Generating optimized in-channel and cross-channel promotion recommendations using free shipping qualifier

Abstract

In general, embodiments of the present invention provide systems, methods and computer readable media for recommending contextually relevant promotions to consumers in order to facilitate their discovery of promotions that they are likely to purchase from a promotion and marketing service.

1 - 20 . (canceled) 
     
     
         21 . A computer-implemented method comprising:
 receiving, by a processor, promotion data describing a set of promotions;   causing, by the processor, rendering of an electronic user interface to a first consumer device associated with a first consumer, the electronic user interface comprising a plurality of impressions, wherein the plurality of impressions are respectively associated with a displayed promotion subset of the set of promotions;   receiving, by the processor, clickstream data indicating real time interaction by the first consumer with a particular impression of the plurality of impressions, wherein the particular impression is associated with a particular promotion;   generating, by the processor, a ranked set of promotions based at least in part on the promotion data and the particular promotion, wherein the ranked set of promotions is based at least in part on adaptive collaborative filtering signals and static similarity signals; and   causing, by the processor, updating of the electronic user interface to display an updated plurality of impressions associated with the ranked set of promotions simultaneously with and positioned proximate to the particular impression associated with the particular promotion.   
     
     
         22 . The computer-implemented method of  claim 21 , wherein generating the ranked set of promotions comprises:
 generating a collaborative filtering ranked list based on one or more adaptive filtering signals;   generating a static similarity ranked list based on one or more static similarity signals; and   generating the ranked set of promotions based on the collaborative filtering ranked list and the static similarity ranked list.   
     
     
         23 . The computer-implemented method of  claim 22 , wherein the one or more adaptive filtering signals are based at least in part on the clickstream data. 
     
     
         24 . The computer-implemented method of  claim 23 , wherein the promotion data describes a plurality of promotion pairs (X, Y), wherein the particular promotion is a member X promotion of each promotion pair, wherein a member Y promotion of each promotion pair of the plurality of promotion pairs (X, Y) respectively represents one of a group of other live promotions, and wherein each promotion pair (X, Y) of the plurality of promotion pairs was selected based on determining that similarity metrics respectively associated with the promotion pair (X, Y) are respectively above a similarity threshold and a popularity threshold. 
     
     
         25 . The computer-implemented method of  claim 24  further comprising, for each promotion pair (X, Y) of the plurality of promotion pairs (X, Y),
 calculating, using the associated similarity metrics, a co-view score representing a likelihood that the member Y promotion will be selected by a consumer while the consumer is viewing the member X promotion; and 
 for the member X promotion, generating the collaborative filtering ranked list of all member Y promotions in the plurality of promotion pairs, wherein the member Y promotions are ranked in descending order of their associated co-view scores. 
 
     
     
         26 . The computer-implemented method of  claim 22 , wherein generating the ranked set of promotions comprises:
 determining whether the clickstream data are sparse data; and in an instance in which the clickstream data are sparse data:   generating the ranked set of promotions using the adaptive collaborative filtering signals; and using the static similarity signals to backfill the ranked set of promotions.   
     
     
         27 . The computer-implemented method of  claim 22 , wherein generating the ranked set of promotions comprises:
 in an instance in which the clickstream data are not sparse data:   generating the ranked set of promotions using the adaptive collaborative filtering signals.   
     
     
         28 . The computer-implemented method of  claim 21 , wherein the updated plurality of impressions comprises immediate purchase options for the ranked set of promotions. 
     
     
         29 . The computer-implemented method of  claim 21 , wherein the ranked set of promotions is generated based at least in part on a historical co-purchase representing a purchase that is performed by a consumer different from the first consumer. 
     
     
         30 . The computer-implemented method of  claim 29  further comprising:
 determining a co-purchase score for the historical co-purchase based at least in part on calculating a frequency of purchases by consumers of promotions associated with a first service and purchases by those same consumers of promotions associated with a second service; and 
 ranking the ranked set of promotions in descending order of their respective co-purchase scores. 
 
