Patent Publication Number: US-2022215455-A1

Title: Computer-Implemented Method for Recommendation System Input Management

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     The present U.S. Utility Patent Application claims priority pursuant to 35 U.S.C. § 120 as a continuation of U.S. Utility application Ser. No. 16/529,411, entitled “Computer-Implemented Method for Recommendation System Input Management”, filed Aug. 1, 2019, which is a continuation of U.S. Utility application Ser. No. 14/433,624, entitled “Computer-Implemented Method for Recommendation System Input Management”, filed Oct. 7, 2013, issued as U.S. Pat. No. 10,373,230 on Aug. 6, 2019, which is a U.S. National Stage Application submitted pursuant to 35 U.S.C. § 371 of Patent Cooperation Treaty Application No. PCT/ IB13/03030, entitled “Computer-Implemented Method and System for Recommendation System Input Management,” filed Oct. 7, 2013, which claims priority pursuant to 35 U.S.C. § 119(e) to U.S. Provisional Application No. 61/710,564, entitled “Computer-Implemented Method and System for Recommendation System Input Management”, filed Oct. 5, 2012, expired, and is related to U.S. Utility application Ser. No. 13/646,190, entitled “Method And System For Personalized Recommendation Of Lifestyle Items”, filed Oct. 5, 2012, issued on Nov. 10, 2015 as U.S. Pat. No. 9,183,510, all of which are hereby incorporated herein by reference in their entirety and made part of the present U.S. Utility Patent Application for all purposes. 
    
