Economic optimization for product search relevancy

Abstract

In one embodiment, a method is illustrated as including defining a set of perspective objects capable of being placed onto a modified web page, monitoring parameters of a web page, the parameters including a number of times a current object is executed on the web page, using an Artificial Intelligence (AI) algorithm to determine a perspective object with a preferred Return On Investment (ROI), and selecting the perspective object to be placed onto the modified web page.

1 . A computer system comprising:
 a definition engine to define a set of objects capable of being placed onto a web page;   a monitor to capture content request data including a number of times an object, of the set of objects, is executed; and   a selection engine to select the object for placement onto the web page, the selecting including a use of an algorithm to determine a Return On Investment (ROI) associated with the object.   
     
     
         2 . The computer system of  claim 1 , further comprising:
 an update module to update a decision tree to include the captured content request data, the decision tree including members of a traffic optimizer module set as nodes; and   a traversal module to traverse the decision tree and selecting a member of the traffic optimizer module set, the selecting based upon the ROI by the member of the traffic optimizer module set.   
     
     
         3 . The computer system of  claim 2 , wherein the member of the traffic optimizer module set includes at least one of a page-type, merchandising, ad, or navigation module. 
     
     
         4 . The computer system of  claim 2 , further comprising a calculator to calculate the ROI based upon a difference between a cost associated with generating the web page, and a revenue amount generated by the web page. 
     
     
         5 . The computer system of  claim 2 , further comprising a calculator to calculate the ROI relative to another ROI. 
     
     
         6 . The computer system of  claim 2 , further comprising a histogram engine to create a histogram relating to keyword usage, the histogram including a portion of the content request data that identifies the member of the traffic optimizer module set. 
     
     
         7 . The computer system of  claim 1 , further comprising:
 a first retrieval engine to retrieve a set of histograms and item types, the set of histograms including the content request data;   a category engine to categorize each histogram of the set of histograms based upon the item type, the item type including items to he sold;   a filter to filter the set of histograms using a configuration rules set, the filtering to remove histograms from the set of histograms that violate a rule in the configuration rules set; and   a sorting engine to sort the set of histograms.   
     
     
         8 . The computer system of  claim 7 , further comprising:
 a classification engine to divide the set of histograms by classification, the classification relating at least one member of the set of histograms to at least one member of a traffic optimizer module set; and   a histogram selector to select at least one histogram from the set of histograms, the selection based upon the relationship between the member of the set of histograms and he least one member of the traffic optimizer module set.   
     
     
         9 . The computer system of  claim 7 , further comprising:
 a second retrieval engine to retrieve a histogram from the set of histograms;   a third retrieval engine to retrieve object data associated with the histogram, the retrieving based upon the ROI as determined by at least one AI algorithm; and   a transmitter to transmit an object associated with the object data as part of a web page instruction set.   
     
     
         10 . The computer system of  claim 7 , wherein the algorithm is an AI algorithm that includes at least one of a deterministic, or non-deterministic AI algorithm. 
     
     
         11 . A non-transitory machine-readable medium storing instructions, which, when executed by one or more processors of one or more machines, cause the one or more machines to perform a method comprising:
 define a set of objects capable of being placed onto a web page;   capture content request data including a number of times an object, of the set of objects, is executed; and   select the object for placement onto the web page, the selecting including a use of an algorithm to determine a Return On Investment (ROI) associated with the object.   
     
     
         12 . The non-transitory machine-readable medium of  claim 11 , storing additional instructions, which, when executed, cause the machine to additionally:
 update a decision tree to include the captured content request data, the decision tree including members of a traffic optimizer module set as nodes; and   traverse the decision tree and selecting a member of the traffic optimizer module set, the selecting based upon the ROI by the member of the traffic optimizer module set.   
     
     
         13 . The non-transitory machine-readable medium of  claim 12 , wherein the member of the traffic optimizer module set includes at least one of a page-type, merchandising, ad, or navigation module. 
     
     
         14 . The non-transitory machine-readable medium of  claim 12 , storing additional instructions, which, when executed, cause the machine to additionally:
 calculate the ROI based upon a difference between a cost associated with generating the web page, and a revenue amount generated by the web page.   
     
     
         15 . The non-transitory machine-readable medium of  claim 12 , storing additional instructions, which, when executed, cause the machine to additionally:
 calculate the ROI relative to another ROI.   
     
     
         16 . The non-transitory machine-readable medium of  claim 12 , storing additional instructions, which, when executed, cause the machine to additionally:
 create a histogram relating to keyword usage, the histogram including a portion of the content request data that identifies the member of the traffic optimizer module set.   
     
     
         17 . The non-transitory machine-readable medium of  claim 11 , storing additional instructions, which, when executed, cause the machine to additionally:
 retrieve a set of histograms and item types, the set of histograms including the content request data;   categorize each histogram of the set of histograms based upon the item type, the item type including items to be sold;   filter the set of histograms using a configuration rules set, the filtering to remove histograms from the set of histograms that violate a rule in the configuration rules set; and   sort the set of histograms.   
     
     
         18 . The non-transitory machine-readable medium of  claim 17 , storing additional instructions, which, when executed, cause the machine to additionally:
 divide the set of histograms by classification, the classification relating at least one member of the set of histograms to at least one member of a traffic optimizer module set; and   select at least one histogram from the set of histograms, the selection based upon the relationship between the member of the set of histograms and the least one member of the traffic optimizer module set.   
     
     
         19 . The non-transitory machine-readable medium of  claim 17 , storing additional instructions, which, when executed, cause the machine to additionally:
 retrieve a histogram from the set of histograms;   retrieve object data associated with the histogram, the retrieving based upon the ROI as determined by at least one AI algorithm; and   transmit an object associated with the object data as part of a web page instruction set.   
     
     
         20 . The non-transitory machine-readable medium of  claim 17 , wherein the algorithm is an AI algorithm that includes at least one of a deterministic, or non-deterministic AI algorithm.