
identifying a keyword indicative of a semantic context associated with one or more product items from the list of product items and with a consumer attribute;
selecting a media file based on the keyword indicative of the semantic context; and
providing an advertisement payload for a one or more product items, including the media file, within a streaming feed for the mobile device with the consumer.
2 . The method of claim 1 wherein selecting a list of product items includes ranking the list of product items based on a likelihood of purchase by the consumer, a price, and an availability of the one or more product items at the retail store.
3 . The method of claim 1 , wherein selecting a list of product items includes matching the purchasing history of the consumer with a list of items in an advertising campaign by the retail store.
4 . The method of claim 1 , wherein identifying the keyword indicative of a semantic context includes identifying the consumer attribute from a posting of the consumer in a social network portal.
5 . The method of claim 1 , wherein selecting a media file includes receiving, from an advertising technology server, an image including one or more product items.
6 . The method of claim 1 , wherein selecting a list of product items includes correlating the purchase history of the consumer with a list of products in an advertisement campaign promoted at the retail store.
7 . The method of claim 1 , wherein selecting a media file based on the keyword includes providing the keyword to a third-party machine learning service, and receiving the media file generated by the third-party machine learning service.
8 . The method of claim 1 , wherein selecting a media file based on the keyword includes using the keyword as input for a machine learning algorithm.
9 . The method of claim 8 , further comprising updating the machine learning algorithm in response to the consumer purchase of at least one of the one or more product items.
10 . The method of claim 8 , further comprising updating the machine learning algorithm after expiration of a pre-selected period of time and a determination of a purchase of the one or more product items.
11 . The method of claim 1 , further comprising providing a coupon, an offer, or a value-added certificate for at least one of the one or more product items based on a reward score for the frequent shopper identification.
12 . The method of claim 1 wherein identifying a keyword indicative of a semantic context includes identifying the consumer attribute based on the purchasing history of the consumer.
13 . The method of claim 1 further comprising ranking one or more media files associated with the one or more product items.
14 . The method of claim 13 , wherein ranking the one or more media files comprises retrieving the one or more media files from a database based on a score of a salient feature with the semantic context.
15 . The method of claim 13 , wherein ranking one or more images includes generating at least one image having a salient feature including a score with the semantic context higher than a pre-selected value.
16 . The method of claim 15 wherein ranking one or more media files includes:
identifying a salient feature of the one or more media files,
assigning a numeric value to each of the salient feature to form a vector in a multidimensional space,
defining the semantic context as a first axis in the multidimensional space, and
evaluating a projection of the vector on the first axis.
17 . The method of claim 13 , wherein ranking one or more media files includes generating at least one media file including an image of a consumer product that has the salient feature.
18 . A system, comprising:
a memory storing multiple instructions;
one or more processors configured to execute the instructions to cause the system to:
receive, from a mobile device with a consumer in a retail store, a location indication;
match a mobile device identifier with a frequent shopper identification for the consumer at the retail store;
select a list of product items that are likely to be purchased by the consumer based on a purchasing history of the consumer in the retail store associated with the frequent shopper identification;
identify a keyword indicative of a semantic context associated with one or more product items from the list of product items and with a consumer attribute;
select a media file based on the keyword indicative of the semantic context; and
provide an advertisement payload for a one or more product items, including the media file, within a streaming feed for the mobile device with the consumer.
19 . The system of claim 18 , wherein the one or more processors being configured to execute the instructions to cause the system to select a list of product items includes matching the purchasing history of the consumer with a list of items in an advertising campaign by the retail store.
20 . The system of claim 18 , wherein the one or more processors being configured to execute the instructions to cause the system to identify the keyword indicative of a semantic context includes identifying the consumer attribute from a posting of the consumer in a social network portal.
21 . The system of claim 18 , wherein the one or more processors being configured to execute the instructions to cause the system to select a media file based on the keyword includes providing the keyword to a third-party machine learning service, and receiving the media file generated by the third-party machine learning service.
22 . The system of claim 18 , wherein the one or more processors being configured to execute the instructions to cause the system to select a media file based on the keyword includes using the keyword as input for a machine learning algorithm.
23 . The system of claim 22 , wherein the instructions are further configured to cause the system to update the machine learning algorithm in response to the consumer purchase of at least one of the one or more product items.
24 . The system of claim 22 , wherein the instructions are further configured to cause the system to update the machine learning algorithm after expiration of a pre-selected period of time and a determination of a purchase of the one or more product items.
