Abstract: A method for recommending items includes forming one or more choice sets of recommendable items. At least one choice set is a personalized choice set of recommendable items formed for a first user based in part on data associated with that user. Recommendable items can be divided into classification groups. Forming a choice set of recommendable items can be based on a user's classification group preferences. The method also includes forming a recommendation set of recommendable items from the choice sets of recommendable items. Forming the recommendation set is based in part on additional data associated with the user. The additional data is not used in forming the personalized choice set.

Abstract: A method for recommending items in a domain to users, either individually or in groups, makes user of users' 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.