System and method for using past or external information for future search results

Abstract

Various embodiments can include a system comprising one or more processing modules; a database system that can comprise a first database cluster H and a second database cluster L; and one or more non-transitory memory storage modules storing computing instructions. The computing instructions can be configured to run on the one or more processing modules and perform acts of: creating a first mapping of a product to search terms for the product on a first social media platform; analyzing a first popularity factor of the product on the first social media platform during a first time period; analyzing a second popularity factor of the product on the first social media platform during a second time period different from the first time period; comparing the first popularity factor of the product on the first social media platform with the second popularity factor of the product on the first social media platform to create a first popularity trend signal of the product; storing a record containing information about the product in either the first database cluster H or the second database cluster L using the first popularity trend signal of the product; receiving a search request from a requester; and presenting, in response to the search request, a search result that can comprise one or more records in a set of distinct records for a set of distinct products to the requester, wherein the set of distinct records can comprise the record and wherein the set of distinct products can comprise the product.

We claim: 
     
         1 . A system comprising:
 one or more processing modules;   a database system comprising a first database cluster H and a second database cluster L; and   one or more non-transitory memory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of:
 creating a first mapping of a product to search terms for the product on a first social media platform; 
 analyzing a first popularity factor of the product on the first social media platform during a first time period; 
 analyzing a second popularity factor of the product on the first social media platform during a second time period different from the first time period; 
 comparing the first popularity factor of the product on the first social media platform with the second popularity factor of the product on the first social media platform to create a first popularity trend signal of the product; 
 storing a record containing information about the product in either the first database cluster H or the second database cluster L using the first popularity trend signal of the product; 
 receiving a search request from a requester; and 
 presenting, in response to the search request, a search result comprising one or more records in a set of distinct records for a set of distinct products to the requester, wherein the set of distinct records comprise the record and wherein the set of distinct products comprise the product. 
   
     
     
         2 . The system of  claim 1 , wherein the computing instructions are further configured to perform acts of:
 for each distinct product in the set of distinct products:
 creating a first mapping of the distinct product in the set of distinct products to search terms for the distinct product in the set of distinct products on the first social media platform; 
 analyzing a first popularity factor of the distinct product in the set of distinct products on the first social media platform during a third time period; 
 analyzing a second popularity factor of the distinct product in the set of distinct products on the first social media platform during a fourth time period different from the third time period; 
 comparing the first popularity factor of the distinct product in the set of distinct products on the first social media platform with the second popularity factor of the distinct product in the set of distinct products on the first social media platform to create a first popularity trend signal of the distinct product in the set of distinct products; and 
 storing a distinct record in the set of distinct records containing information about the distinct product in the set of distinct products in either the first database cluster H or the second database cluster L using the first popularity trend signal of the distinct product in the set of distinct products. 
   
     
     
         3 . The system of  claim 2 , wherein the computing instructions are further configured to perform acts of:
 for each distinct product in the set of distinct products:
 creating a second mapping of the distinct product in the set of distinct products to search terms for the distinct product in the set of distinct products on the second social media platform; 
 analyzing a second popularity factor of the distinct product in the set of distinct products on the second social media platform during a fifth time period; 
 analyzing a second popularity factor of the distinct product in the set of distinct products on the second social media platform during a sixth time period different from the fifth time period; 
 comparing the first popularity factor of the distinct product in the set of distinct products on the second social media platform with the second popularity factor of the distinct product in the set of distinct products on the second social media platform to create a second popularity trend signal of the distinct product in the set of distinct products; 
 combining the first popularity trend signal of the distinct product in the set of distinct products and the second popularity trend signal of the distinct product in the set of distinct products to create an aggregate popularity trend signal of the distinct product in the set of distinct products; and 
 storing the distinct record in the set of distinct records containing information about the distinct product in the set of distinct products in either the first database cluster H or the second database cluster L using aggregate popularity trend signal of the distinct product in the set of distinct products. 
   
     
     
         4 . The system of  claim 1 , wherein the computing instructions are further configured to perform acts of:
 creating a second mapping of the product to the search terms for the product on a second social media platform different from the first social media platform;   analyzing a first popularity factor of the product on the second social media platform during a third time period;   analyzing a second popularity factor of the product on the second social media platform during a fourth time period different from the third time period;   comparing the first popularity factor of the product on the second social media platform with the second popularity factor of the product on the second social media platform to create a second popularity trend signal of the product;   combining the first popularity trend signal of the product and the second popularity trend signal of the product to create an aggregate popularity trend signal of the product; and   storing the record in either the first database cluster H or the second database cluster L using the aggregate popularity trend signal of the product.   
     
     
         5 . The system of  claim 1 , wherein the first time period and the second time period each comprise a time period with a length of twenty-four hours. 
     
     
         6 . The system of  claim 5 , wherein a time gap exists between the first time period and the second time period. 
     
     
         7 . The system of  claim 5 , wherein the first time period and the second time period overlap. 
     
     
         8 . The system of  claim 1 , wherein:
 the first database cluster H is stored on a first database server;   the second database cluster L is stored on a second database server different than the first database server;   the first database server has greater processing capabilities than the second database server; and   storing the record further comprises:
 storing the record containing the information about the product on the first database server when the first popularity trend signal is greater than a predefined threshold. 
   
