Predictive recommendation system using absolute relevance

Abstract

In general, embodiments of the present invention provide systems, methods and computer readable media for ranking promotions selected for recommendation to consumers based on predictions of promotion performance and consumer behavior. In embodiments, a set of promotions to be recommended to a consumer can be sorted and/or ranked according to respective relevance scores representing a probability that the consumer's behavior in response to the promotion will match a ranking target. In embodiments, calculating scores is based on a relevance model (a predictive function) derived from one or more contextual data sources representing attributes of promotions and consumer behavior. In embodiments, an absolute relevance score represents an absolute prediction of a ranking target variable. In embodiments, absolute relevance may be used to determine personalized local merchant discovery frontiers; featured result set thresholding for impressions; and/or promotion notification triggers. In embodiments, predictive models based on gross revenue may be optimized using promotion category-dependent price boosting.

1 - 24 . (canceled) 
     
     
         25 . An apparatus comprising at least one processor and at least one non-transitory computer readable storage medium storing instructions that, with the at least one processor, cause the apparatus to:
 receive input data representing a user request, the input data comprising a user location;   retrieve, from one or more repositories, user attributes associated with a user associated with the user request;   retrieve, from the one or more repositories, promotion attributes associated with each of a set of available promotions for the user;   generate, based at least in part on the user attributes, the promotion attributes, and a distance-independent relevance model, an initial absolute relevance score for each of the set of available promotions, wherein the initial absolute relevance score represents an estimated absolute conversion probability for the promotion;   generate a subset of the available promotions by selecting promotions having an initial absolute relevance score above an absolute relevance score threshold; and   determine an optimal discovery region size for the user based at least in part on the subset of the available promotions.   
     
     
         26 . The apparatus of  claim 25 , wherein the at least one non-transitory computer readable storage medium stores instructions that, with the at least one processor, further cause the apparatus to:
 rank the subset of available promotions based at least in part on their respective initial absolute relevance scores.   
     
     
         27 . The apparatus of  claim 26 , wherein the at least one non-transitory computer readable storage medium stores instructions that, with the at least one processor, further cause the apparatus to:
 generate featured result set thresholding based at least in part on the ranked subset of available promotions.   
     
     
         28 . The apparatus of  claim 26 , wherein the at least one non-transitory computer readable storage medium stores instructions that, with the at least one processor, further cause the apparatus to:
 generate at least one personalized notification trigger for the user based at least in part on the ranked subset of available promotions, the user attributes, and the promotion attributes.   
     
     
         29 . The apparatus of  claim 25 , wherein determining the optimal discovery region size comprises:
 receiving initialization parameters including a target quantity of promotions to be scored by a core conversion rate model, a minimum conversion rate of interest to the consumer, and a promotion distance radius;   receiving a first subset of the available promotions that each are located within the promotion distance radius of the consumer location;   calculating a first count of the first subset of the available promotions that are associated with an absolute relevance score that is greater than or equal to the minimum conversion rate of interest;   comparing the first count to a scores count threshold; and   in an instance in which the first count is greater than or equal to the scores count threshold,   comparing the first count to the target quantity of promotions; and   in an instance in which the first count is less than or equal to the target quantity,   selecting the first subset of promotions to be scored by the core conversion rate model.   
     
     
         30 . The apparatus of  claim 29 , wherein determining an optimal discovery region size for the user comprises determining a personalized local merchant discovery frontier for the user. 
     
     
         31 . At least one non-transitory computer readable storage medium storing instructions that, with at least one processor, cause an apparatus to:
 receive input data representing a user request, the input data comprising a user location;   retrieve, from one or more repositories, user attributes associated with a user associated with the user request;   retrieve, from the one or more repositories, promotion attributes associated with each of a set of available promotions for the user;   generate, based at least in part on the user attributes, the promotion attributes, and a distance-independent relevance model, an initial absolute relevance score for each of the set of available promotions, wherein the initial absolute relevance score represents an estimated absolute conversion probability for the promotion;   generate a subset of the available promotions by selecting promotions having an initial absolute relevance score above an absolute relevance score threshold; and   determine an optimal discovery region size for the user based at least in part on the subset of the available promotions.   
     