    
     STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT 
     Not Applicable. 
     THE NAMES OF THE PARTIES TO A JOINT RESEARCH AGREEMENT 
     Not Applicable. 
     INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC 
     Not Applicable. 
     FIELD 
     The present invention relates to input management systems. More particularly, the present invention relates to input management systems for recommendation systems. 
     BACKGROUND 
     A plethora of varied recommendation systems exist on the Internet. Recommendation systems typically apply knowledge discovery techniques to the problem of making product recommendations during a customer interaction. These systems have found great utility in E-commerce, but the current systems are challenged by the exponential growth in the number of customers, and products available to those customers. 
     Current recommender systems are challenged by their inherent approach to gathering and managing input to generate recommendations. First, they rely on historical user data to develop an initial knowledge base for the recommendation engine. This reliance causes a “cold start” problem, wherein a recommender system is generally inoperable and unreliable until a certain critical mass of user data input has been accumulated by the system. Second, when dealing with sparse input data, these systems are less able to provide relevant recommendations to customers. For example, input data is considered sparse when product lines or brands are emerging rather than mature. 
     Collaborative filtering is a type of recommender system technology that works by matching input of a customer&#39;s preferences to the aggregate inputted or observed preferences of other customers. Collaborative filtering performance degrades as the number of customers or products increases. A recommendation system capable of quickly producing relevant recommendations without relying on management of inputs associated with preference matching would be desirable. It is further desirable to provide such as system capable of handling very large scale application would likewise be desirable. 
     Recommendation systems have generally evolved in the extremely interactive environment of the World Wide Web, the system of interlinked hypertext documents accessed via the Internet. These systems apply data analysis techniques to help customers find which products they would like to purchase at E-Commerce sites. For instance, a recommender system of AMAZON.COM (www.amazon.com) suggests additional books for purchase by a customer based on books the customer have already purchased from AMAZON, or, based on books a customer has told AMAZON they like. Another recommender system on CDNOW (http://www.cdnow.com) helps customers choose CDs to purchase as gifts, based on other CDs the recipient has liked in the past. 
     In general, many recommender systems are an application of a particular type of Knowledge Discovery in Databases (KDD) (Fayyad et al. 1996) technique. KDD systems use subtle data analysis techniques to achieve two primary unsubtle goals. First, these systems attempt to save money by discovering the potential for efficiencies. Second, these systems attempt to generate more revenue by discovering ways to sell more products to customers. For instance, companies use KDD to discover which products sell well at which times of year, so they can manage their retail store inventory more efficiently, potentially saving millions of dollars a year (Brachman et al. 1996). Other companies use KDD to discover which customers will be most interested in a special offer, reducing the costs of direct mail or outbound telephone campaigns by hundreds of thousands of dollars a year (Bhattacharyya 1998, Ling et al. 1998). Companies use KDD to discover a new sales model, and then, apply that model to a new sales application. Businesses use KDD to increase sales of existing products by matching customers to the products they will be most likely to purchase. 
     KDD-based recommender systems are limited in their ability to perform interactively due to their necessary reliance on association of historical data input. For example, while a customer is at specific web site, typically an e-commerce site, the recommender system must learn from the customer&#39;s behavior, develop a model of that behavior, and apply that model to recommend products to the customer. The recommendations are based upon the management of historical data input gleaned from other users. 
     Both collaborative and content-based filtering recommendation systems require management of a base input user profile, driven by textual input by the user, or, selection of various options. This initial input is also known as seed data. The user profile is used to predict relevant items for each user. Initial user inputs can be refined through subsequent user feedback including ranking or rating items, user purchase behavior, and user social network activity. The recommendation system then compares all the collected data and calculates a list of relevant items for the user. 
     Additionally, current recommender systems typically require iterative interaction by a user, supplemented by historical information concerning the behavior of other users. For instance, in collaborative filtering approaches, like EBAY or AMAZON, a user&#39;s past or historical behavior is analyzed for similarities to the behavior of other users. These types of systems are not flexible and do not allow users to actively participate in the development of their personal preference profile. Users cannot remove actions from their history nor can they create an entirely new profile based on desired actions. A user cannot hypothetically add purchases or browsing history to his account that did not actually occur. Additionally, these systems gradually account for activity over some period of time; inputs cannot be changed instantaneously to adjust the personal preference profile. 
     In content-based filtering approaches, like PANDORA and NETFLIX, the systems require the user to rate items to provide initial seed data. Inherently, these systems do not yield consistent results when their databases have only a few values, creating an inability to derive the most relevant searchable key attributes. Furthermore, where a recommender system requires a minimum amount of seed data to initiate, a user must spend more time at the outset to implement the system to provide relevant results for the user. In addition, content-based filtering approaches are limited to one-to-one comparisons of content types. PANDORA, for example, can only recommend music; similarly, NETFLIX can only recommend films. 
     It would be preferable to allow each user to understand how the user&#39;s inputs are managed to create relevant recommendations. Current recommendation systems do not lend themselves to user transparency in dealing with input management. A user generally does not understand how a particular recommender system manages the user&#39;s inputs to generate subsequent product recommendations. Accordingly, a user would not have a sense as to how to influence those recommendations. Input management for current recommender systems does not leverage visual cues or visual elements to assist a user in developing an understanding of how recommendations are developed by the system. 
     Consequently, in light of the aforementioned limitations, a need exists for methods and systems to manage input for recommendation systems, using visual cues and elements, wherein user input can be changed instantly and resulting recommendations are likewise changed instantly. In addition, a need exists for methods and systems having transparency in operation so a user can play an active role in determining whether the resultant recommendations are consistent with the user&#39;s own perception of his or her personal preferences. 
     SUMMARY 
     In view of the foregoing described needs, an aspect of the inventive subject matter is directed to a computer-implemented method and system to manage and support instantaneous and transparent modification of inputs to a recommender system by a user wherein historical data is not a prerequisite to generation of relevant recommendations for items. A further aspect of the inventive subject matter comprises an input management system for recommendation systems that satisfies the above needs for a straightforward, less data-intensive approach for matching each user with relevant items of interest without the need for an initial usage baseline, or specific rating of items of interest prior to provision of relevant results. 
     The method and system of the inventive subject matter herein, also referred to herein as the “StyleSeek®” method and system, uniquely and directly maps individual users to discrete items without the need for historic user data. The method and system comprises a software module programmed for operation on a computer to support interaction with a user via a plurality of user interfaces. The user interface may be deployed on any one of a web browser across the Internet, a smart phone display, a kiosk in a retail environment, a touchscreen, a holographic display, a gesture recognition interface and other such perceptual user interfaces which may evolve in the future. This flexibility in deployment device and location supports use in both static and mobile environments to suit the requirements of each user and the specific deployment scenarios. The StyleSeek software module provides a means for selecting one or more images or other visual cues or elements to determine a user&#39;s preferences. The resulting image selections serve as the initial input to one or more computer-implemented algorithms that transform metadata and attributes associated with the image selections into the user&#39;s unique personal preference profile. The personal preference profile is then used by the system software to evaluate, correlate and display relevant recommendations for the user. Hence, the method and system provide input management via visual elements. Although input to the user interface can occur via keyboard and mouse, the method and system is uniquely suited to touch and other such perceptual input and display modalities, including voice and gesture recognition, since the invention emphasizes the use of visual elements, images and graphic representations rather than textual or numeric input. 
     In keeping with the use of visual elements to create input, an aspect of the inventive subject matter facilitates the use of cognitive and emotional responses from users via the user&#39;s visual perception and cognitive interpretation of multiple images to allow digital transformation of the user&#39;s cognitive and emotional responses to create the user&#39;s personal preference profile, hereinafter, also referred to as the user&#39;s “StyleDNA®.” Of course, depending on the selected underpinning for the recommendation system, the user&#39;s personal preference profile could be referred to as the user&#39;s “SocialDNA,” “HealthDNA,” “PoliticalDNA,” “EmotionalDNA,” “SexualDNA,” “CulinaryDNA,” or other such foundational underpinnings. The method and system subtly accesses the user&#39;s visceral response to one or more images to generate a collection of images that is subsequently digitally transformed to aptly represent the user&#39;s tastes and preferences. The method and system is designed to elicit a visceral response, generated in a fluid manner, where the user is able to provide input merely by selecting various images through the display used for interaction with the system. 
     The inventive subject matter comprises a computer-implemented method and system for creating, managing, modifying, importing, and sharing inputs to a user personal preference profile, which may then be uses as one or more inputs to a relevance assessment engine to determine a user&#39;s personal product, service and lifestyle preferences. In one embodiment targeted toward fashion and lifestyle, the system comprises software for assessing and aggregating lifestyle items via style dimension mapping via one or more computer-implemented algorithms to process metadata associated with one or more selected images. The method and system is easily expanded to support recommendations for personal preferences for other subject matter areas beyond lifestyle preferences. For example, images may be related to a user&#39;s religious, political, social, emotional, athletic, sexual, culinary, experiential and other such personal underpinnings or personal attributes that cause each user to have a unique personal preference profile. In other embodiments, images, and therefore resulting recommendations, may be tied to one or a combination of any of the subject matter areas described above. For simplicity in describing the inventive subject matter, the method and system are described in relation to an embodiment wherein “lifestyle” preferences are assessed and recommendations are based upon those lifestyle preferences. 
     Image metadata need not be disclosed to a user since the system uses preexisting metadata associated with each user-selected image to create individualized vector inputs to create an individualized user lifestyle preference index (i.e. the user&#39;s StyleDNA®) and subsequent individualized lifestyle preference output parameters. The generated output parameters are then associated or mapped to a plurality of correlated products, services, lifestyles and experiences for presentation to the user. The system interactively gathers and manages input information about a user&#39;s unique preferences driven by the user&#39;s image selections. The method and system directly matches each user to discrete items without access to historic data. 
     The method and system flexibly allows users to continuously interact with the system throughout its breadth to manage inputs to the system. Input management leverages various activities by a user, including (1) the user&#39;s instantaneous selection of images relevant to the user&#39;s tastes and preferences, (2) creation of one or more separate and distinct user personal preference profiles, StyleDNA, based on user selections, (3) on-demand modification of existing StyleDNA through the selection of a single image, (4) instantaneous import of preferences from another user&#39;s personal preference profile (StyleDNA) to create a new or additional StyleDNA for the user, and (5) sharing of the user&#39;s StyleDNA and associated personal preferences with other users. The computer-implemented methods and system of the inventive subject matter likewise provides a process to use the aforementioned inputs to immediately generate an updated personal preference profile, which is then used to subsequently generate output to identify relevant items of interest to the user. 
     In an embodiment of the method and system of the inventive subject matter, multiple images are presented to a user for consideration. Each image serves as an opportunity for visual visceral perception by a user, wherein the image is correlated with associated metadata indicative of a basis for selection of the image by a user. Each image presented to a user for potential selection is pre-associated with structured metadata, which is then applied and processed using various algorithms incorporated within the system software to transform the selected images into the user&#39;s StyleDNA. Historical data collection is not a requirement of the method and system. Images used within the method and system have no subject constraints. Images can feature any type of content, including but not limited to, various products, brands, logos, trademarks, landscapes, and even specific people. 
     Although each image is associated with specific metadata, the use of images to develop a user&#39;s StyleDNA provides a more vigorous approach and a greater level of definition for the spectrum of a user&#39;s preferences since the user has an innate and instinctive response to each image, in other words, a visceral and subliminal response. Hence, the method and system allows a user to select one or more images based upon the user&#39;s conscious and subliminal preference. The selected images then drive the creation of the user&#39;s individualized StyleDNA through a unique combination of inputs from the user. 
     Visual perception, and the subliminal aspects of such visual perception, is an element of the subject matter of the invention. Hermann von Helmholtz is often credited with the first study of visual perception in modern times. Helmholtz examined the human eye and concluded that it was, optically, rather poor. The poor-quality information gathered via the eye seemed to Helmholtz to make vision impossible. He therefore concluded that vision could only be the result of some form of unconscious inferences: a matter of making assumptions and conclusions from incomplete data, based on previous experiences. Hence, the method and system according to various embodiments of the invention uniquely leverages each individual&#39;s user-centric experience to refine choices that drive preference definitions. The consideration of an image allows a user to make selections based on unconscious inferences that rely on assumptions and conclusions from incomplete data based on a user&#39;s previous experiences. Although every image presented to a user of the system may be interpreted as being visually incomplete, the user&#39;s experiential assumptions and conclusions associated with each image create a more complex yet complete view of the user&#39;s particular personal preferences. This selection methodology avoids limits found where choices are made using direct selection of structured data attributes. For example, where a user is asked to describe a preference in textual form, or select a preference from a list of descriptors, the user will not necessarily be leveraging unconscious inferences associated with the sense of sight. Hence, where a user may select two images for similar reasons due to his unconscious preferences, the metadata with each image may be distinct, causing the development of a more accurate and reliable assessment of user preferences. 
     The input management features of the method and system allows users to modify their individual or multiple StyleDNA at any time, using any image within the system. An entire StyleDNA can be replaced with new images; no historical data is required. Determination of a user&#39;s StyleDNA is flexible; a StyleDNA can be driven by as little as one image, two or more, or, a plurality of images. In one aspect, the user&#39;s StyleDNA is driven by a group of images selected by a user. The number of images selected for the user&#39;s StyleDNA group can be adapted based on results of user interaction. 
     The method and system supports creation of additional StyleDNA by the user, which may be used interchangeably, for example, depending on the mood of the user. Additionally, a user may share their StyleDNA with another individual so that individual can shop for the user or select gifts more likely to satisfy the user&#39;s personal preferences. Further, the system allows each user to share his or her StyleDNA with other users, or, import StyleDNA shared by other users. In addition, the system supports the use of exemplary StyleDNA, which may be associated with persons of note, including celebrities, politicians, scientists and other such notable figures. Hence, the method and system provides each user with various alternatives to manage inputs to the recommender system. 
    
