System and method for computer-based marketing

A system for predicting, for a user selected from a group of users, the reactions of the selected user to items sampled by one or more users in the group but not sampled by the selected user, comprising:

• a module for defining, for each item sampled by the selected user, a rating representing the reaction of the selected user to that item;
• a module for defining a set of raters from the group of users, each rater in the set having a rating for one or more items sampled by the selected user, wherein at least one rater is an objective archetype having hypothetical user ratings for one or more items sampled by the selected user;
• a module for successively pairing the selected user with each rater to determine a difference in ratings for items sampled by both members of each successive pair;
• a module for designating at least one of the raters as a mentor and assigning a similarity function to the mentor based on the difference in ratings between that mentor and the selected user;
• and
• a module for predicting the reaction of the selected user to the items not yet sampled by the selected user from a prediction function based on the similarity function, the at least one mentor's rating of the items, and a previously determined prediction of the selected user's reaction to the items.

A method of automatically predicting, for a user selected from a group of users, the reactions of the selected user to items sampled by one or more users in the group but not sampled by the selected user, the reaction predictions being based on other items previously sampled by the selected user, comprising:

• defining, for each item sampled by the selected user, a rating representing the reaction of the selected user to that item;
• defining a set of raters including ones of the group of users, each rater in the set having a rating for one or more items sampled by the selected user, wherein at least one rater is an objective archetype having hypothetical user ratings for one or more items sampled by the selected user;
• successively pairing the selected user with the raters to determine a difference in ratings for items sampled by both members of each successive pair;
• designating at least one of the raters as a mentor and assigning a similarity function based on the difference in ratings between that mentor and the selected user;
• and
• predicting the reaction of the user to the items not sampled by the selected user from a prediction function based on the similarity function, the mentor's rating of the items, and a previously determined prediction of the user's reaction to the items.