     
     
         9 . The system of  claim 1  wherein the computing instructions are further configured to perform acts of:
 placing the first popularity trend signal in a feature vector; and 
 using the feature vector in a predictive model to determine where to store the record containing the information about the product, wherein storing the record further comprises storing the record containing the information about the product in either the first database cluster H or the second database cluster L based on using the feature vector in the predictive model. 
 
     
     
         10 . The system of  claim 1 , wherein the first mapping of the product to the search terms on the first social media platform comprises at least one of a generic mention or a specific mention. 
     
     
         11 . A method comprising:
 creating a first mapping of a product to search terms for the product on a first social media platform;   analyzing a first popularity factor of the product on the first social media platform during a first time period;   analyzing a second popularity factor of the product on the first social media platform during a second time period different from the first time period;   comparing the first popularity factor of the product on the first social media platform with the second popularity factor of the product on the first social media platform to create a first popularity trend signal of the product;   storing a record containing information about the product in either the first database cluster H or the second database cluster L using the first popularity trend signal of the product;   receiving a search request from a requester; and   presenting, in response to the search request, a search result comprising one or more records in a set of distinct records for a set of distinct products to the requester, wherein the set of distinct records comprise the record and wherein the set of distinct products comprise the product.   
     
     
         12 . The method of  claim 11  further comprising:
 for each distinct product in the set of distinct products:
 creating a first mapping of the distinct product in the set of distinct products to search terms for the distinct product in the set of distinct products on the first social media platform; 
 analyzing a first popularity factor of the distinct product in the set of distinct products on the first social media platform during a third time period; 
 analyzing a second popularity factor of the distinct product in the set of distinct products on the first social media platform during a fourth time period different from the third time period; 
 comparing the first popularity factor of the distinct product in the set of distinct products on the first social media platform with the second popularity factor of the distinct product in the set of distinct products on the first social media platform to create a first popularity trend signal of the distinct product in the set of distinct products; and 
 storing a distinct record in the set of distinct records containing information about the distinct product in the set of distinct products in either the first database cluster H or the second database cluster L using the first popularity trend signal of the distinct product in the set of distinct products. 
 
 
     
     
         13 . The method of  claim 12  further comprising:
 for each distinct product in the set of distinct products:
 creating a second mapping of the distinct product in the set of distinct products to search terms for the distinct product in the set of distinct products on the second social media platform; 
 analyzing a second popularity factor of the distinct product in the set of distinct products on the second social media platform during a fifth time period; 
 analyzing a second popularity factor of the distinct product in the set of distinct products on the second social media platform during a sixth time period different from the fifth time period; 
 comparing the first popularity factor of the distinct product in the set of distinct products on the second social media platform with the second popularity factor of the distinct product in the set of distinct products on the second social media platform to create a second popularity trend signal of the distinct product in the set of distinct products; 
 combining the first popularity trend signal of the distinct product in the set of distinct products and the second popularity trend signal of the distinct product in the set of distinct products to create an aggregate popularity trend signal of the distinct product in the set of distinct products; and 
 storing the distinct record in the set of distinct records containing information about the distinct product in the set of distinct products in either the first database cluster H or the second database cluster L using aggregate popularity trend signal of the distinct product in the set of distinct products. 
 
 
     
     
         14 . The method of  claim 11  further comprising:
 creating a second mapping of the product to the search terms for the product on a second social media platform different from the first social media platform; 
 analyzing a first popularity factor of the product on the second social media platform during a third time period; 
 analyzing a second popularity factor of the product on the second social media platform during a fourth time period different from the third time period; 
 comparing the first popularity factor of the product on the second social media platform with the second popularity factor of the product on the second social media platform to create a second popularity trend signal of the product; 
 combining the first popularity trend signal of the product and the second popularity trend signal of the product to create an aggregate popularity trend signal of the product; and 
 storing the record in either the first database cluster H or the second database cluster L using the aggregate popularity trend signal of the product. 
 
     
     
         15 . The method of  claim 11 , wherein the first time period and the second time period each comprise a time period with a length of twenty-four hours. 
     
     
         16 . The method of  claim 15 , wherein a time gap exists between the first time period and the second time period. 
     
     
         17 . The method of  claim 15 , wherein the first time period and the second time period overlap. 
     
     
         18 . The method of  claim 11 , wherein:
 the first database cluster H is stored on a first database server;   the second database cluster L is stored on a second database server different than the first database server;   the first database server has greater processing capabilities than the second database server; and   storing the record further comprises:
 storing the record containing the information about the product on the first database server when the first popularity trend signal is greater than a predefined threshold. 
   
     
     
         19 . The method of  claim 11  further comprising:
 placing the first popularity trend signal in a feature vector; and 
 using the feature vector in a predictive model to determine where to store the record containing the information about the product, wherein storing the record further comprises storing the record containing the information about the product in either the first database cluster H or the second database cluster L based on using the feature vector in the predictive model. 
 
     
     
         20 . The system of  claim 11 , wherein the first mapping of the product to the search terms on the first social media platform comprises at least one of a generic mention or a specific mention.