System and method for determining an event occurrence rate

Abstract

Described are a system and method for determined an event occurrence rate. A sample set of content items may be obtained. Each of the content items may be associated with at least one region in a hierarchical data structure. A first impression volume may be determined for the at least one region as a function of a number of impressions registered for the content items associated with the at least one region. A scale factor may be applied to the first impression volume to generate a second impression volume. The scale factor may be selected so that the second impression volume is within a predefined range of a third impression volume. A click-through-rate (CTR) may be estimated as a function of the second impression volume and a number of clicks on the content item.

1 . A computer-implemented method, comprising:
 electronically, obtaining, via a processing device, a sample set of content items, each of the content items including a plurality of features associated with at least one region in a hierarchical data structure, the hierarchical data structure comprising nodes in an advertisement taxonomy hierarchy and nodes in a page taxonomy hierarchy, with the at least one region identified by a combination of nodes from the advertisement taxonomy hierarchy and nodes from the page taxonomy hierarchy, wherein the sample set is representation of a whole set of content items including features associated with the at least one region;   determining a first impression volume for each of the features corresponding to at least one region as a function of a number of impressions registered for a given content item from the sample set of content items;   applying a scale factor to the first impression volume to generate a second impression volume, the scale factor being selected so that the second impression volume is within a predefined range of a third impression volume;   electronically, estimating, via the processing device, a click-through-rate (CTR) as a function of the second impression volume and a number of clicks on the content item.   
     
     
         2 . The method according to  claim 1 , wherein the content items include at least one of webpages and ads. 
     
     
         3 . The method according to  claim 1 , wherein the obtaining includes:
 identifying first content items that have been clicked;   identifying a predetermined number of second content items that have not been clicked; and   generating the sample set as a function of the first and second content items.   
     
     
         4 . The method according to  claim 3 , further comprising:
 calculating the first impression volume as a function of the impressions for the first and second content items.   
     
     
         5 . The method according to  claim 1 , wherein the third impression volume is a total number of impressions associated within a preselected level in the hierarchical data structure. 
     
     
         6 . The method according to  claim 1 , wherein the estimating includes:
 assigning a state variable to each of the at least one region; and   applying a Markovian model to the state variable to estimate the CTR.   
     
     
         7 . The method according to  claim 6 , wherein the applying includes:
 computing a posterior for the state variable using a Kalman filter; and   propagating the posterior to the at least one region; and   repeating the computing and the propagating until convergence of the state variable to the CTR.   
     
     
         8 . The method according to  claim 7 , further comprising:
 upon the convergence, identifying the CTR for the at least one region.   
     
     
         9 . The method according to  claim 1 , further comprising:
 storing the CTR on a storage medium.   
     
     
         10 . Computer readable media comprising program code that when executed by a programmable processor causes the processor to execute a method, the method comprising:
 obtaining a sample set of content items, each of the content items including a plurality of features associated with at least one region in a hierarchical data structure, the hierarchical data structure comprising nodes in an advertisement taxonomy hierarchy and nodes in a page taxonomy hierarchy, with the at least one region identified by a combination of nodes from the advertisement taxonomy hierarchy and nodes from content items including features associated with the at least one region;   determining a first impression volume for each of the features corresponding to at least one region as a function of a number of impressions registered for a given content item from the sample set of content items;   applying a scale factor to the first impression volume to generate a second impression volume, the scale factor being selected so that the second impression volume is within a predefined range of a third impression volume;   estimating a click-through-rate (CTR) as a function of the second impression volume and a number of clicks on the content item.   
     
     
         11 . The computer readable media of  claim 10 , wherein the content items include at least one of webpages and ads. 
     
     
         12 . The computer readable media of  claim 10 , wherein the obtaining includes:
 identifying first content items that have been clicked;   identifying a predetermined number of second content items that have not been clicked; and   generating the sample set as a function of the first and second content items.   
     
     
         13 . The computer readable media of  claim 12 , further comprising:
 calculating the first impression volume as a function of the impressions for the first and second content items.   
     
     
         14 . The computer readable media of  claim 10 , wherein the third impression volume is a total number of impressions associated within a preselected level in the hierarchical data structure. 
     
     
         15 . The computer readable media of  claim 10 , wherein the estimating includes:
 assigning a state variable to each of the at least one region; and   applying a Markovian model to the state variable to estimate the CTR.   
     
     
         16 . The computer readable media of  claim 15 , wherein the applying includes:
 computing a posterior for the state variable using a Kalman filter; and   propagating the posterior to the at least one region; and   repeating the computing and the propagating until convergence of the state variable to the CTR.   
     
     
         17 . The computer readable media of  claim 16 , further comprising:
 upon the convergence, identifying the CTR for the at least one region.   
     
     
         18 . A system comprising a processor and a memory device storing executable instructions thereon that when executed causes the processor to perform a method comprising:
 obtaining a sample set of content items, each of the content items including a plurality of features associated with at least one region in a hierarchical data structure, the hierarchical data structure comprising nodes in an advertisement taxonomy hierarchy and nodes in a page taxonomy hierarchy, with the at least one region identified by a combination of nodes from the advertisement taxonomy hierarchy and nodes from the page taxonomy hierarchy, wherein the sample set is representation of a whole set of content items including features associated with the at least one region;   determining a first impression volume for each of the features corresponding to at least one region as a function of a number of impressions registered for a given content item from the sample set of content items;   applying a scale factor to the first impression volume to generate a second impression volume, the scale factor being selected so that the second impression volume is within a predefined range of a third impression volume;   estimating a click-through-rate (CTR) as a function of the second impression volume and a number of clicks on the content item.   
     
     
         19 . The system of  claim 18 , wherein the content items include at least one of webpages and ads. 
     
     
         20 . The system of  claim 18 , wherein the obtaining includes:
 identifying first content items that have been clicked;   identifying a predetermined number of second content items that have not been clicked; and   generating the sample set as a function of the first and second content items.   
     
     
         21 . The system of  claim 20 , further comprising:
 calculating the first impression volume as a function of the impressions for the first and second content items.   
     
     
         22 . The system of  claim 18 , wherein the third impression volume is a total number of impressions associated within a preselected level in the hierarchical data structure. 
     
     
         23 . The system of  claim 18 , wherein the estimating includes:
 assigning a state variable to each of the at least one region; and   applying a Markovian model to the state variable to estimate the CTR.   
     
     
         24 . The system of  claim 23 , wherein the applying includes:
 computing a posterior for the state variable using a Kalman filter; and   propagating the posterior to the at least one region; and   repeating the computing and the propagating until convergence of the state variable to the CTR.   
     
     
         25 . The system of  claim 24 , further comprising:
 upon the convergence, identifying the CTR for the at least one region.