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS 
       For a better understanding of the inventive subject matter, reference is made to the detailed description contained herein and the accompanying drawings numbered below which are given by way of illustration only and are not intended to be limitative to any extent. Commonly used reference numbers identify the same or equivalent parts of the claimed invention throughout the several figures. These and other features, aspects and advantages of various embodiments of the inventive subject matter will become better understood with regard to the following description, appended claims, and accompanying drawings where: 
         FIG. 1  is a diagram of an overview of the method and system embodying principles of the invention. 
         FIG. 2  is a block diagram of the various vectors used in the method and system embodying principles of the invention. 
         FIG. 3  is a high-level flowchart for management of inputs to the computer-implemented method and system embodying principles of the invention. 
         FIG. 4  is a block diagram illustrating the top-level structure of the software interface embodying principles of the invention. 
         FIG. 5  is a block diagram illustrating the structure and components of the Manage StyleDNA module of the software embodying principles of the invention. 
         FIG. 6  is a block diagram illustrating the structure and components of the Global module of the software embodying principles of the invention. 
         FIG. 7  is a block diagram illustrating the structure and components of the user Account module of the software embodying principles of the invention. 
         FIG. 8  is an exemplary screen shot of the user interface associated with the “All Brands” selection from the Global module of  FIG. 6  embodying principles of the invention. 
         FIG. 9  is an exemplary screen shot of the user interface associated with the “Occasions” selection from the Global module of  FIG. 6  embodying principles of the invention. 
         FIG. 10  is an exemplary screen shot of the user interface associated with a selection of “Business casual” from the “Occasions” drop down list of  FIG. 9  embodying principles of the invention. 
         FIG. 11  is a flowchart illustrating the computer-implemented method of the StyleGame embodying principles of the invention. 
         FIG. 12A  is a first view of an exemplary screen of the user interface associated with the StyleGame of  FIG. 11  wherein a user has not made any image selections embodying principles of the invention. 
         FIG. 12B  is a subsequent view of an exemplary screen of the user interface associated with the StyleGame of  FIG. 11  wherein a user has made several image selections embodying principles of the invention. 
         FIG. 12C  is a final view of an exemplary screen of the user interface associated with the StyleGame of  FIG. 11  wherein a user has completed his selection of images embodying principles of the invention. 
         FIG. 13  is a functional block diagram of the structure and operation of the Manage StyleDNA module embodying principles of the invention. 
         FIG. 14  is an exemplary screen shot of a first user interface associated with the Manage StyleDNA module of  FIG. 13  embodying principles of the invention. 
         FIG. 15  is an exemplary screen shot of “Rename StyleDNA  40 ” associated with the Manage StyleDNA user interface of  FIG. 14  embodying principles of the invention. 
         FIG. 16  is an exemplary screen shot of “Create StyleDNA” associated with the Manage StyleDNA user interface of  FIG. 14  embodying principles of the invention. 
         FIG. 17  is an exemplary screen shot of another view of the Manage StyleDNA user interface embodying principles of the invention. 
         FIG. 18  is an exemplary screen shot of the Browse StyleDNA module user interface embodying principles of the invention. 
         FIG. 19  is an exemplary screen shot of the detail for another user&#39;s StyleDNA selected from the Browse StyleDNA module user interface embodying principles of the invention. 
         FIG. 20  is an exemplary screenshot of the Imported StyleDNA user interface embodying principles of the invention. 
         FIG. 21  is a functional block diagram of the Explore module embodying principles of the invention. 
         FIG. 22  is an exemplary screenshot of the Explore user interface embodying principles of the invention. 
         FIG. 23A  is an exemplary screenshot of the Explore user interface wherein a user has hovered a pointer over a particular item embodying principles of the invention. 
         FIG. 23B  is an exemplary screenshot of the Explore user interface wherein a user has selected the item hovered over in  FIG. 23A  and added the item to his MyDNA account embodying principles of the invention. 
         FIG. 24  is an exemplary screenshot of the detail page associated with the item added to the user&#39;s MyDNA account of  FIG. 23A  embodying principles of the invention. 
         FIG. 25  is an exemplary screenshot of the user interface dropdown for Manage StyleDNA to allow a user to change StyleDNA. 
         FIG. 26  is an exemplary screenshot of the new Explore user interface after the user has selected a new StyleDNA from the dropdown of  FIG. 25  embodying principles of the invention. 
         FIG. 27  is a functional block diagram of the Buy module embodying principles of the invention. 
         FIG. 28  is an exemplary screenshot of the Buy user interface embodying principles of the invention. 
         FIG. 29  is an exemplary screenshot of the detail page for an item selected from the Buy user interface of  FIG. 28  embodying principles of the invention. 
         FIG. 30  is a functional block diagram of the Brands module embodying principles of the invention. 
         FIG. 31  is an exemplary screenshot of the Brands user interface embodying principles of the invention. 
         FIG. 32  is an exemplary screenshot of the detail page for a particular brand selected from the Brands user interface of  FIG. 31  embodying principles of the invention. 
         FIG. 33  is an exemplary screenshot of the detail page for a particular representative brand embodying principles of the invention. 
         FIG. 34  is an illustration of a representative network used to implement the method and system embodying principles of the invention. 
         FIG. 35  is a block diagram for a representative computer system used to implement the method and system embodying principles of the invention. 
     
    
    
