Ad placement

Abstract

This invention concerns optimal ad selection for Web pages by selecting and updating an attribute set, obtaining and updating an ad-attribute profile, and optimally choosing the next ad. The present invention associates a set of attributes with each customer. The attributes reflect the customers' interests and they incorporate the characteristics that impact ad selection. Similarly, the present invention associates with each ad an ad-attribute profile in order to calculate a customer's estimated ad selection probability and measure the uncertainty in that estimate. An ad selection algorithm optimally selects which ad to show based on the click probability estimates and the uncertainties regarding these estimates.

What is claimed is: 
     
         1 . A computer-implemented method comprising:
 associating user attributes with a user that indicate interests of the user;   receiving information regarding one or more network-based activities of the user on one or more network-based marketing mediums;   updating the user attributes based on the received information, wherein more recent information is given more weight than less recent information when updating the user attributes;   selecting one or more advertisements that relate to the updated user attributes to present to the user by determining selection probabilities for the one or more advertisements based on the updated user attributes; and   serving the selected one or more advertisements over a network to a computing device associated with the user.   
     
     
         2 . The method as recited in  claim 1 , further comprising updating the user attributes based on the received information by employing one or more matrices to store and retrieve attributes. 
     
     
         3 . The method as recited in  claim 1 , further comprising updating the user attributes using an exponentially-weighted approach or moving average. 
     
     
         4 . The method as recited in  claim 1 , further comprising updating the user attributes based on the received information by employing one or more matrices to store and retrieve attributes. 
     
     
         5 . The method as recited in  claim 1 , wherein selecting the one or more advertisements comprises:
 determining a probability that each of the one or more advertisements will be selected by the user; and   selecting a given advertisement from the one or more advertisements with a high probability to provide to the computing device associated with the user.   
     
     
         6 . The method as recited in  claim 5 , wherein the probability that the given advertisement will be selected by the user is a function of a click-thru-rate for the given advertisement. 
     
     
         7 . The method as recited in  claim 5 , further associating advertisement attributes with the one or more advertisements, the advertisement attributes reflecting a correlation value between each of the one or more advertisements and user attributes. 
     
     
         8 . The method as recited in  claim 7 , wherein the probability that the given advertisement will be selected by the user is a function of the user attributes and the advertisement attributes for the given advertisement. 
     
     
         9 . The method as recited in  claim 1 , wherein the one or more network-based marketing mediums comprise one or more websites or software program. 
     
     
         10 . The method as recited in  claim 9 , further comprising:
 determining interest categories in which the user commonly browses, wherein selecting the one or more advertisements comprises selecting advertisements related to the interest categories in which the user commonly browses; and   measuring a percentage of time the user spends browsing in one or more interest categories.   
     
     
         11 . The method as recited in  claim 1 , wherein the computing device associated with the user is a mobile device. 
     
     
         12 . A non-transitory computer-readable storage medium storing instructions that, when executed by at least one processor, cause a computer system to:
 associate user attributes with a user that indicate interests of the user;   receive information regarding one or more network-based activities of the user on one or more network-based marketing mediums;   update the user attributes based on the received information, wherein more recent information is given more weight than less recent information when updating the user attributes;   select one or more advertisements that relate to the updated user attributes to present to the user by determining selection probabilities for the one or more advertisements based on the updated user attributes; and   serve the selected one or more advertisements over a network to a computing device associated with the user.   
     
     
         13 . The non-transitory computer-readable storage medium as recited in  claim 12 , further comprising instructions that, when executed by the at least one processor, cause the computer system to update the user attributes based on the received information by employing one or more matrices to store and retrieve attributes. 
     
     
         14 . The non-transitory computer-readable storage medium as recited in  claim 12 , further comprising instructions that, when executed by the at least one processor, cause the computer system to update the user attributes based on the received information by employing one or more matrices to store and retrieve attributes. 
     
     
         15 . The non-transitory computer-readable storage medium as recited in  claim 12 , wherein the instructions that, when executed by the at least one processor, cause the computer system to select the one or more advertisements by:
 determining a probability that each of the one or more advertisements will be selected by the user; and   selecting a given advertisement from the one or more advertisements with a high probability to provide to the computing device associated with the user.   
     
     
         16 . The non-transitory computer-readable storage medium as recited in  claim 15 , wherein the probability that the given advertisement will be selected by the user is a function of a click-thru-rate for the given advertisement. 
     
     
         17 . The non-transitory computer-readable storage medium as recited in  claim 16 , further comprising instructions that, when executed by the at least one processor, cause the computer system to associate advertisement attributes with the one or more advertisements, the advertisement attributes reflecting a correlation value between each of the one or more advertisements and user attributes, wherein the probability that the given advertisement will be selected by the user is a function of the user attributes and the advertisement attributes for the given advertisement. 
     
     
         18 . A system comprising:
 at least one processor; and   at least one non-transitory computer readable storage medium storing instructions that, when executed by the at least one processor, cause the system to:
 associate user attributes with a user that indicate interests of the user; 
 receive information regarding one or more network-based activities of the user on one or more network-based marketing mediums; 
 update the user attributes based on the received information, wherein more recent information is given more weight than less recent information when updating the user attributes; 
 select one or more advertisements that relate to the updated user attributes to present to the user by determining selection probabilities for the one or more advertisements based on the updated user attributes; and 
 serve the selected one or more advertisements over a network to a computing device associated with the user. 
   
     
     
         19 . The system as recited in  claim 18 , further comprising instructions that, when executed by the at least one processor, cause the system to update the user attributes based on the received information by employing one or more matrices to store and retrieve attributes. 
     
     
         20 . The system as recited in  claim 18 , further comprising instructions that, when executed by the at least one processor, cause the system to update the user attributes based on the received information by employing one or more matrices to store and retrieve attributes.