Determining Utility Functions from Ordinal Rankings

Abstract

A user's preference structure in respect of alternative “objects” with which the user is presented is captured in a multi-attribute utility function. The user ranks these competing objects in order of the user's relative preference for such objects. A utility function that defines the user's preference structure is provided as output on the basis of this relative ranking. This technique can be used to assist a buyer in selecting between multi-attribute quotes or bids submitted by prospective suppliers to the buyer.

1 . A method of providing goods or services, said method comprising: 
 requesting a list of goods or services available from a supplier for purchase by a buyer through a computer system;    associating attributes with said goods or services, wherein said attributes correspond to characteristics of said goods or services and are measured according to a quantifiable standard;    providing a subset list of said goods or services as a training example to a multi-layered neural network;    automatically generating a utility function comprising weighted components associated with said attributes based on training performed by the neural network;    automatically ranking all said goods or services according to said utility function;    presenting to said buyer a ranked list comprising a pair of the highest ranked goods or services as a representative sample from all said goods or services available from said supplier;    evaluating said ranked list to determine whether said highest ranked goods or services match a buyer preference corresponding to said attributes of said goods or services;    submitting the evaluation to said neural network;    inputting said evaluation into said utility function;    re-ranking all said goods or services according to said attributes and said evaluation;    presenting to said buyer a re-ranked list comprising a pair of the highest re-ranked goods or services as a representative sample from all said goods or services available from said supplier;    re-evaluating said re-ranked list to determine whether said highest re-ranked goods or services match said buyer preference corresponding to said attributes of said goods or services;    iteratively repeating the submitting process through the re-evaluating process until said re-ranked list matches said ranked list; and    presenting to said buyer a final ranked list of all said goods or services ranked according to buyer preferences corresponding to said attributes.    
   
   
       2 . The method of  claim 1 , all the limitations of which are incorporated herein by reference, wherein said subset list of said goods or services is substantially less than a total number of said goods or services that are being ranked.  
   
   
       3 . The method of  claim 2 , all the limitations of which are incorporated herein by reference, wherein said subset list of said goods or services is less than one percent of said total number of said goods or services that are being ranked.  
   
   
       4 . The method of  claim 1 , all the limitations of which are incorporated herein by reference, further comprising: 
 calculating an error measure associated with said utility function;    revising utility function in response to said error measure; and    performing the calculating of said error measure and the revising until said error measure results in a user-defined satisfactory difference between said re-ranked list and said ranked list.    
   
   
       5 . The method of  claim 1 , all the limitations of which are incorporated herein by reference, wherein said quantifiable standard comprises any of a numeric measure and a true/false measure.  
   
   
       6 . The method of  claim 1 , all the limitations of which are incorporated herein by reference, wherein said neural network is a feed-forward neural network.  
   
   
       7 . The method of  claim 1 , all the limitations of which are incorporated herein by reference, wherein said subset list of said goods or services comprises a ranking of said goods or services.  
   
   
       8 . The method of  claim 7 , all the limitations of which are incorporated herein by reference, wherein the ranking in said subset list is performed by said buyer.  
   
   
       9 . The method of  claim 1 , all the limitations of which are incorporated herein by reference, further comprising performing the repeating process a fixed predetermined number of times.  
   
   
       10 . The method of  claim 1 , all the limitations of which are incorporated herein by reference, wherein said utility function is mathematically denoted as ƒ(x), 
 wherein                f   ⁢           ⁢     (   x   )       =       a             ⁢   0       +       ∑   i     ⁢       a             ⁢   i       ⁢           ⁢     x             ⁢   i           +       ∑     i   ,   j       ⁢           ⁢       a             ⁢   ij       ⁢           ⁢     x             ⁢   i       ⁢           ⁢     x             ⁢   j               ,           wherein a is a good or service; x is said pair of the highest ranked goods or services; and i and j are attributes of said goods or services.    
   
   
       11 . A program storage device readable by computer, tangibly embodying a program of instructions executable by said computer to perform a method of providing goods or services, said method comprising: 
 requesting a list of goods or services available from a supplier for purchase by a buyer through a computer system;    associating attributes with said goods or services, wherein said attributes correspond to characteristics of said goods or services and are measured according to a quantifiable standard;    providing a subset list of said goods or services as a training example to a multi-layered neural network;    automatically generating a utility function comprising weighted components associated with said attributes based on training performed by the neural network;    automatically ranking all said goods or services according to said utility function;    presenting to said buyer a ranked list comprising a pair of the highest ranked goods or services as a representative sample from all said goods or services available from said supplier;    evaluating said ranked list to determine whether said highest ranked goods or services match a buyer preference corresponding to said attributes of said goods or services;    submitting the evaluation to said neural network;    inputting said evaluation into said utility function;    re-ranking all said goods or services according to said attributes and said evaluation;    presenting to said buyer a re-ranked list comprising a pair of the highest re-ranked goods or services as a representative sample from all said goods or services available from said supplier;    re-evaluating said re-ranked list to determine whether said highest re-ranked goods or services match said buyer preference corresponding to said attributes of said goods or services;    iteratively repeating the submitting process through the re-evaluating process until said re-ranked list matches said ranked list; and    presenting to said buyer a final ranked list of all said goods or services ranked according to buyer preferences corresponding to said attributes.    
   
   
       12 . The program storage device of  claim 11 , all the limitations of which are incorporated herein by reference, wherein said subset list of said goods or services is substantially less than a total number of said goods or services that are being ranked.  
   
   
       13 . The program storage device of  claim 12 , all the limitations of which are incorporated herein by reference, wherein said subset list of said goods or services is less than one percent of said total number of said goods or services that are being ranked.  
   
   
       14 . The program storage device of  claim 11 , all the limitations of which are incorporated herein by reference, further comprising: 
 calculating an error measure associated with said utility function;    revising utility function in response to said error measure; and    performing the calculating of said error measure and the revising until said error measure results in a user-defined satisfactory difference between said re-ranked list and said ranked list.    
   
   
       15 . The program storage device of  claim 11 , all the limitations of which are incorporated herein by reference, wherein said quantifiable standard comprises any of a numeric measure and a true/false measure.  
   
   
       16 . The program storage device of  claim 11 , all the limitations of which are incorporated herein by reference, wherein said neural network is a feed-forward neural network.  
   
   
       17 . The program storage device of  claim 11 , all the limitations of which are incorporated herein by reference, wherein said subset list of said goods or services comprises a ranking of said goods or services.  
   
   
       18 . The program storage device of  claim 17 , all the limitations of which are incorporated herein by reference, wherein the ranking in said subset list is performed by said buyer.  
   
   
       19 . The program storage device of  claim 11 , all the limitations of which are incorporated herein by reference, further comprising performing the repeating process a fixed predetermined number of times.  
   
   
       20 . The program storage device of  claim 11 , all the limitations of which are incorporated herein by reference, wherein said utility function is mathematically denoted as ƒ(x), wherein  
     
       
         
           
             
               
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     wherein a is a good or service; x is said pair of the highest ranked goods or services; and i and j are attributes of said goods or services.