     ASPECTS OF THE INVENTIVE SUBJECT MATTER 
     One aspect of the inventive subject matter discloses a user-centric method and system for recommendation system input management wherein inputs to the recommender system need not rely on historical data from other users. 
     Another aspect of the inventive subject matter discloses a means by which a user can provide input to the recommendation system using an inferred or subconscious visceral response to selected images, thereby avoiding reliance on selection of specific narrative text by the user to indicate preferences. 
     Another aspect of the inventive subject matter discloses a user-centric method and system to transform images selected by the user into an individualized set of recommendations for lifestyle items incorporating intangible style elements, branding, and public perception to provide relevant and accurate results matched to the user&#39;s unique personal preferences. 
     Another aspect discloses a personalized recommendation method and system operable with limited data wherein historic user data is not required to generate accurate results to overcome the cold-start problem inherent to other recommender systems. The first user of the system will receive recommendations equal in quality to any subsequent user of the system. 
     Another aspect discloses a personalized recommendation method and system operable wherein data from the user is sparse. 
     Another aspect discloses a personalized recommendation method and system for managing inputs associated with user preferences and associated lifestyle items wherein the user is not required to rate, evaluate, or give scaled or objective preferences for any lifestyle item. 
     Another aspect discloses a method and system for managing inputs to a recommendation system wherein the user is directly matched to discrete items and products, including, but not limited to, clothing, lifestyle items, and brands, as opposed to being assigned to predetermined categories and then shown specific groups of products. 
     Another aspect discloses a method and system wherein a user may immediately change his or her inputs to the system by selecting new images for inclusion or replacement of other images in the selection matrix and then receive an immediate update of recommendations from the system based upon the changed inputs. 
     Another aspect discloses a method and system wherein both user inputs and outputs are delivered in a visual manner rather than through the input and output of text or other data, including selections from drop-down menus. 
     Another aspect discloses a method and system wherein a user may have one or more personal preference profiles and change between such profiles instantaneously, thereby receiving updated recommendations instantaneously. 
     Another aspect discloses a user input management method and system wherein the user may elect to use either an entire set of inputs or individual inputs from other users or templates within the user&#39;s own inputs. 
     DETAILED DESCRIPTION 
     The following description is merely exemplary in nature and is in no way intended to limit the invention, the inventive subject matter, its application, or its uses. Before the inventive subject matter is described in further detail, it is to be understood that the invention is not limited to the particular aspects described, as such may, of course, vary. It is also to be understood that the terminology used herein is for describing particular aspects only, and is not intended to be limiting, since the scope of the present invention will be limited only by the appended claims. In particular, the recommender method and system may be described in the context of “lifestyle” item recommendations, but the method and system is equally applicable to providing recommendations for items of any type, in any category, subject matter, domain or classification. 
     Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this inventive subject matter belongs. Although any methods and materials similar or equivalent to those described herein can also be used in the practice or testing of the inventive subject matter, a limited number of the exemplary methods and materials are described herein. 
     It must be noted that as used herein and in the appended claims, the singular forms “a”, “an”, and “the” include plural referents unless the context clearly dictates otherwise. 
     All publications mentioned herein are incorporated herein by reference to disclose and describe the methods and/or materials in connection with which the publications are cited. The publications discussed herein are provided solely for their disclosure prior to the filing date of the present application. Nothing herein is to be construed as an admission that the present invention is not entitled to antedate such publication by virtue of prior invention. Further, the dates of publication provided may be different from the actual publication dates, which may need to be independently confirmed. 
     Following is a description of computer-implemented method(s) and system(s) for managing inputs in a recommendation system. 
     First, referring to  FIG. 1 , features of the method(s) and system(s) are illustrated and disclosed. For simplicity and brevity, the method and system may hereinafter be likewise referred to as the system or the method. In one embodiment, initially, a new user accesses the computer-implemented method and system to initiate a “game” module, hereinafter referred to as the StyleGame® module  20 . The StyleGame module  20  causes one or more images  22 ,  24  to be displayed to the user for visual perception V and mental processing B. The user relies on his or her personal preferences to select one or more images  24  as representative of the user&#39;s personal preferences or tastes. The user leaves unselected those images  22  that do not match the user&#39;s personal preferences. The selected images  24  are then put through a transformation  30  according to the method and system using a computer and software programmed to operate on the computer to implement the method to generate a unique user preference profile  40  for the user, hereinafter the user&#39;s StyleDNA  40 . After the user&#39;s StyleDNA  40  has been generated, the StyleDNA  40  drives correlation  50  against an item database  60 . During correlation  50 , uncorrelated items  62  are ignored and correlated items  64  are selected. The selected correlated items  64  are then transformed for presentation  70  to the user. The presentation transformation  70  sends images  85  of the selected correlated items  64  to a display  80  presentation to the user for additional visual perception V and mental processing B. 
     Vector Structure - Now referring to  FIG. 2 , the vector structure  90  of the system in one embodiment is described in greater detail. First, the system creates user vector  92  based on earlier image selections. The preliminary input vector  92  drives the selection of item/brand/content matches from multiple databases wherein the matches are deemed to correlate with the user&#39;s StyleDNA  40 . The method and system is not limited in the number of databases  60  and in various aspects, the prescribed number of database matches can be limited to just one database  60  or any number of additional databases  60  with additional category specifications. Individual image vectors  91  driven by the selections by a user from each of the image matrices  1225  are combined by the system to create the individualized user vector  92 . The user vector  92  and corresponding product images  1225  comprise a user&#39;s StyleDNA  40 . 
     A variety of supplemental inputs can impact input image vectors  91  in providing matched results. These supplemental inputs may be combined to form separate vectors, or merely act as filters against database queries. For instance, account information provided by the user from other Internet sites such as FACEBOOK, LINKED-IN, or PINTEREST may be used within a system of algorithms to enhance the quality of recommendations. Data that may be used includes demographic information, FACEBOOK likes, keywords within comments, occupations, and followers/friends/connections. Additionally, users may select specific items within the StyleSeek database  60  to refine their recommendations. For instance, a user may provide a positive preference for blue dress shirts, and the system will provide a bias towards other similar items. Alternatively, a user may provide a negative preference for sunglasses, for example, and therefore never be shown those item types again, regardless of their match due to the input image vectors  91 . The rules for these supplemental systems may be turned on or off by the user to control the results. In other examples, a user may provide a positive preference for a particular piece of furniture, and the system will provide a bias toward other items having similar or complementary associations. For example, a user may select an image of a leather desk chair and the system will provide a bias toward items having similar metadata, such as button-down shirts for men or mahogany desks or brass writing utensils. In other words, the system facilitates selection of items of interest without requiring the user to view and rank similar items of interest. For example, the system does not require a user to assess, evaluate and rank different styles of shirts to determine that the user might be biased toward button down shirts. Likewise, a user may be shown an image of a vehicle, storefront, house or other item and the system will determine the user&#39;s preferences for food, sports teams, and other items and products. 
     Now, referring to  FIG. 3 , a structured flowchart of the method and system is disclosed. At input level  100 , the user provides key inputs  105  to the system software, which then performs various calculations. At output level  200 , the system generates various outputs  205  based on the inputs  105  in the form of images of various items and supports basic interaction by the user. At advanced user interactive level  300 , the system provides additional functionality to allow advanced user activity, which includes additional refinement of the system via additional user input. 
     Now, in greater detail, additional features and function at input level  100  are described. At Start step  110 , the user visits a web page associated with the system. At choice step  120 , the user is prompted to create an account or use the StyleGame  20 . If the user elects to use the StyleGame  20 , at selection step  130 , the user is prompted to select one or more items  1225  from a group  1220  of images of those items  1225 . At user vector creation step  140 , the system creates a user vector  92  based upon the item images  1225  selected and then, at confirmation step  150 , the system requests confirmation to confirm that the user is satisfied with his choices. If the user is not satisfied with the choices, the user can repeat the StyleGame  20  and make different choices. If satisfied, the method and system proceeds to output level  200 . 