     
     
         31 . An apparatus comprising at least one processor and at least one non-transitory memory comprising program code, wherein the at least one non-transitory memory and the program code are configured to, with the at least one processor, cause the apparatus to:
 receive promotion data describing a set of promotions;   cause rendering of an electronic user interface to a first consumer device associated with a first consumer, the electronic user interface comprising a plurality of impressions, wherein the plurality of impressions are respectively associated with a displayed promotion subset of the set of promotions;   receive clickstream data indicating real time interaction by the first consumer with a particular impression of the plurality of impressions, wherein the particular impression is associated with a particular promotion;   generate a ranked set of promotions based at least in part on the promotion data and the particular promotion, wherein the ranked set of promotions is based at least in part on adaptive collaborative filtering signals and static similarity signals; and   cause updating of the electronic user interface to display an updated plurality of impressions associated with the ranked set of promotions simultaneously with and positioned proximate to the particular impression associated with the particular promotion.   
     
     
         32 . The apparatus of  claim 31 , wherein generating the ranked set of promotions comprises:
 generating a collaborative filtering ranked list based on one or more adaptive filtering signals;   generating a static similarity ranked list based on one or more static similarity signals; and   generating the ranked set of promotions based on the collaborative filtering ranked list and the static similarity ranked list.   
     
     
         33 . The apparatus of  claim 32 , wherein the one or more adaptive filtering signals are based at least in part on the clickstream data. 
     
     
         34 . The apparatus of  claim 32 , wherein the promotion data describes a plurality of promotion pairs (X, Y), wherein the particular promotion is a member X promotion of each promotion pair, wherein a member Y promotion of each promotion pair of the plurality of promotion pairs (X, Y) respectively represents one of a group of other live promotions, and wherein each promotion pair (X, Y) of the plurality of promotion pairs was selected based on determining that similarity metrics respectively associated with the promotion pair (X, Y) are respectively above a similarity threshold and a popularity threshold. 
     
     
         35 . The apparatus of  claim 34 , wherein the at least one non-transitory memory and the program code are further configured to, with the at least one processor, cause the apparatus to:
 for each promotion pair (X, Y) of the plurality of promotion pairs (X, Y),   calculate, using the associated similarity metrics, a co-view score representing a likelihood that the member Y promotion will be selected by a consumer while the consumer is viewing the member X promotion; and   for the member X promotion, generate the collaborative filtering ranked list of all member Y promotions in the plurality of promotion pairs, wherein the member Y promotions are ranked in descending order of their associated co-view scores.   
     
     
         36 . The apparatus of  claim 32 , wherein generating the ranked set of promotions comprises:
 determining whether the clickstream data are sparse data; and in an instance in which the clickstream data are sparse data:   generating the ranked set of promotions using the adaptive collaborative filtering signals;   and using the static similarity signals to backfill the ranked set of promotions.   
     
     
         37 . The apparatus of  claim 32 , wherein generating the ranked set of promotions comprises:
 in an instance in which the clickstream data are not sparse data:   generating the ranked set of promotions using the adaptive collaborative filtering signals.   
     
     
         38 . A non-transitory computer readable medium, comprising instructions that when executed on one or more computers cause the one or more computers to:
 receive promotion data describing a set of promotions;   cause rendering of an electronic user interface to a first consumer device associated with a first consumer, the electronic user interface comprising a plurality of impressions, wherein the plurality of impressions are respectively associated with a displayed promotion subset of the set of promotions;   receive clickstream data indicating real time interaction by the first consumer with a particular impression of the plurality of impressions, wherein the particular impression is associated with a particular promotion;   generate a ranked set of promotions based at least in part on the promotion data and the particular promotion, wherein the ranked set of promotions is based at least in part on adaptive collaborative filtering signals and static similarity signals; and   cause updating of the electronic user interface to display an updated plurality of impressions associated with the ranked set of promotions simultaneously with and positioned proximate to the particular impression associated with the particular promotion.   
     
     
         39 . The non-transitory computer readable medium of  claim 38 , wherein generating the ranked set of promotions comprises:
 generating a collaborative filtering ranked list based on one or more adaptive filtering signals;   generating a static similarity ranked list based on one or more static similarity signals; and   generating the ranked set of promotions based on the collaborative filtering ranked list and the static similarity ranked list.   
     
     
         40 . The non-transitory computer readable medium of  claim 39 , wherein the one or more adaptive filtering signals are based at least in part on the clickstream data.