Article trading process

Abstract

Articles such as compact discs are traded among a plurality of members who are registered in a common community. Each member has an account including a trading credit balance and a mailing address. A user interface is provided that allows a member to enter articles that the member owns and is willing to trade and articles that the member wants to own. The user interface also receives article trading instruction messages. An administration computer hosts the plurality of members and facilitates article trades among the members. The administration computer identifies matches between owned and wanted articles. For each identified match, an article trading instruction message is sent to the article owner requesting that the article owner mail the owned article to the mailing address of the member who wants to own the article. If the member sends the article, a trading credit is posted to the member's account and a trading debit and a monetary charge is posted to the member's account who will be receiving the article. Each traded article has the same trading credit. A priority algorithm is used to select the member who will receive the article if there is more than one member who wants an article that another member owns and is willing to trade. The priority algorithm is based in part on a member score that is a function of the relative value of the articles that a member owns and sends to another member compared to the relative value of the articles that the member wants and receives from another member, the relative value of the articles being determined by a demand for the article compared to a supply for the article among the members. A portion of the monetary charge is placed in a pool for distribution to the content creator of the article.

1 - 102 . (canceled) 
     
     
         103 . A computer-implemented method comprising:
 providing a user interface for members of a community;   receiving member data associated with each member, the member data comprising member identities and article data that is associated with each article associated with each member;   identifying members having associated article data that are similar to the article data associated with other members, the identifications being based on a similarity algorithm;   grouping a predetermined number of members identified as having similar article data into an interest group; and   enabling a member of an interest group to view the article data of other members of the interest group.   
     
     
         104 . The method of  claim 103 , further comprising:
 receiving a request by a first member to indicate that an article associated with the first member is available for trade;   receiving a request by a second member to acquire an article; and   displaying, to the second member, member data associated with members associated with the requested article and who have indicated that the article is available for trade.   
     
     
         105 . The method of  claim 104 , further comprising:
 receiving a request by the second member to acquire the article associated with a member selected by the second member; and   notifying the selected member of the request to acquire.   
     
     
         106 . The method of  claim 105 , wherein the articles comprise electronic sound recordings and the member data comprise listen data. 
     
     
         107 . The method of  claim 103 , wherein the identification of members having similar article data is determined by a numerical representation of the similarity based on a distance function, and wherein the user interface displays the numerical representation for each of the members whose article data is displayed. 
     
     
         108 . The method of  claim 103 , wherein the similarity algorithm comprises determining the cardinal of the intersection between the article data associated with one member and the article data associated with a different member for each member of the community, wherein each member is represented as a row vector in a matrix and each item of article data associated with each member is a column vector in the matrix. 
     
     
         109 . The method of  claim 108 , wherein the similarity algorithm further comprises adding data associated with the member's most recent query to the article data associated with the member. 
     
     
         110 . The method of  claim 103 , wherein the similarity algorithm comprises computing a histogram yielding one row per member and one column per item of article data, wherein the similarity between two members is computed with the formula:
   Distance( X, Y )=| A|−|A  inter  B|     
       wherein X and Y are two members of the community, A is the total number of article data associated with member X, B is the total number of article data associated with member Y, and |A inter B| is the total number of article data associated with member Y that is also associated with member X. 
     
     
         111 . The method of  claim 110 , wherein the similarity algorithm further comprises adding data associated with the member's most recent query to the article data associated with the member. 
     
     
         112 . The method of  claim 110 , wherein the similarity algorithm further comprises dividing Distance(X, Y) by the average number of article data associated with member X and member Y. 
     
     
         113 . The method of  claim 103 , wherein the similarity algorithm comprises:
 associating each item of article data with one of a plurality of genre;   calculating the probability distribution for each genre associated with the article data associated with each member;   wherein the similarity between two members is computed for each genre with the formula:
   Distance( X, Y )=Σ( p *log( p/q ))
 
   wherein X and Y are two members of the community, p is the probability distribution for a genre associated with member X, and q is the probability distribution associated with Y for the same genre.   
     
     
         114 . The method of  claim 113 , wherein the similarity algorithm further comprises adding data associated with the member's most recent query to the article data associated with the member. 
     
     
         115 . A system for displaying identities of members who are interested in similar articles as other members of a community, the system comprising:
 a processor;   a module configured to control the processor to provide a user interface for members of a community;   a module configured to control the processor to receive member data associated with each member, the member data comprising member identities and article data that is associated with each article associated with each member;   a module configured to control the processor to identify members having associated article data that are similar to the article data associated with other members, the identification based on a similarity algorithm;   a module configured to control the processor to group a predetermined number of members identified as having similar article data into an interest group; and   a module configured to control the processor to enable a member of an interest group to view the article data of other members of the interest group.   
     
     
         116 . The system of  claim 115 , further comprising:
 a module configured to control the processor to receive a request by a first member to indicate that an article associated with the first member is available for trade;   a module configured to control the processor to receive a request by a second member to acquire an article; and   a module configured to control the processor to display to the second member data associated with members associated with the requested article and who have indicated that the article is available for trade.   
     
     
         117 . The system of  claim 116 , further comprising:
 a module configured to control the processor to receive a request by the second member to acquire the article associated with a member selected by the second member; and   a module configured to control the processor to notify the selected member of the request to acquire.   
     
     
         118 . The system of  claim 117 , wherein the articles comprise electronic sound recordings and the member data comprise listen data. 
     
     
         119 . The system of  claim 115 , wherein the identification of members having similar article data is determined by a numerical representation of the similarity based on a distance function, and wherein the user interface displays the numerical representation for each of the members whose article data is displayed. 
     
     
         120 . The system of  claim 115 , wherein the similarity algorithm comprises determining the cardinal of the intersection between the article data associated with one member and the article data associated with a different member for each member of the community, wherein each member is represented as a row vector in a matrix and each item of article data associated with each member is a column vector in the matrix. 
     
