1. A computer-implemented method of determining a similarity of a set of comparison products to a focal product in a computer system having a processor, display device, and a graphical user interface, the method comprising: obtaining, by the processor, a user selection of a focal product, the focal product having a plurality of attributes; obtaining, by the processor, a set of attribute weightings and weighting the plurality of attributes; obtaining, by the processor, a set of comparison products via a wide-vector analysis, wherein each of the comparison products have a plurality of attributes in common with the focal product; filling, by the processor, data holes for comparison products missing attributes present in the focal product, wherein the data holes are filled with values that represents the attribute averages for other comparison products having the missing attributes, the other comparison products are in a product family associated with the comparison product missing the attributes; determining, by the processor, a similarity score for each of the comparison products according to the plurality of attributes in common with the focal product and the attribute weightings, each similarity score representing the distance of similarity of the comparison product to the focal product, wherein the distance of similarity of the comparison product to the focal product is determined by: comparing each quantitative attribute (y) of the comparison product to each quantitative attribute (x) of the focal product to calculate a total weighted distance between the focal product and the comparison product, wherein the total weighted distance for the comparison product represents a total quantitative score (d xy ) for the comparison product, d xy = ∑ i = 1 n ⁢ ⁢ ( w i ⁡ ( x i - y i ) ) 2 , where n is the number of quantitative attributes and w is the weight, comparing each categorical attribute of the comparison product to each categorical attribute of the focal product to identify matches between the focal product and the comparison product, wherein the weight of each matching categorical attribute is included to calculate a total categorical score for the comparison products, by evaluating sum j i m = { if ⁢ ⁢ x j = y j , ⁢ then ⁢ ⁢ sum j = 1 ; else sum j = 0 } where m is the number of categorical attributes, and determining a total score (sim xy ) based upon said total categorical and quantitative scores for each of the one or more comparison products sim xy = sim c ⁡ ( xy ) · ( ∑ i = 1 n ⁢ ⁢ w ci ) + sim q ⁡ ( xy ) · ( ∑ j = 1 m ⁢ ⁢ w qi ) ∑ i = 1 n ⁢ ⁢ w ci + ( ∑ j = 1 m ⁢ ⁢ w qi ) ⁢ ⁢ where ⁢ ⁢ sim cxy = ∑ j m ⁢ ⁢ w j ⁢ sum j ∑ j m ⁢ ⁢ w j ⁢ ⁢ and ⁢ ⁢ sim qxy = 1 - d xy max ⁢  d  , max ⁢  d  is evaluated using boundary values for each quantitative attribute and said weighting of said plurality of attributes further comprises user-weightings that specify the importance of the attribute to the user and editorial-weightings that are system defaults; and outputting, by the processor, the set of comparison products with the corresponding determined similarity scores for display on the display device, wherein the set of output comparison products are ordered according to the similarity scores.
1. A computer-implemented method of determining a similarity of a set of comparison products to a focal product in a computer system having a processor, display device, and a graphical user interface, the method comprising: obtaining, by the processor, a user selection of a focal product, the focal product having a plurality of attributes; obtaining, by the processor, a set of attribute weightings and weighting the plurality of attributes; obtaining, by the processor, a set of comparison products via a wide-vector analysis, wherein each of the comparison products have a plurality of attributes in common with the focal product; filling, by the processor, data holes for comparison products missing attributes present in the focal product, wherein the data holes are filled with values that represents the attribute averages for other comparison products having the missing attributes, the other comparison products are in a product family associated with the comparison product missing the attributes; determining, by the processor, a similarity score for each of the comparison products according to the plurality of attributes in common with the focal product and the attribute weightings, each similarity score representing the distance of similarity of the comparison product to the focal product, wherein the distance of similarity of the comparison product to the focal product is determined by: comparing each quantitative attribute (y) of the comparison product to each quantitative attribute (x) of the focal product to calculate a total weighted distance between the focal product and the comparison product, wherein the total weighted distance for the comparison product represents a total quantitative score (d xy ) for the comparison product, d xy = ∑ i = 1 n ⁢ ⁢ ( w i ⁡ ( x i - y i ) ) 2 , where n is the number of quantitative attributes and w is the weight, comparing each categorical attribute of the comparison product to each categorical attribute of the focal product to identify matches between the focal product and the comparison product, wherein the weight of each matching categorical attribute is included to calculate a total categorical score for the comparison products, by evaluating sum j i m = { if ⁢ ⁢ x j = y j , ⁢ then ⁢ ⁢ sum j = 1 ; else sum j = 0 } where m is the number of categorical attributes, and determining a total score (sim xy ) based upon said total categorical and quantitative scores for each of the one or more comparison products sim xy = sim c ⁡ ( xy ) · ( ∑ i = 1 n ⁢ ⁢ w ci ) + sim q ⁡ ( xy ) · ( ∑ j = 1 m ⁢ ⁢ w qi ) ∑ i = 1 n ⁢ ⁢ w ci + ( ∑ j = 1 m ⁢ ⁢ w qi ) ⁢ ⁢ where ⁢ ⁢ sim cxy = ∑ j m ⁢ ⁢ w j ⁢ sum j ∑ j m ⁢ ⁢ w j ⁢ ⁢ and ⁢ ⁢ sim qxy = 1 - d xy max ⁢  d  , max ⁢  d  is evaluated using boundary values for each quantitative attribute and said weighting of said plurality of attributes further comprises user-weightings that specify the importance of the attribute to the user and editorial-weightings that are system defaults; and outputting, by the processor, the set of comparison products with the corresponding determined similarity scores for display on the display device, wherein the set of output comparison products are ordered according to the similarity scores. 2. The method of claim 1 , wherein said one or more attributes comprise a selection of at least one of the following: one or more discrete in nature attributes; one or more continuous in nature attributes; one or more categorical in nature attributes; one or more binary in nature attributes; one or more quantitative in nature attributes; one or more aggregate in nature attributes; and combinations thereof.