25 . The system of claim 18 , wherein the instructions are further configured to cause the system to rank one or more media files associated with the one or more product items.
26 . The system of claim 25 , wherein the one or more processors being configured to execute the instructions to cause the system to rank the one or more media files comprises retrieving the one or more media files from a database based on a score of a salient feature with the semantic context.
27 . The system of claim 25 , wherein the one or more processors being configured to execute the instructions to cause the system to rank the one or more images includes generating at least one image having a salient feature including a score with the semantic context higher than a pre-selected value.
28 . The system of claim 25 wherein the one or more processors being configured to execute the instructions to cause the system to rank one or more media files includes:
identifying a salient feature of the one or more media files,
assigning a numeric value to each of the salient feature to form a vector in a multidimensional space,
defining the semantic context as a first axis in the multidimensional space, and
evaluating a projection of the vector on the first axis, and
generating at least one media file including an image of a consumer product that has the salient feature.
29 . A non-transitory computer-readable medium storing instructions which, when executed by a processor, cause a computer to perform a method for generating an advertisement payload for a user, the method comprising:
receiving, from a mobile device with a consumer in a retail store, a location indication;
matching a mobile device identifier with a frequent shopper identification for the consumer at the retail store;
selecting a list of product items that are likely to be purchased by the consumer based on a purchasing history of the consumer in the retail store associated with the frequent shopper identification;
identifying a keyword indicative of a semantic context associated with one or more product items from the list of product items and with a consumer attribute;
selecting a media file based on the keyword indicative of the semantic context; and
providing an advertisement payload for a one or more product items, including the media file, within a streaming feed for the mobile device with the consumer.
30 . The non-transitory computer-readable medium of claim 29 , wherein selecting a list of product items includes matching the purchasing history of the consumer with a list of items in an advertising campaign by the retail store.
31 . The non-transitory computer-readable medium of claim 29 , wherein identifying the keyword indicative of a semantic context includes identifying the consumer attribute from a posting of the consumer in a social network portal.
32 . The non-transitory computer-readable medium of claim 29 , wherein selecting a media file includes receiving, from an advertising technology server, an image including one or more product items.
33 . The non-transitory computer-readable medium of claim 29 , wherein selecting a list of product items includes correlating the purchase history of the consumer with a list of products in an advertisement campaign promoted at the retail store.
34 . The non-transitory computer-readable medium of claim 29 , wherein selecting a media file based on the keyword includes providing the keyword to a third-party machine learning service, and receiving the media file generated by the third-party machine learning service.
35 . The non-transitory computer-readable medium of claim 29 , wherein selecting a media file based on the keyword includes using the keyword as input for a machine learning algorithm.
36 . The non-transitory computer-readable medium of claim 35 , further comprising updating the machine learning algorithm in response to the consumer purchase of at least one of the one or more product items.
37 . The non-transitory computer-readable medium of claim 35 , further comprising updating the machine learning algorithm after expiration of a pre-selected period of time and a determination of a purchase of the one or more product items.
38 . The non-transitory computer-readable medium of claim 29 , further comprising providing a coupon, an offer, or a value-added certificate for at least one of the one or more product items based on a reward score for the frequent shopper identification.
39 . The non-transitory computer-readable medium of claim 29 wherein identifying a keyword indicative of a semantic context includes identifying the consumer attribute based on the purchasing history of the consumer.
40 . The non-transitory, computer-readable medium of claim 29 further comprising ranking one or more media files associated with the one or more product items.
41 . The non-transitory computer-readable medium of claim 40 , wherein ranking the one or more media files comprises retrieving the one or more media files from a database based on a score of a salient feature with the semantic context.
42 . The non-transitory computer-readable medium of claim 40 , wherein ranking one or more images includes generating at least one image having a salient feature including a score with the semantic context higher than a pre-selected value.
43 . The non-transitory computer-readable medium of claim 40 wherein ranking one or more media files includes:
identifying a salient feature of the one or more media files,
assigning a numeric value to each of the salient feature to form a vector in a multidimensional space,
defining the semantic context as a first axis in the multidimensional space, and
evaluating a projection of the vector on the first axis.
44 . The non-transitory computer-readable medium of claim 40 , wherein ranking one or more media files includes generating at least one media file including an image of a consumer product that has the salient feature.