Abstract: An active control of advertising is enhanced through the use of solicited information to gain knowledge about a desired population of users. In some examples, this knowledge about users provides a self-identification of target users, and this self-identification is used to select further users with similar characteristics, who would also be likely to self-identify themselves as target users.

Abstract: A method for selection of items for one or more users includes performing each of a set of steps to determine the items. Performing the steps includes introducing a controlled variation into a result of each of multiple of the steps. The items for the one or more users are then selected according to the results of the steps.

Abstract: A method for determining product recommendations to be presented to users includes forming, by a formula generation module, a plurality of different recommendation formulas, including, for each recommendation formula, assigning a weight to at least some of a plurality of recommendation characteristics, wherein each recommendation characteristic is representative of at least one of a characteristic of a product, a characteristic of a method for presenting the product recommendations to the users, and a characteristic of a user.

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: Systems and methods for recommending items to users include maintaining a database of user-related information including user profiles, each including at least a history of user activities associated with a first entity that has a relationship with an inventory of recommendable items; obtaining information about an identity of a user interacting with a second entity different from the first entity and different from the service provider; associating the identity of the user interacting with the second entity with a corresponding user profile; selecting a first set of items from the inventory for presentation to the user based at least on an analysis of a history of user activities associated with the first entity; and forming a specification of the selected first set items for presentation to the user during the user's interaction with the second entity.

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.