     At output level  200 , the system generates various outputs  205  and supports basic user activity. At categorization step  210 , the system categorizes the user vector  92  and finds and displays related items to the user based on correlation with the user vector  92 . At interactive step  220 , the system software provides interactive functionality to allow the user to review, select and buy any of the items presented to the user. In addition, at filter step  230 , the user is able to filter displayed items by various criteria. 
     At level  300 , the system provides more advanced input management functionality to the user for system refinement. At feedback step  310 , the user provides feedback to the system on displayed items and browses a more extensive database  60  of items. At user profile input step  320 , the user inputs additional user profile information. At refinement step  330 , the system aggregates the additional user input to refine the user vector  92  according to the tastes and preferences of the user. At update step  340 , the system updates and displays items deemed preferable to the user. 
     Now, in greater detail, we describe the flowchart in  FIG. 3  depicting a multilevel overview of one aspect of the method and system of the inventive subject matter. The method and system manages correlation at level  100  between inputs  105  to the system and at level  200  to outputs  205  of the system. Only a de minimis initial input  105  from the user is required to initiate the system. At step  130 , a user chooses lifestyle items from an image matrix. Although identified herein as a matrix, other aspects support organization of the images in any geometric grouping. For example, the images might be displayed in circular groupings, hierarchical groupings or other similar geometric layouts. At step  140 , the system creates an N-Dimensional User Vector  92  from images selected by the user. In one aspect, testing by the inventors has shown that the efficiency and utility of the input management method and system appears to be maximized when the number of choices of lifestyle items available from the image matrix is equal to nine. Users appeared to be best served by this number; cognitively evaluating nine images provides a balance between useful inputs for the system while minimizing the level of effort required by a user, thus minimizing the number of users abandoning the input process due to length of completion time. 
     Importantly, a user does not rate or rank items displayed in an image matrix. The system does not require an initial baseline assessment of items by the user for initialization, thus overcoming the well-known “cold start” issue associated with existing recommender systems and methods. Instead, at step  210 , the method and system according to an embodiment of the invention uniquely and directly maps the user to each specific item, and not to categories, genres, or tags associated with the item. In one aspect, the method and system does not rely on inputs from historical user data or associated historical product acquisition data. A first user of the StyleSeek method and system will experience results equivalent in accuracy to all subsequent users. No machine learning is required. The item/brand/content vectors  93  do not change over time based upon input  105  to the system or user feedback; the item/brand/content vectors  93  are independent of user interactions. 
     At input level  100 , the method and system is configured to first receive initial user inputs  105  and perform various calculations based upon those inputs  105 . Next, at output level  200 , the method and system uses the results from input level  100  to generate output  205 , such as recommended items, which are then displayed to the user for other basic user activity. Finally, at interactive level  300 , the method and system provides additional functionality to support more advanced user activity and support system refinement. 
     At step  110 , Visit web page, a user enters the site. The user may enter the site by visiting the web page on their computer, or by using a mobile phone application to view the web page or a phone-specific version of the web page. Alternatively, the user may enter the site to interact with the system and receive recommendations by visiting an in-store interface device. The in-store interface device can be tuned to provide recommendations according to the available store inventory and/or product line. Alternatively, the user may interact with the system while mobile within a mall or other similar environment. The interaction once again may occur via direct interaction with an input device, such as a touchscreen, or, interaction may occur via the passive delivery of a wirelessly transmitted unique identifier that communicates the user&#39;s identifier to initiate recommendations based upon the user&#39;s StyleDNA  40 . Likewise, a user may enter the site by interacting with an email, text or other digital communication wherein the email or text communicates items of potential interest to the user and the user can directly investigate the item or access the site via a link on the email. 
     At step  120 , the user is provided with two choices: 1) Create Account, or, 2) Use StyleGame. If a first time user, the system will require the user to first create an account. The system prompts the user to enter information to establish their contractual relationship and account with the system. If the user has an existing account, the system launches the user to an interactive interface, called the StyleGame  20 , where a matrix of images is selected by the user. The StyleGame  20  orchestrates the creation of a user&#39;s unique personal preference profile, otherwise known as the user&#39;s StyleDNA  40 . 
     At step  130 , Select Items from Matrix, the system prompts the user to select a plurality of items from various image sets for inclusion in the StyleGame  20  matrix. The system repeats step  130  until the user has completely filled the StyleGame  20  matrix with images selected from the earlier image sets presented to the user.  FIG. 12A ,  FIG. 12B  and  FIG. 12C  illustrate exemplary screens displayed to a user during the user&#39;s interaction with and completion of the StyleGame  20 .  FIG. 12A  is an illustration of the initial empty matrix displayed to a user before any items have been selected from various image matrices;  FIG. 12B  is an illustration of a partially completed matrix;  FIG. 12C  is an illustration of a completed matrix. 
     The user interacts with and selects images of items from one or more matrices of pictures or image sets. At step  140 , Create N-Dimensional User Vector, the system causes the computer system to process the various attributes associated with the user&#39;s selections according to one or more algorithms. Once the user has selected the number of images required to complete his StyleGame  20  matrix, the system generates an N-dimensional user vector  92 . At step  150 , Confirm Matrix Choices, the system prompts the user to confirm his matrix choices. The user confirms completion and satisfaction with item choices, with the option to change any previous image selections. Any changes will be reflected in a newly created user vector  92 . 
     At output level  200 , System Outputs and Basic User Activity, the system causes various outputs  205  to be generated and supports basic user activity. At step  210 , Categorize Vector, Find &amp; Display Related Item, the system categorizes the user vector  92  associated with the user&#39;s StyleDNA  40  and then correlates the user vector  92  against a database  60  of items to find and display relevant items for recommendation to the user. 
     At step  220 , Review, Select, and Buy Items, the user may review displayed items, and then select them interactively to learn more about the item or to purchase the item via affiliate networks, or directly through the site. At step  230 , Filter Items by Selected Criteria, the user has the option to filter initial displayed results by style dimension, occasion, color, price, brand, or other attributes. 
     At level  300 , Advanced User Activity, System Refinement, the method and system presents the user with additional options to alter various prior inputs  105 . At step  310 , Provide Feedback, Browse Database, the system prompts the user to provide feedback on an item a number of different ways: rating (like/dislike), commenting, recommending, or other options. Additionally, the user may opt to browse other items in the database (whether related or not), “follow” other users of choice, or simply browse choices made by other users. At step  320 , Input User Profile Info, the system presents an input page to the user so that he may enter personal information for potential presentation to other users of the system. 
     In another aspect, the system provides additional input pages to allow the user to configure and adapt his StyleDNA  40  according to additional information input by the user, such as height, weight, hair color, brand preferences, price preferences, color preferences, material preferences, fit qualifiers and other relevant attributes, specific to the recommended items. 
     At step  330 , Vector &amp; Prediction Refinement, the system adapts and refines both the user&#39;s input vector  92  and predictive output. As user activity, selection, and site input increases, the system dynamically adjusts a user&#39;s initial input vector  92  and related item predictions to account for and reflect these additional inputs. 
     At step  340 , Update Preferred Items, the system automatically updates correlated items to present new or updated recommendations of preferred items to the user. In addition to these automatic vector and prediction updates based on user activity, a user may also force updates by changing his inputted preferences at any time. 
     Creation of Input Vectors—Referring to  FIG. 2 , the input vector structure  90  drives multiple aspects of the method and system. , the system leverages three primary vectors: 1) image vector  91 , 2) user vector  92 , and 3) item/brand/content vector  93 . The system creates an individual image input vector  91  (hereinafter, the image vector  91 ) which is associated with each image of an item/brand/content presented to a user. An aggregate input user vector  92  (hereinafter, the user vector  92 ), in one aspect, is computed by the system as the weighted average of the image vectors  91 . 
     This transformation of a user&#39;s preference of certain items into an aggregate user vector  92  is described by the following equation: 
     
