Social network-based recommendation

Abstract

Embodiments of the invention provide methods and program products for making a recommendation to a purchaser and/or member of a social network. A first aspect of the invention provides a method of making a recommendation to a purchaser, the method comprising: determining a plurality of features of a first product selected by a purchaser; prioritizing the plurality of features of the first product; and making at least one recommendation to the purchaser, the at least one recommendation being selected from a group consisting of: a second product sharing at least one feature of the first product and a social network connection determined to have purchased another product sharing at least one feature of the first product.

What is claimed is: 
     
         1 . A method of making a recommendation to a purchaser, the method comprising:
 determining a plurality of features of a first product selected by a purchaser;   prioritizing the plurality of features of the first product; and   making at least one recommendation to the purchaser, the at least one recommendation being selected from a group consisting of: a second product sharing at least one feature of the first product and a social network connection determined to have purchased another product sharing at least one feature of the first product.   
     
     
         2 . The method of  claim 1 , further comprising:
 broadcasting the plurality of features of the first product to a social network.   
     
     
         3 . The method of  claim 1 , wherein prioritizing the plurality of features includes comparing at least one of the plurality of features to at least one feature of at least one previously-purchased product. 
     
     
         4 . The method of  claim 3 , wherein the previously-purchased product was purchased by a member of a social network other than the purchaser. 
     
     
         5 . The method of  claim 3 , wherein comparing the at least one of the plurality of features to at least one feature of at least one previously-purchased product includes calculating a correlation value for the compared features. 
     
     
         6 . The method of  claim 5 , wherein prioritizing the plurality of features includes ranking the correlation values. 
     
     
         7 . The method of  claim 6 , wherein a relatively low correlation value is deemed indicative of an anomalous feature having purchasing significance and is given a higher priority than a relatively high correlation value. 
     
     
         8 . The method of  claim 7 , further comprising:
 constructing a dendrogram including a plurality of products for recommendation, wherein each of the plurality of products for recommendation shares at least one feature of the first product.   
     
     
         9 . The method of  claim 8 , wherein constructing the dendrogram includes:
 including in a first level of the dendrogram a product that shares with the first product a feature having a highest ranked correlation value; and   including in a second level of the dendrogram a product that shares with the first product a feature having a second-highest ranked correlation value.   
     
     
         10 . The method of  claim 5 , further comprising:
 determining whether the at least one previously-purchased product was purchased within a predetermined period; and   in the case that the at least one previously-purchased product was purchased within the predetermined period, including the at least one previously-purchased product when calculating the correlation value.   
     
     
         11 . A program product stored on a computer-readable storage medium, which when executed, is operable to make a recommendation to a purchaser by performing a method comprising:
 determining a plurality of features of a first product selected by a purchaser;   prioritizing the plurality of features of the first product; and   making at least one recommendation to the purchaser, the at least one recommendation being selected from a group consisting of: a second product sharing at least one feature of the first product and a social network connection determined to have purchased another product sharing at least one feature of the first product.   
     
     
         12 . The program product of  claim 11 , wherein prioritizing the plurality of features includes:
 calculating at least one correlation value between at least one of the plurality of features and at least one feature of at least one additional product selected by another member of the social network;   ranking the calculated correlation values, wherein a relatively low correlation value is deemed indicative of an anomalous feature having significance and is given a higher priority than a relatively high correlation value; and   constructing a dendrogram including the at least one of the plurality of features and the at least one feature of the at least one product selected by another member of the social network, wherein constructing the dendrogram includes:
 including in a first level of the dendrogram at least one additional product that shares with the product selected by the member of the social network a feature having a highest ranked correlation value; and 
 including in a second level of the dendrogram any additional product included in the first level that shares with the product selected by the member of the social network a feature having a second-highest ranked correlation value. 
   
     
     
         13 . The program product of  claim 11 , wherein the method further comprises:
 determining whether the additional product was selected within a predetermined period; and   in the case that the additional product was selected within the predetermined period, including the additional product when prioritizing the plurality of features.   
     
     
         14 . A system comprising:
 at least one computing device adapted to make a recommendation to a purchaser by carrying out a method comprising:
 determining a plurality of features of a first product selected by a purchaser; 
 prioritizing the plurality of features of the first product; and 
 making at least one recommendation to the purchaser, the at least one recommendation being selected from a group consisting of: a second product sharing at least one feature of the first product and a social network connection determined to have purchased another product sharing at least one feature of the first product. 
   
     
     
         15 . A system comprising:
 at least one computing device adapted to make a recommendation to a member of a social network by carrying out a method comprising:
 determining a plurality of features of a product selected by a member of a social network; 
 prioritizing the plurality of features, including:
 calculating at least one correlation value between at least one of the plurality of features and at least one feature of at least one additional product selected by another member of the social network; and 
 ranking the calculated correlation values, wherein a relatively low correlation value is deemed indicative of an anomalous feature having significance and is given a higher priority than a relatively high correlation value; and 
 
 making at least one recommendation to the member of the social network, the at least one recommendation being selected from a group consisting of: a product sharing at least one feature of the product selected by the member of the social network and a member of the social network that has selected a product sharing at least one feature of the product selected by the member of the social network. 
   
     
     
         16 . The system of  claim 15 , wherein prioritizing the plurality of features includes constructing a dendrogram including the at least one of the plurality of features and the at least one feature of the at least one product selected by another member of the social network. 
     
     
         17 . The system of  claim 16 , wherein constructing the dendrogram includes:
 including in a first level of the dendrogram at least one additional product that shares with the product selected by the member of the social network a feature having a highest ranked correlation value; and   including in a second level of the dendrogram any additional product included in the first level that shares with the product selected by the member of the social network a feature having a second-highest ranked correlation value.   
     
     
         18 . The system of  claim 16 , wherein the method further comprises:
 determining whether the additional product was selected within a predetermined period; and   in the case that the additional product was selected within the predetermined period, including the additional product when prioritizing the plurality of features.   
     
     
         19 . The system of  claim 15 , wherein the method further comprises:
 broadcasting to the social network the plurality of features.