Abstract: A method for execution by a computing device of a recommendation system includes receiving, from a user computing device associated with a user, a selection of one or more images from one or more selectable image groups to produce a subset of images, where each image of the subset of images has an individual image vector. The method further includes combining the individual image vector of each image of the subset of images to produce a preliminary input vector. The method further includes correlating the preliminary input vector with a first set of items of one or more databases of items based on the preliminary input vector and a corresponding item vector for each of the first set of items. The method further includes generating a representation of the first set of items for displaying on a display of the user computing device.

Abstract: A method for execution by a computing device of a recommendation system includes receiving, from a user computing device of the recommendation system, a selection of one or more images from one or more selectable image groups, where the user computing device is associated with a user profile. The method continues by generating, by the computing device, a personal preference profile for the user profile based upon the selection of the one or more images. The method continues by determining, by the computing device, to correlate one or more databases of items based on the personal preference profile for the user profile. The method continues by generating, by the computing device, a representation of one or more correlated items of the correlated one or more database of items for displaying on a display of the user computing device.

Abstract: A method for execution by a computing device of a recommendation system includes receiving, from a user computing device of the recommendation system, a selection of one or more images from one or more selectable image groups, where the user computing device is associated with a user profile. The method continues by generating, by the computing device, a personal preference profile for the user profile based upon the selection of the one or more images. The method continues by determining, by the computing device, to correlate one or more databases of items based on the personal preference profile for the user profile. The method continues by generating, by the computing device, a representation of one or more correlated items of the correlated one or more database of items for displaying on a display of the user computing device.

Abstract: A user-centric computer-implemented method and system for managing inputs and creating, modifying, importing, and sharing one or more user lifestyle preference profiles as input into a relevance assessment engine to determine a user's product or service preferences comprises a personalized recommendation software for assessing and aggregating lifestyle items via style dimension mapping of one or more computer-implemented algorithms to process image-associated metadata to create unique vector inputs, individualized lifestyle preference indices, and lifestyle preference outputs, associated with a plurality of correlated products, services and experiences. The recommendation system then directly matches each user to discrete relevant items without the need for management of historical data from other users.

Abstract: A browser-based personalized recommendation system and method for lifestyle items comprising style dimension mapping, inputs, outputs, and a computer-algorithm is described. The system gathers information about a user's unique lifestyle preferences, which is then correlated to clothing and lifestyle items. The recommendation system uniquely and directly matches users to discrete items without the need for historic user data.

Abstract: A user-centric computer-implemented method and system for managing inputs and creating, modifying, importing, and sharing one or more user lifestyle preference profiles as input into a relevance assessment engine to determine a user's product or service preferences comprises a personalized recommendation software for assessing and aggregating lifestyle items via style dimension mapping of one or more computer-implemented algorithms to process image-associated metadata to create unique vector inputs, individualized lifestyle preference indices, and lifestyle preference outputs, associated with a plurality of correlated products, services and experiences. The recommendation system then directly matches each user to discrete relevant items without the need for management of historical data from other users.