Automatically determining in real-time a triggering model for personalized recommendations

Abstract

A method including building a recommendation triggering model. The method can include receiving, via a user device of a user through a network, an add-to-cart command associated with an anchor item for the user. The method further can include determining, in real-time after receiving the add-to-cart command, a recommendation for one or more complementary items of the anchor item for the user. The method also can include determining, in real-time after determining the recommendation, a recommendation confidence for the recommendation. The method additionally can include after determining the recommendation confidence, when the recommendation confidence is positive, transmitting, in real-time through the network, the one or more complementary items to be presented to the user via the user device. The method likewise can include after determining the recommendation confidence, when the recommendation confidence is not positive, refraining from transmitting the one or more complementary items to the user. Other embodiments are disclosed.

What is claimed is: 
     
         1 . A system comprising:
 one or more processors; and   one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform:
 receiving, via a user device of a user through a network, an add-to-cart command associated with an anchor item for the user; 
 determining, in real-time after receiving the add-to-cart command, a recommendation for one or more complementary items of the anchor item for the user; 
 determining, in real-time after determining the recommendation, a recommendation confidence for the recommendation; 
 after determining the recommendation confidence, when the recommendation confidence is positive, transmitting, in real-time through the network, the one or more complementary items to be presented to the user via the user device; and 
 after determining the recommendation confidence, when the recommendation confidence is not positive, refraining from transmitting the one or more complementary items to the user. 
   
     
     
         2 . The system in  claim 1 , wherein the computing instructions are further configured to perform:
 prior to determining the recommendation confidence for the recommendation, removing a duplicate item of the one or more complementary items when at least one of:
 one or more prior declined recommendations comprise the duplicate item; or a cart for the user comprises the duplicate item. 
   
     
     
         3 . The system in  claim 1 , wherein determining the recommendation confidence further comprises:
 determining an approximate entropy for one or more joint probability distributions, each respective joint probability distribution of the one or more joint probability distributions being associated with the user, the anchor item, and each of the one or more complementary items;   after determining the approximate entropy, when the approximate entropy is less than a predetermined uncertainty threshold, the recommendation confidence is positive; and   after determining the approximate entropy, when the approximate entropy is not less than the predetermined uncertainty threshold, the recommendation confidence is negative.   
     
     
         4 . The system in  claim 3 , wherein:
 a respective joint probability distribution for the user, the anchor item, and each of the one or more complementary items is determined based at least in part on one or more of:
 a recommendation add-to-cart score associated with one or more of: a transactional history of the user, one or more prior recommendations, or one or more feedbacks from the user associated with the one or more prior recommendations; 
 an add-to-cart score for the anchor item associated with the user; or 
 a respective likelihood score for each of the one or more complementary items associated with the anchor item and the user. 
   
     
     
         5 . The system in  claim 4 , wherein:
 the respective likelihood score for each of the one or more complementary items is determined based at least in part on one or more user attribute preferences of the user for one or more respective attributes associated with each of the one or more complementary items.   
     
     
         6 . The system in  claim 1 , wherein:
 determining the recommendation further comprises:
 determining each of the one or more complementary items based at least in part on one or more of:
 one or more complementary item types for an item type of the anchor item; 
 a respective item-item complementarity score for the each of the one or more complementary items associated with the anchor item; or 
 a respective user-item compatibility score for the each of the one or more complementary items associated with one or more system users, the one or more system users comprising the user. 
 
   
     
     
         7 . The system in  claim 6 , wherein:
 each of the one or more complementary items is associated with a respective co-bought score determined based at least in part on one of:
 the respective item-item complementarity score; 
 the respective user-item compatibility score; or 
 a respective item-type compatibility score; and 
   determining the recommendation further comprises:
 determining a respective rank for each of the one or more complementary items based on the respective co-bought score. 
   
     
     
         8 . The system in  claim 1 , wherein:
 determining the recommendation further comprises re-ranking the one or more complementary items based on a respective likelihood score for each of the one or more complementary items associated with the anchor item and the user.   
     
     
         9 . The system in  claim 1 , wherein the computing instructions are further configured to perform:
 prior to determining the recommendation confidence for the recommendation, re-ranking the one or more complementary items by boosting a boosted item of the one or more complementary items, when at least one of:
 one or more favorites of the user comprises the boosted item; or one or more promotional items of a retailer comprises the boosted item. 
   