     
         32 . The at least one non-transitory computer readable storage medium of  claim 31 , storing instructions that, with the at least one processor, further cause the apparatus to:
 rank the subset of available promotions based at least in part on their respective initial absolute relevance scores.   
     
     
         33 . The at least one non-transitory computer readable storage medium of  claim 32 , storing instructions that, with the at least one processor, further cause the apparatus to:
 generate featured result set thresholding based at least in part on the ranked subset of available promotions.   
     
     
         34 . The at least one non-transitory computer readable storage medium of  claim 31 , storing instructions that, with the at least one processor, further cause the apparatus to:
 generate at least one personalized notification trigger for the user based at least in part on the ranked subset of available promotions, the user attributes, and the promotion attributes.   
     
     
         35 . The at least one non-transitory computer readable storage medium of  claim 31 , wherein determining the optimal discovery region size comprises:
 receiving initialization parameters including a target quantity of promotions to be scored by a core conversion rate model, a minimum conversion rate of interest to the consumer, and a promotion distance radius;   receiving a first subset of the available promotions that each are located within the promotion distance radius of the consumer location;   calculating a first count of the first subset of the available promotions that are associated with an absolute relevance score that is greater than or equal to the minimum conversion rate of interest;   comparing the first count to a scores count threshold; and   in an instance in which the first count is greater than or equal to the scores count threshold,   comparing the first count to the target quantity of promotions; and   in an instance in which the first count is less than or equal to the target quantity,   selecting the first subset of promotions to be scored by the core conversion rate model.   
     
     
         36 . The at least one non-transitory computer readable storage medium of  claim 31 , wherein determining an optimal discovery region size for the user comprises determining a personalized local merchant discovery frontier for the user. 
     
     
         37 . A computer-implemented method, comprising:
 receiving input data representing a user request, the input data comprising a user location;   retrieving, from one or more repositories, user attributes associated with a user associated with the user request;   retrieving, from the one or more repositories, promotion attributes associated with each of a set of available promotions for the user;   generating, based at least in part on the user attributes, the promotion attributes, and a distance-independent relevance model, an initial absolute relevance score for each of the set of available promotions, wherein the initial absolute relevance score represents an estimated absolute conversion probability for the promotion;   generating a subset of the available promotions by selecting promotions having an initial absolute relevance score above an absolute relevance score threshold; and   determining an optimal discovery region size for the user based at least in part on the subset of the available promotions.   
     
     
         38 . The computer-implemented method of  claim 37 , further comprising:
 ranking the subset of available promotions based at least in part on their respective initial absolute relevance scores.   
     
     
         39 . The computer-implemented method of  claim 38 , further comprising:
 generating featured result set thresholding based at least in part on the ranked subset of available promotions.   
     
     
         40 . The computer-implemented method of  claim 37 , further comprising:
 generating at least one personalized notification trigger for the user based at least in part on the ranked subset of available promotions, the user attributes, and the promotion attributes.   
     
     
         41 . The computer-implemented method of  claim 37 , wherein determining the optimal discovery region size comprises:
 receiving initialization parameters including a target quantity of promotions to be scored by a core conversion rate model, a minimum conversion rate of interest to the consumer, and a promotion distance radius;   receiving a first subset of the available promotions that each are located within the promotion distance radius of the consumer location;   calculating a first count of the first subset of the available promotions that are associated with an absolute relevance score that is greater than or equal to the minimum conversion rate of interest;   comparing the first count to a scores count threshold; and   in an instance in which the first count is greater than or equal to the scores count threshold,   comparing the first count to the target quantity of promotions; and   in an instance in which the first count is less than or equal to the target quantity,   selecting the first subset of promotions to be scored by the core conversion rate model.   
     
     
         42 . The computer-implemented method of  claim 37 , wherein determining an optimal discovery region size for the user comprises determining a personalized local merchant discovery frontier for the user.