|ChoiceStream, Inc. Patent applications|
|Patent application number||Title||Published|
|20130325624||ACTIVE AUDIENCE CONTROL - 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.||12-05-2013|
|20130031104||RECOMMENDATION JITTER - 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.||01-31-2013|
|20120143718||OPTIMIZATION OF A WEB-BASED RECOMMENDATION SYSTEM - 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. The method further includes iteratively performing the steps of: for each of the plurality of recommendation formulas, selecting, by a product recommendation module, at least one product for presentation to the users on the basis of the corresponding recommendation formula; sending, by a communications module, instructions to a server to present the selected product to the users; receiving, by a data evaluation module, data representative of user responses to each of the products presented to the users; evaluating, by the data evaluation module, the received data; and selecting, using the data evaluation module, a subset of the recommendation formulas included in the plurality of recommendation formulas on the basis of the evaluation of the collected data.||06-07-2012|
|20110066497||PERSONALIZED ADVERTISING AND RECOMMENDATION - 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.||03-17-2011|
|20090210246||STATISTICAL PERSONALIZED RECOMMENDATION SYSTEM - 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. 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.||08-20-2009|
Patent applications by ChoiceStream, Inc.