1. A method for recommending other users who have similar preference with a specific user comprising the steps of: (a) acquiring information on objects selected, respectively, by a specific user and random other users, wherein the random other users include 1-st to r-th other users while the objects include 1-st to s-th objects and a specific object is selected among the 1-st to s-th objects by the specific user; (b) acquiring information on topics which include 1-st to n-th topics by dividing the objects into n groups by referring to characteristic(s) of each object; (c) determining to which topic the specific user and the random other users respectively correspond; (d) evaluating a degree of user similarity by each random other user, the degree of user similarity being relevant to a value equivalent to sum of probabilities of the specific object appearing in the state of a random topic while a k-th other user and the specific user have relationships with the random topic among the 1-st to n-th topics as a probability of the k-th other user and the specific user, at the same time, having selected the specific object, by referring to correspondence relationships between user information which includes information on the specific user and the random other users and topic information and those between the topic information and object information, wherein k is one or more and r or less; and (e) recommending one or more specific other users among the random other users who are ranked in top levels in terms of the degree of user similarity as appropriate users who have similar preference with the specific user, wherein, at the step (d), the degree of user similarity is evaluated by multiplying the value by a probability of the specific object appearing independently.