       
         
           
             
               v 
               input 
             
             = 
             
               
                 
                   ∑ 
                   
                     i 
                     = 
                     1 
                   
                   m 
                 
                 ⁢ 
                 
                   [ 
                   
                     
                       i 
                       1 
                     
                     , 
                     
                       i 
                       2 
                     
                     , 
                     
                       i 
                       3 
                     
                     , 
                     
                       i 
                       4 
                     
                     , 
                     
                       i 
                       5 
                     
                     , 
                     
                       i 
                       6 
                     
                     , 
                     
                       i 
                       7 
                     
                     , 
                     
                       i 
                       8 
                     
                     , 
                     
                       i 
                       9 
                     
                   
                   ] 
                 
               
               m 
             
           
         
       
     
     Wherein, v input  is the user vector  92  and i 1  through i 9  exemplify individual image vectors  91 , with m the number of individual image vectors  91  created and used in establishing the user vector  92 . 
     In a broader aspect, the method and system correlates and recommends any type of item including, among other things, clothing, accessories, jobs, colleges, hotels, food, furniture, decor, electronic devices, destinations, neighborhoods, cars, etc. Likewise, the items and products can be correlated against a plurality of categories or domains. For simplicity, in one aspect, the system herein focuses on recommendations for men&#39;s clothing. However, the methodology and approach is the same for recommendations for any type of item, product, etc. 
     In an embodiment emphasizing recommendation of lifestyle items, the user visits a website, mobile application or other user interface and is prompted with a set of lifestyle image groupings or matrices. The image matrices can be randomized, but include categories such as Cars, Movies, Music, Magazines, Cities, Objects, Alcohol Brands, Activities/Sports, Restaurants, and Houses. The user clicks one image he likes from each category grouping, and upon completion of all selections to complete the matrix, the user is presented by the system with recommended items/brands/content deemed to correlate to the user&#39;s personal preferences and style. In this instance, the user will receive recommendations and be shown specific articles of clothing for purchase, as well as information regarding the clothing brands deemed by the system to be most relevant to his personal preferences and style. 
     Features of the method and system of the inventive subject matter are several. For example, a first user of the system receives recommendations equal in quality to recommendations to subsequent users. Additionally, the system does not require a user to objectively or quantifiably rate, evaluate, or give preferences for clothing, clothing brands, or anything immediately related to fashion to generate his or her personal preference profile, known as the StyleDNA  40 . Further, the system uniquely and directly matches discrete clothing items, brands and other content with the user according to his or her StyleDNA  40 . Still further, the system does not associate or assign a user to predetermined categories to justify the presentation of specific groups of products based upon those assigned predetermined categories. 
     The method and system provides a vehicle that gathers and receives input information about a user&#39;s unique lifestyle preferences. This input information is then correlated to clothing, brands, content and other lifestyle items. The computer-implemented method and system comprises elements of style dimension mapping, inputs, outputs, and one or more algorithms. 
     Now, in greater detail, the structure, function and operation of the method and system of the input management aspect of the inventive subject matter is described. 
     Referring to  FIG. 4 , in one aspect, upon visiting the StyleSeek website  400  associated with the method and system, the user is presented with an option to either directly Log In  402 , wherein the user can access a previously-created account  700  and saved preferences, or, to Create Account  404 , wherein the user can generate a new user account to access the system. Alternatively, in another aspect, a user may also engage with the system while reviewing the StyleDNA of another user or while simply reviewing brands, items, and other content at another website. Fundamentally, a prospective user can access the functionality of the system from any other application or even from an individual image of a product of interest to the user. This element of ubiquitous access expands the applicability of the system to any interactive digital source of information. 
     In the case of viewing another person&#39;s StyleDNA, the user is provided with an option on the StyleSeek website  400  to import the other person&#39;s StyleDNA. Consequently, the user is able to access the Log In  402  and Create Account  404  options directly from an Import StyleDNA function  2620 . However, if a user is not logged into the site under an existing account  700 , upon attempting to use the Import StyleDNA function  2620 , the system presents the user with the option to either Log In  402  or Create Account  404  options. After the user has logged in or created a new user account, the system then performs a first-time user check  412  to determine whether the user is new or has previously used the StyleSeek website  400 . If the user is new, the system prompts the user to create a first StyleDNA  41  via an interactive computer-implemented process hereinafter referred to as a StyleGame module  20 . A user&#39;s first StyleDNA  41  is created through completion of the StyleGame  20 . If the user began the StyleSeek website  400  entry process with the Import StyleDNA function  2620 , the particular StyleDNA  40  the user selected for import is added to the user&#39;s Imported StyleDNA  2120  along with the de novo creation of the user&#39;s initial StyleDNA  41 . If the user is not new and has an existing account  700 , the user is taken directly to the main web page  420  of the StyleSeek application, bypassing the initial StyleGame  20  process required for new users. 
     Manage StyleDNA Module—Now, referring to  FIG. 5 , a Manage StyleDNA module  500  of the method and system is disclosed. In one aspect, a StyleDNA site section  510  can be directly accessed via a Log In screen  402 . After logging in, the user may go directly to the Manage StyleDNA page  530 , or, if it is the user&#39;s first time logging in, they may be taken to StyleGame  20  before accessing the Manage StyleDNA page  530 . At the Manage StyleDNA page  530 , the user is presented with a list of his various StyleDNA  40 , represented by a StyleDNA picture icon  2110 , name, and date. The user may hover over any StyleDNA picture icon  2110  included in the list with his mouse pointer or other input method and select a gear icon that appears while hovering over the picture icon  2110 . Selecting the gear icon enables a function to allow the user to rename the specific StyleDNA  40 . 
     Global Module—Now referring to  FIG. 6 , a Global module  600  allows a user to review non-personalized items. A “non-personalized” item is defined herein as an item presented to a user wherein the item has not been earlier selected by the user for incorporation as a vector used to create the user&#39;s active StyleDNA  40 . Access for review of non-personalized items is available through three primary functions. First, a user may select the “All Brands” function  620 . Alternatively, a user may select the “Occasions” function  640 . Finally, a user may select a separate Search function  660  at the top of the homepage. 
     Additionally, to further expand access to specific items that might be relevant to a user&#39;s StyleDNA  40 , a search box  662  at a top right hand corner of the main web page  420  allows the user to access the Search function  660  and perform a keyword search for specific items like “blue polo shirt,” product features like “Goodyear belt,” or even styles like “Street Style” or “Alternative.” The search box  662  is available globally from any page on the StyleSeek website  400 . 
     User Account—Now referring to  FIG. 7 , the system provides a user&#39;s account  700  containing data relevant to the user&#39;s individually identifiable information, the user&#39;s website preferences, and, general information concerning the website  400 . In one aspect, the user accesses his account  700  by selecting “Account”  702  in the upper right hand corner of the StyleSeek website  400 . The user may view and edit his individually identifiable information and preferences by selecting “My Profile”  710 . He may enable a function to submit content to the StyleSeek website  400  by selecting the “Submit Content” module  730 . He may contact the website owners or managers by selecting “Contact Us”  750 . He may access a help page with information about using the StyleSeek website  400  by selecting “Help”  770 . 
     Concerning content contribution by a user, in addition to using the functionality of the system to develop his or her own StyleDNA  40 , the system likewise provides functionality to allow a user to contribute original or sourced content to the StyleSeek website  400 . The system can use this contributed content in a number of different ways. To contribute content, the user first selects and clicks on “Account”  702  in the upper right hand corner of the StyleSeek website  400 . This launches a separate content submission module  730  to allow the user to “Submit Content.” The content submission module  730  allows the user to contribute original or sourced content. In one aspect, before any user-contributed content may be added to the system, the contributed content is reviewed by a site content review team, which may consist of human reviewers, an automated software review system, or a combination thereof, to determine if the offered content is relevant and appropriate to aggregate with the site database for subsequent presentation to other users. 
     Referring to  FIG. 8 , the method and system likewise supports search by all available brands in the system. For example, a user can search broadly by specific brands by clicking the “All Brands” dropdown selector  622  on the main web page  420 , or, by using the search box  662  on the main web page  420  to find a specific brand. Clicking the All Brands dropdown selector  622  presents the user with a brands list  624  of all brands associated with content in the system, arranged alphabetically. Referring to  FIG. 32 , selecting a particular brand will filter content results to that brand, displaying brand images  5120  representing products aggregated from the selected brand. The All Brands dropdown selector  622  and its associated functionality is available globally from any page of the website  400 . 
     Referring to  FIG. 9 , the main web page  420  includes an “Occasions” dropdown selector  642  that allows the user to access the Occasions function  640  and view products and content grouped by occasion categories  644 , such as “Job Interview,” “Date Night,” “Tech Startup,” and others. 
     Referring to  FIG. 10 , results are displayed on an Occasions results page  646 . A product may be associated with multiple occasions results. The Occasions dropdown selector  642  is available globally from any page on the website  400 . 
     StyleGame Module  1000 —Now referring to  FIG. 11 , the structure and operation of the StyleGame module  1000  is illustrated and described. As previously indicated, a user creates his or her own unique StyleDNA  40  by “playing” the StyleGame  20 . In initiate step  1100 , the system prompts a user to start the StyleGame  20 . An objective of the StyleGame  20  is to complete an image matrix  1210 . At selection step  1200 , to create a first StyleDNA  40 , a new user is presented with one or more image groups  1220  and prompted by the system to select one or more images  1225  from the image groups  1220  to populate the image matrix  1210 . Each image group  1220  displayed to the user is comprised of variable image content. Each image  1225  is indicative or illustrative of various lifestyle preferences. Image groupings  1220  are provided to a user in categorical or randomized presentation. In one aspect, each image group  1220  includes images  1225  having a common category likely to be implicitly or explicitly discernible to a user. Exemplary categories include but are not limited to Cars, Movies, Music, Magazines, Cities, Objects, Alcohol Brands, Activities, Sports, Restaurants, and Houses. Each image  1225  provided in a group  1220  is mapped via associated metadata to an assessment engine comprising one or more proprietary algorithms. Each algorithm is configured to assess an n-dimensional space for various style components. 
