1. A computer-implemented method of predicting an active user's rating for an item in a recommendation system implemented on computing equipment, wherein the active user and a plurality of non-active users are members of a social network implemented on computing equipment, comprising: providing a similarity measure, w″ a,u for each pair of users in said recommendation system; determining ratings for items rated by non-active users in said recommendation system; calculating, with the similarity measure w″ a,u , by the processor, a weighted average of ratings for said item to produce the predicted active user's rating; wherein the similarity measure, w″ a,u comprises a relationship distance in said social network between pairs of users, wherein an initial version of the similarity measure w a,u for active user a and non-active user u in the system comprises the relationship w a , u = ∑ i ⁢ ⁢ ( r a , i - r _ a ) ⁢ ( r u , i - r _ u ) ∑ i ⁢ ⁢ ( r a , i - r _ a ) 2 ⁢ ∑ i ⁢ ⁢ ( r u , i - r _ u ) 2 where r u,i is the rating of non-active user u who has rated item i, r a,i is the rating of active user, a, who has rated item i, wherein calculating a second version of the similarity measure, w′ a,u for active user a and neighbor user u in the system comprises enhancing the initial version correlation coefficient w″ a,u in accordance with the relationship: w′ a,u =w′ a,u , if there are more than N co-rated items w a , u ′ = w a , u ⁢  I a ⋂ I u  N , otherwise where I u is the set of items that neighbor user u has rated, I a is the set of items that active user a has rated, wherein the similarity measure w″ a,u is enhanced from the second version w′ a,u of the similarity measure in accordance with the relationship 
 w″ a,u η d a,u +(1−η d a,u )w′ a,u where η is a tunable parameter and d a,u is a measure of relationship distance between active user a and non-active user u in the social network.
1. A computer-implemented method of predicting an active user's rating for an item in a recommendation system implemented on computing equipment, wherein the active user and a plurality of non-active users are members of a social network implemented on computing equipment, comprising: providing a similarity measure, w″ a,u for each pair of users in said recommendation system; determining ratings for items rated by non-active users in said recommendation system; calculating, with the similarity measure w″ a,u , by the processor, a weighted average of ratings for said item to produce the predicted active user's rating; wherein the similarity measure, w″ a,u comprises a relationship distance in said social network between pairs of users, wherein an initial version of the similarity measure w a,u for active user a and non-active user u in the system comprises the relationship w a , u = ∑ i ⁢ ⁢ ( r a , i - r _ a ) ⁢ ( r u , i - r _ u ) ∑ i ⁢ ⁢ ( r a , i - r _ a ) 2 ⁢ ∑ i ⁢ ⁢ ( r u , i - r _ u ) 2 where r u,i is the rating of non-active user u who has rated item i, r a,i is the rating of active user, a, who has rated item i, wherein calculating a second version of the similarity measure, w′ a,u for active user a and neighbor user u in the system comprises enhancing the initial version correlation coefficient w″ a,u in accordance with the relationship: w′ a,u =w′ a,u , if there are more than N co-rated items w a , u ′ = w a , u ⁢  I a ⋂ I u  N , otherwise where I u is the set of items that neighbor user u has rated, I a is the set of items that active user a has rated, wherein the similarity measure w″ a,u is enhanced from the second version w′ a,u of the similarity measure in accordance with the relationship 
 w″ a,u η d a,u +(1−η d a,u )w′ a,u where η is a tunable parameter and d a,u is a measure of relationship distance between active user a and non-active user u in the social network. 2. The computer-implemented method of claim 1 , wherein the social network implemented on computing equipment is a network graph of relationships between users, wherein the social network is constructed out of social relationships, wherein degrees of separation between users within the social network is a primary metric of distance between users of the network, wherein a neighbor user is a non-active user that is close in distance to the active user, and wherein the similarity measure w″ a,u is calculated using only those non-active users that are neighbor users.