     
     
         10 . The system in  claim 1 , wherein:
 transmitting the one or more complementary items to be presented to the user via the user device is performed:
 at a check out by the user; 
 in real-time after determining the recommendation confidence for the recommendation; or 
 at a session-change by the system. 
   
     
     
         11 . A method being implemented via execution of computing instructions configured to run at one or more processors and stored at one or more non-transitory computer-readable media, the method comprising:
 receiving, via a user device of a user through a network, an add-to-cart command associated with an anchor item for the user;   determining, in real-time after receiving the add-to-cart command, a recommendation for one or more complementary items of the anchor item for the user;   determining, in real-time after determining the recommendation, a recommendation confidence for the recommendation;   after determining the recommendation confidence, when the recommendation confidence is positive, transmitting, in real-time through the network, the one or more complementary items to be presented to the user via the user device; and   after determining the recommendation confidence, when the recommendation confidence is not positive, refraining from transmitting the one or more complementary items to the user.   
     
     
         12 . The method in  claim 11  further comprising:
 prior to determining the recommendation confidence for the recommendation, removing a duplicate item of the one or more complementary items when at least one of:
 one or more prior declined recommendations comprise the duplicate item; or a cart for the user comprises the duplicate item. 
 
 
     
     
         13 . The method in  claim 11 , wherein determining the recommendation confidence further comprises:
 determining an approximate entropy for one or more joint probability distributions, each respective joint probability distribution of the one or more joint probability distributions being associated with the user, the anchor item, and each of the one or more complementary items;   after determining the approximate entropy, when the approximate entropy is less than a predetermined uncertainty threshold, the recommendation confidence is positive; and   after determining the approximate entropy, when the approximate entropy is not less than the predetermined uncertainty threshold, the recommendation confidence is negative.   
     
     
         14 . The method in  claim 13 , wherein:
 a respective joint probability distribution for the user, the anchor item, and each of the one or more complementary items is determined based at least in part on one or more of:
 a recommendation add-to-cart score associated with one or more of a transactional history of the user, one or more prior recommendations, or one or more feedbacks from the user associated with the one or more prior recommendations; 
 an add-to-cart score for the anchor item associated with the user; or 
 a respective likelihood score for each of the one or more complementary items associated with the anchor item and the user. 
   
     
     
         15 . The method in  claim 14 , wherein:
 the respective likelihood score for each of the one or more complementary items is determined based at least in part on one or more user attribute preferences of the user for one or more respective attributes associated with each of the one or more complementary items.   
     
     
         16 . The method in  claim 11 , wherein:
 determining the recommendation further comprises:
 determining each of the one or more complementary items based at least in part on one or more of:
 one or more complementary item types for an item type of the anchor item; 
 a respective item-item complementarity score for the each of the one or more complementary items associated with the anchor item; or 
 a respective user-item compatibility score for the each of the one or more complementary items associated with one or more system users, the one or more system users comprising the user. 
 
   
     
     
         17 . The method in  claim 16 , wherein:
 each of the one or more complementary items is associated with a respective co-bought score determined by one of:
 the respective item-item complementarity score; 
 the respective user-item compatibility score; or 
 a respective item-type compatibility score; and 
   determining the recommendation further comprises:
 determining a respective rank for each of the one or more complementary items based on the respective co-bought score. 
   
     
     
         18 . The method in  claim 11 , wherein:
 determining the recommendation further comprises re-ranking the one or more complementary items based on a respective likelihood score for each of the one or more complementary items associated with the anchor item and the user.   
     
     
         19 . The method in  claim 11  further comprising:
 prior to determining the recommendation confidence for the recommendation, re-ranking the one or more complementary items by boosting a boosted item of the one or more complementary items, when at least one of:
 one or more favorites of the user comprises the boosted item; or one or more promotional items of a retailer comprises the boosted item. 
 
 
     
     
         20 . The method in  claim 11 , wherein:
 transmitting the one or more complementary items to be presented to the user via the user device is performed when one or more of:
 at a check out by the user; 
 in real-time after determining the recommendation confidence for the recommendation; or 
 at a session-change.