a method for recommending items in a domain to users , either individually or in groups , makes user of users &# 39 ; characteristics , their carefully elicited preferences , and a history of their ratings of the items are maintained in a database . users are assigned to cohorts that are constructed such that significant between - cohort differences emerge in the distribution of preferences . cohort - specific parameters and their precisions are computed using the database , which enable calculation of a risk - adjusted rating for any of the items by a typical non - specific user belonging to the cohort . personalized modifications of the cohort parameters for individual users are computed using the individual - specific history of ratings and stated preferences . these personalized parameters enable calculation of a individual - specific risk - adjusted rating of any of the items relevant to the user . the method is also applicable to recommending items suitable to groups of joint users such a group of friends or a family . a related method can be used to discover users who share similar preferences . similar users to a given user are identified based on the closeness of the statistically computed personal - preference parameters .