1. In a machine which recommends at least one item to a first user based, in part, on similarity factors between the first user and at least a second user, a method for operating the machine to determine a similarity factor between the first user and the second user, the method comprising the steps of: (a) retrieving from memory, using the machine an item profile of each of a plurality of items rated by the first user so as to define a plurality of retrieved item profiles, each of said retrieved item profiles includes a plurality of first values, each of the plurality of first values representing a rating given to an associated one of the items by each of a plurality of users; (b) determining from the retrieved item profiles, using the machine, whether the second user has previously rated any of the items; (c) retrieving from memory, using the machine, a corresponding user profile of the first user so as to define a retrieved first user profile; (d) retrieving, from memory, a corresponding user profile of the second user provided the second user has previously rated any of the items, so as to define a retrieved second user profile, wherein each of the retrieved first and second user profiles includes a plurality of second values, each of the plurality of second values representing a rating given to one of a plurality of items by a corresponding one of the users; and (e) calculating, using the machine, a similarity factor between the first user and the second user responsive to the retrieved profiles of the first and second users, wherein the first and second user profiles are distinct from the plurality of item profiles.
2. The method of claim 1 wherein the step of calculating a similarity factor includes sub-steps of: subtracting, using the machine, for each item rated by both users, the rating given to the item by the second user from the rating given to the item by the first user; squaring, using the machine, each rating difference; and dividing, using the machine, the sum of the squared differences by the number of items rated by both users.