     
         121 . The system of  claim 120 , wherein the similarity algorithm further comprises adding data associated with the member's most recent query to the article data associated with the member. 
     
     
         122 . The system of  claim 115 , wherein the similarity algorithm comprises computing a histogram yielding one row per member and one column per item of article data, wherein the similarity between two members is computed with the formula:
   Distance( X, Y )=| A|−|A  inter  B|     
       wherein X and Y are two members of the community, A is the total number of article data associated with member X, B is the total number of article data associated with member Y, and |A inter B| is the total number of article data associated with member Y that is also associated with member X. 
     
     
         123 . The system of  claim 122 , wherein the similarity algorithm further comprises adding data associated with the member's most recent query to the article data associated with the member. 
     
     
         124 . The system of  claim 122 , wherein the similarity algorithm further comprises dividing Distance(X, Y) by the average number of article data associated with member X and member Y. 
     
     
         125 . The system of  claim 115 , wherein the similarity algorithm further comprises:
 associating each item of article data with one of a plurality of genre;   calculating the probability distribution for each genre associated with the article data associated with each member;   wherein the similarity between two members is computed for each genre with the formula:
   Distance( X, Y )=Σ( p *log( p/q ))
 
   wherein X and Y are two members of the community, p is the probability distribution for a genre associated with member X, and q is the probability distribution associated with Y for the same genre.   
     
     
         126 . The system of  claim 125 , wherein the similarity algorithm further comprises adding data associated with the member's most recent query to the article data associated with the member. 
     
     
         127 . A computer-implemented method comprising:
 providing a user interface;   receiving article interest data associated with each member, the article interest data comprising:
 (i) article information associated with articles that each member indicates an interest in, 
 (ii) article information associated with articles that each member owns, and 
 (iii) article information associated with articles that each member wants to own; 
   calculating how similar the article interest data of a member is to the article interest data of other members, comprising a similarity algorithm; and   displaying the identities of a predetermined number of members calculated to have similar article interest data.   
     
     
         128 . The method of  claim 127 , further comprising:
 receiving a request by a first member to indicate that an article associated with the first member is available for trade;   receiving a request by a second member to acquire an article; and   displaying, to the second member, member data associated with members associated with the requested article and who have indicated that the article is available for trade.   
     
     
         129 . The method of  claim 128 , further comprising:
 receiving a request by the second member to acquire the article associated with a member selected by the second member; and   notifying the selected member of the request to acquire.   
     
     
         130 . The method of  claim 129 , wherein the articles comprise electronic sound recordings and the member data comprise listen data. 
     
     
         131 . The method of  claim 127 , wherein the identification of members having similar article data is determined by a numerical representation of the similarity based on a distance function, and wherein the user interface displays the numerical representation for each of the members whose article data is displayed. 
     
     
         132 . The method of  claim 127 , wherein the similarity algorithm comprises determining the cardinal of the intersection between the article data associated with one member and the article data associated with a different member for each member of the community, wherein each member is represented as a row vector in a matrix and each item of article data associated with each member is a column vector in the matrix. 
     
     
         133 . The method of  claim 132 , wherein the similarity algorithm further comprises adding data associated with the member's most recent query to the article data associated with the member. 
     
     
         134 . The method of  claim 127 , wherein the similarity algorithm comprises computing a histogram yielding one row per member and one column per item of article data, wherein the similarity between two members is computed with the formula:
   Distance( X, Y )=| A|−|A  inter  B|     
       wherein X and Y are two members of the community, A is the total number of article data associated with member X, B is the total number of article data associated with member Y, and |A inter B| is the total number of article data associated with member Y that is also associated with member X. 
     
     
         135 . The method of  claim 134 , wherein the similarity algorithm further comprises adding data associated with the member's most recent query to the article data associated with the member. 
     
     
         136 . The method of  claim 134 , wherein the similarity algorithm further comprises dividing Distance(X, Y) by the average number of article data associated with member X and member Y. 
     
     
         137 . The method of  claim 127 , wherein the similarity algorithm further comprises:
 associating each item of article data with one of a plurality of genre;   calculating the probability distribution for each genre associated with the article data associated with each member;   wherein the similarity between two members is computed for each genre with the formula:
   Distance( X, Y )=( p *log( p/q )) 
   wherein X and Y are two members of the community, p is the probability distribution for a genre associated with member X, and q is the probability distribution associated with Y for the same genre.   
     
     
         138 . The method of  claim 137 , wherein the similarity algorithm further comprises adding data associated with the member's most recent query to the article data associated with the member. 
     
     
         139 . A system for displaying identities of members who are interested in similar articles as other members of a community, the system comprising:
 a processor;   a module configured to control the processor to provide a user interface;   a module configured to control the processor to receive article interest data associated with each member, the article interest data comprising:
 (i) article information associated with articles that each member indicates an interest in, 
 (ii) article information associated with articles that each member owns, and 
 (iii) article information associated with articles that each member wants to own; 
   a module configured to control the processor to calculate how similar the article interest data of a member is to the article interest data of other members, comprising a similarity algorithm; and   a module configured to control the processor to display the identities of a predetermined number of members calculated to have similar article interest data   
     
     
         140 . The system of  claim 139 , further comprising:
 a module configured to control the processor to receive a request by a first member to indicate that an article associated with the first member is available for trade;   a module configured to control the processor to receive a request by a second member to acquire an article;   a module configured to control the processor to display, to the second member, member data associated with members associated with the requested article and who have indicated that the article is available for trade   a module configured to control the processor to receive a request by the second member to acquire the article associated with a member selected by the second member; and   a module configured to control the processor to notify the selected member of the request to acquire.