     Referring to  FIG. 12A, 12B and 12C , an illustrative example of the process associated with playing of the StyleGame  20  is described. At selection step  1200 , where the user selects items to populate a StyleGame input matrix  1210 , the user is presented with a first interface screen ( FIG. 12A ) , where the user is prompted to select at least one image  1225  from a first image grouping  1220  to fill a first empty cell  1215  of the StyleGame input matrix  1210 . After this image  1225  is selected and added to the StyleGame input matrix  1210  to fill the empty cell  1215 , additional image groupings  1220  are subsequently presented to the user for selection of an image  1225  from each subsequent image grouping  1220 . 
     Referring to  FIG. 12B , an exemplary screen shot of the StyleGame input matrix  1210  is shown just prior to completion. Eight of nine cells  1215  have been filled and only one empty cell  1215  remains. 
     Referring to  FIG. 12C , the StyleGame input matrix  1210  has been completed by the user. The system then displays an option button  1402  to the user to allow the user to submit the current completed StyleGame input matrix  1210  to the system for processing. Once the user selects and activates the Submit button  1402 , the method and system transforms the image selections in the StyleGame input matrix  1210  and generates a preliminary input vector  92  for the user. The preliminary input vector  92  is a transformational composite of the individual image selections  1225  gathered in the StyleGame input matrix  1210 . 
     Once again referring to  FIG. 11 , and with additional reference to  FIG. 2 , upon completion of the StyleGame  20 , the system ingests and processes the user vector  92  created at step  1300 . The system at step  1400  presents the user with an option to confirm selection of the chosen images  1225  in the StyleGame input matrix  1210 . Once the user has confirmed his matrix choices, at step  1500 , the system then creates a StyleGame StyleDNA  41  based on the user vector  92 . The system then performs one or more calculations driven by one or more algorithms to create an association of the preliminary user vector  92  with one or more databases  60 . Each database  60  is comprised of a plurality of different items to which the preliminary user vector  92  will be correlated. At a higher level, once the user confirms choices at step  1400 , and the system creates the user&#39;s first StyleDNA  41  at step  1500 , at step  1600 , the system then finds and displays correlated items to the user. 
     Now referring to  FIG. 11 ,  FIG. 13 , and  FIG. 14 , when a user completes the StyleGame  20 , at step  1500 , the system creates and saves a unique StyleDNA  41 . This StyleDNA  41  is displayed on the user&#39;s Manage StyleDNA page  530  as “StyleGame DNA dd-mm-yyyy”, wherein “dd” is the day of the month, “mm” is the month, and “yyyy” is the current year. This first StyleDNA  41  is fully customizable and re-nameable. Images in any StyleDNA  40  may be removed or replaced at any time with other images a user has selected and made available within an area of the website  400  referred to as the MyDNA Holding Area  2140 . A user can create additional StyleDNA  40  using the results of the StyleGame  20  or by using new images selected from the MyDNA Holding Area  2140 . 
     Once the system has created a first StyleGame StyleDNA  41  based on the user&#39;s completion of the StyleGame  20 , the user is presented with access to a main web page  420  of the StyleSeek website  400 . In one aspect, the StyleSeek main web page  420  provides access for the user to three primary web page tabs  430 ,  440 .  450 . The user may interactively select any of the tabs using a selection device such as a mouse, a touch screen interface or vocal activation. The tabs include “EXPLORE”  430 , “BUY”  440 , and “BRANDS”  450 . A user selects any of these tabs  430 ,  440 ,  450  to access content that has been personalized for the user based on the user&#39;s currently selected and active StyleDNA  40 . 
     Manage StyleDNA- Referring to  FIG. 13 , the block diagram illustrating the structure and operation of the Manage StyleDNA module  2000  is described. For purposes herein, StyleDNA are generally referred to with the reference numeral  40 . However, various StyleDNA may be described with other reference numerals for clarification, such as, 1) first StyleDNA  41 , 2) new StyleDNA  42 , 3) newly created StyleDNA  43  and 4) User-shared StyleDNA  44 . The Manage StyleDNA module  2000  may be accessed from the Manage StyleDNA page  530 . The Manage StyleDNA module  2000  serves as a portal to more advanced functions associated with creation or modification of StyleDNA  40 . First, the StyleGame StyleDNA  41 , which is first created by the user, is accessible. Additionally, other StyleDNA  40  are accessible. Further, other images  1225  are accessible via an area referred to as the MyDNA Holding Area  2140 . The MyDNA Holding Area  2140  displays other images  1225  of interest previously saved by the user, which may be selected for use in one or more of current or new StyleDNA  40 . 
     The Manage StyleDNA module  2000  supports several functions. Referring to  FIG. 15 , at step  2200 , a user may rename existing StyleDNA  40 . Referring to  FIG. 16 , at step  2300 , a user may create a new StyleDNA  42 . At step  2400 , a user may delete one or more of his stored StyleDNA  40 . At step  2500 , a user may retake the StyleGame  20  to create a new StyleDNA  42 . At step  2610 , a user may browse the pre-created StyleDNA  40  of others. Finally, at step  2620 , a user may import the StyleDNA  40  of others. A user may also view StyleDNA  44  that has been created and publicly shared by other users. 
     Having developed an initial StyleGame StyleDNA  41 , and recognizing that a user&#39;s style preferences may evolve or change as driven by various factors, including time, environmental factors, peer influence, media, magazines, family, lifestyle or other such influences, the method and system allows the user to modify his existing StyleDNA  40  at any time. The modification is performed by substituting an existing selected image  1225  with any other image  1225  within one or more image database  60 . As a user identifies a desired image  1225 , he can elect to make a substitution at any time. The system will then automatically update his StyleDNA  40  with a new set of images  1225  and a corresponding user vector  92 . The method and system provides real-time functional interactive input by the user to change any or all of the images  1225  that drive and comprise his existing StyleDNA  40 . 
     The method and system allows a user to create additional StyleDNA  40  specific to the user without abandoning either his first StyleGame StyleDNA  41  or other subsequently developed StyleDNA  40 . The system allows a user to create a second new StyleDNA  42  from a blank template using any image within a StyleSeek database  60 . As with creation of a first StyleDNA  41 , the system provides a user interface associated with the StyleGame  20  wherein a user is provided searchable access to one or more database  60  of StyleSeek images. The user may then select one or more images  1225  to add to his StyleDNA  40  profile. In one aspect, six images  1225  are selected to generate a subsequent query to populate and implement one or more algorithms used in calculating a user&#39;s lifestyle input vector  92 . Throughout the operation of the system, a user may remove or replace images  1225  at any time. With each change, the system dynamically creates an updated corresponding input vector  92  that overwrites the existing input vector  92 . As a user creates one or more StyleDNA  40 , individual StyleDNA  40  can be saved to a user&#39;s account profile. 
     Once a StyleDNA  40  has been completed, the system then allows a user to designate whether that StyleDNA  40  is eligible for sharing with other users. The owner user may click an option button to publish the image set associated with his or her selected StyleDNA  40  onto a public facing website, Shared StyleDNA  2630 . When published, other users can copy the User-shared StyleDNA  44 , incorporate the User-shared StyleDNA  44  within their own user profile, and add the User-shared StyleDNA  44  to their store of other StyleDNA  40 . This may be accomplished by the Import StyleDNA function  2620  of the system. A user may elect to enable or disable sharing of his StyleDNA  40  at his discretion. In one aspect, any User-shared StyleDNA  44  previously shared and accessed by other users, will remain available to those users. In another aspect, once a user elects to stop sharing his or her StyleDNA  40 , the system will automatically extract the previously shared StyleDNA  44  from other user accounts. Alternatively, the system will support updates of a User-shared StyleDNA that are offered to those users currently sharing the specific StyleDNA. 
     Before a user can incorporate a User-shared StyleDNA  44  in the user&#39;s own account, the system requires the user to import the User-shared StyleDNA  44  into the user&#39;s account profile using Import StyleDNA  2620  function. The system provides user access to a database of User-shared StyleDNA  44 . The User-shared StyleDNA  44  are provided for access via Shared StyleDNA  2630  on a public facing Internet or web page. New or existing users may click an option button on the User-shared StyleDNA  44  page to import images and input vectors associated with other shared StyleDNA  44  into their personal accounts. Importation produces a local copy of the User-shared StyleDNA  44  in the user&#39;s account, allowing it to be used to create or modify existing StyleDNA  40 , or, for sharing with others. 
     Preferences change; attitudes change; moods change which may affect preferences. Consequently, the system provides users with the option of selecting and activating any of their StyleDNA  40  at any time. At a main web page  420  of the StyleSeek website  400 , a user can see all of the StyleDNA  40  vectors and image sets he has available for use. The user simply selects a StyleDNA  40  from his library to activate that StyleDNA  40 . The activated StyleDNA  40  will then be used by the system as a basis for generating recommendations until the user changes and activates another StyleDNA  40   
     Referring to  FIG. 17 , a user can create new StyleDNA  40  through two options accessible via the Manage StyleDNA page  530 . In a first option, the user selects the “+” sign  2310 . Alternatively, the user can click the “CREATE StyleDNA” button  2302  at the top right of the Manage StyleDNA page  530 . Creating New StyleDNA  42  will create a blank StyleDNA template  2133  with six boxes. The user is then able to name the newly created StyleDNA  40  and customize the contents of the new StyleDNA  40  by selecting and dragging in any images  2142  from the MyDNA Holding Area  2140  on the page. The user can hover over images  2142  in the StyleDNA template  2133  to reveal a “Remove” button  2135 . Selecting the “Remove” button will delete the image  2142  from the StyleDNA template  2133 , returning it to the MyDNA Holding Area  2140  and leaving a blank box that may be filled with other images  2142  from the MyDNA Holding Area  2140 . 
     Any image  1225  available through the databases  60  of the system can be added to the MyDNA Holding Area  2140  by clicking an “Add to MyDNA” button  3202  that appears when the user hovers over any image  1225  displayed by the system. The image  1225  is then converted and identified as a MyDNA Holding Area image  2142 . 
     When an image  2142  is added to the MyDNA Holding Area  2140 , it becomes immediately available for use in any of the user&#39;s existing or newly created StyleDNA  40 . Images  2142  added to the MyDNA Holding Area  2140  do not influence a user&#39;s StyleSeek results until the user adds them to an existing StyleDNA  40  and activates the new/modified StyleDNA  40 . Hence, a user can freely add any images  2142  to the user&#39;s MyDNA Holding Area  2140  as the user browses the system site. In addition, a user may delete any or all images  2142  from the MyDNA Holding Area  2140 . 
     Referring to  FIG. 2 , in an alternative aspect, the method and system implements a set  94  of additional vectors to modify the application and comparison between the user vector  92  and item/brand/content vectors  93 . For example, the method and system computes a separate MyDNA input vector  95  from an algorithmic aggregation of the images  2142  in the MyDNA Holding Area  2140 . The system may then use the MyDNA input vector  95  to influence preferences presented to a user. The system will allow the MyDNA input vector  95  to be ascribed a lower, equal or higher weight than the aggregate user input vector  92  developed via the StyleGame  20 . In an additional alternative aspect, the method and system tracks and correlates those images that are removed from the MyDNA Holding Area  2140  or from existing StyleDNA  40 . These deleted images may then be used by the system to create an additional MyDNA deleted images input vector  96 . Again, this deleted images vector  96  may be ascribed a lower, equal or higher weight than other vectors. The system may then be configured to apply any of these vectors with differing weight during the correlation step to refine preferences presented to a user. Still further, in another alternative aspect, the user vector  92  may be adapted by the system to address and consider a user&#39;s specific likes and dislikes. The user may indicate specific images or items as liked or disliked, such indications tracked by the system in a like filter vector  97  and a dislike filter vector  98 . Other filter vectors  99  may be likewise be applied for other types of filters and processed by the system during the refinement of preferences and before display of recommendations back to the user. 
     EXPLORE Module—Now referring to  FIG. 21 , the structure, function and operation of the EXPLORE module  3000  is disclosed. By selecting the EXPLORE page tab  430  on the main StyleSeek web page  420 , the Display Items Related to Active StyleDNA function  3100  is invoked, presenting the user with a selection of blog posts and reviews which the system has scoured and aggregated from across the Internet. The method and system personalizes the presented selections according to the user&#39;s currently active StyleDNA  40 . Selection of the EXPLORE page tab  430  by the user triggers the presentation to the user of a personalized digital magazine  3105  wherein all the images and articles are selected specifically for the user based on his StyleDNA  40 . At step  296 , the user may change the currently active StyleDNA  40  if he wishes to view a display of items selected with a different StyleDNA  40 . 
     Also, as elsewhere within the StyleSeek website  400 , while active within the EXPLORE module  3000 , the user may elect to add items  1225  to the MyDNA holding area  2140 . 
     Further, the system allows a user to view additional detail for a selected item. Selecting any item within the personalized digital magazine  3105  on the EXPLORE page  430  displays an item detail page  432  providing additional information concerning the item. By selecting and invoking View Item Selection Detail function  3300 , an item detail page  432  is presented containing a diverse set of information related to the selected item. In one aspect, the item detail page  432  contains relevant pictures, one or more short excerpts from relevant blog posts, and/or one or more links to the full text of each blog post or related items. The system supports delivery of multiple forms of content on the item detail page  432  including published articles, magazine reviews, detailed specifications and other narrative, graphic or descriptive content. 
     Again, as the user views detail associated with a selected item, any item from that detailed view may likewise be added to the MyDNA holding area  2140 . This immediate functionality allows a user to lever subliminal or visceral response to the item and consider it for future use in his or her StyleDNA  40 . Further, the user may select and invoke the Visit External Source Articles Offsite function  3330  to access external articles outside the primary StyleSeek website  400 . Additionally, a user may view Related Items  3350 . 
       FIG. 22  is an exemplary screen shot of a representative StyleSeek main web page  420  illustrating a personalized digital magazine  3105  with display of personalized items  3110  to a user. 
     Now referring to  FIG. 23A  and  FIG. 23B , while still within the EXPLORE page  430 , a user may hover his pointing device over any item image to trigger an overlay  3130  displaying a headline relative to the content of the image and an option to add the image to the user&#39;s MyDNA. The user may then elect to add the image to the MyDNA Holding Area  2140  by selecting “ADD TO MyDNA”  3202 , which invokes the Add Item Selection to MyDNA Holding Area function  3200 . As shown in  FIG. 23B , the system will notify the user that the particular item has been added to the MyDNA Holding Area  2140  by changing the overlay  3130  to show the text “ADDED!”  3220  in place of “ADD TO MyDNA”  3202 . 
     Now referring to  FIG. 24 , while still within the EXPLORE page  430 , the user may also elect to explore the item detail page  432  and then select and add an item from that page to the MyDNA holding area  2140 . The user may also elect to drill down into other elements such as the Visit Source Article Offsite hyperlink  3330  or view Related Item  3350 , which will provide additional opportunities to add items to the MyDNA holding area  2140 . 
     The functionality associated with the EXPLORE page  430  allows a user to freely explore and drill down throughout items displayed and continuously acquire images of items and add to the MyDNA Holding Area  2140  at any time. This continual ability to acquire images representative of a user&#39;s lifestyle preferences at any time allows the system to adapt and leverage the user&#39;s visceral response to images and content. 
     EXPLORE Blog Browse—A user may also buy products from blog posts presented via the EXPLORE page tab  430 . Note that the system allows a user to buy products from any content or article delivered to the user, including but not limited to email or text messages. When reviewing articles in a blog post, relevant brands and products are presented to the user in a “Related Items” section  3350  of the Explore page. When a product is available for purchase, the system will list the item in this the Related Items section  3350 . A user may then elect to pursue purchase of the item by clicking on the image of the item of interest. Then, as before, the system navigates the user to a specific product page where the user can purchase the item without further interaction from the StyleSeek system. Again, the separation between the StyleSeek system and the product sellers ensures that the algorithmic and database aspects of the method and system may remain pure and untainted by other non-stylistic influences from retailers or vendors of the various products or services. 
     Now referring to  FIG. 25 , the system provides a means to allow a user to identify the currently active StyleDNA  40 , and, to change from one StyleDNA  40  to another StyleDNA  40 . In an upper left corner of the main web page  420 , a smaller clickable image  2852  is presented. The clickable image  2852  represents and correlates to the user&#39;s currently active StyleDNA  2920 , which is used by the system to personalize the user&#39;s experience. By selecting the clickable image  2852 , the system causes a separate dropdown list  2910  to be presented to the user. The dropdown list  2910  presents icons for all the StyleDNA  40  currently in the user&#39;s account. The user can elect to select and activate another of his existing StyleDNA  40  from the dropdown list  2910 . Alternatively, he may select the option, Manage StyleDNA, which will launch him to the “Manage StyleDNA” page  530  to further change or customize one or more of his StyleDNA  40 . 
     BUY Module—Now referring to  FIG. 27  and  FIG. 28 , the function and operation of the BUY module  4000  is disclosed. The selection of the BUY web page tab  440  causes the presentation of a new web page that invokes the Display Products Related to Active StyleDNA function  4020 , displaying product items  4040  available for purchase based on the user&#39;s unique StyleDNA  40 . These products are generally presented in a manner that allows them to be purchased immediately via an e-commerce portal associated with the product. The user can directly add an item to the user&#39;s MyDNA Holding Area  2140  by hovering over the product item  4040  and invoking the Add Product Selection to MyDNA Holding Area function  4200 . Clicking an item on the BUY web page  440  invokes the View Product Selection Detail function  4300  and displays a full product detail page  442 , where a user is presented with additional detail about the item, including price alternatives, product reviews, and where the item might be purchased. The user may select a View Brand Page link  4340  to view the page associated with the particular brand of the selected product. 
     Related Articles—Referring to  FIG. 27 , the method and system provides functionality that allows a user to browse and view related blogs posts, articles, reviews and other content in a “Related Articles” area  4350  displayed on individual full product detail pages  442 . Related articles may be displayed based on similarity in style of the article topics to the selected product or other criteria describing specific user interests and lifestyle preferences. 
     Product Alternatives—Referring again to  FIG. 27 , the method and system also provides functionality that allows a user to browse and view alternative products in “Product Alternatives”  4361  displayed on individual full product detail pages  442 . Alternative products are defined herein as products that are very similar in style to the selected product, but are available in different price ranges. The “Product Alternatives” area  4361  of an individual full product detail page  442  includes other items of the same basic type (for example, pants, belts, or jackets) as a selected product that are deemed by the function  4360  of the method and system to be related in style to the selected product. In one aspect, alternative products are arranged and presented to a user by price, arranged low to high. In another aspect, the alternative products are filtered to display products in a desired price range selected by the user. Thus, the system allows a user to explore various items without concern for price, and then later, adapt recommendations and selections to conform to a predetermined price range. Likewise, an additional aspect of the system allows a user to create a StyleDNA  40  without considering price. 
     Complete The Look—Referring again to  FIG. 27 , the system provides a Complete the Look function  4370  to allow a user to select additional products to complement their existing stylistic configuration. A “Complete the Look” area  4371  of an individual full product detail page  442  includes other items that are deemed by the Complete the Look function  4370  of the method and system to be related in style to a selected product. The method and system applies a user&#39;s StyleDNA  40  to identify other correlated items that are deemed to pair well with a product currently under review by the user. For example, the system may present a pair of pants for consideration to the user wherein those pants have been deemed to stylistically complement and correlate with a pair of shoes the user is contemplating. In one aspect, the system includes and presents items in the “Complete the Look” area  4371  that are in the same relative price range as the main product being viewed by the user. The user can refresh the recommendations in “Complete the Look”  4371  by clicking the Refresh button within that section, or by refreshing the web browser. This refresh causes the system to present a new set of correlated items deemed to pair with the item currently under consideration. This rapid refresh of alternatives once again allows a user to leverage their visceral response to images to continually transform and evolve their StyleDNA. 
     Price Filtering—Referring to  FIG. 28 , the system also allows a user to modify or normalize their StyleDNA  40  to the user&#39;s desired price range. The system provides two alternatives to filter presented products by price range. First, when in the BUY web page  440 , the user can check one of the price filter options  4120  at the top right hand corner of the page. The price filter options  4120  are presented as $, $$, $$$, and $$$$, where a single $ represents the lowest price, ranging to the highest prices represented by the $$$$. Second, the system allows a user to filter by price on an individual product page using the same selection criteria. This filtering functionality delivered by the system allows the user to browse through the Product Alternatives  4362  to view items of the same style in a price range acceptable to the user. 
     Now referring to  FIG. 29 , in greater detail, shown on the full product detail page  442  is a section referred to as the Complete the Look area  4371 , displaying results of the Complete the Look function  4370 . This area displays to the user other items that the system deems related in style to a selected product and expects to pair well with that product. Items displayed to the user in the Complete the Look  4371  area are selected by the system to fall in the same price range as the main product the user is viewing. The user may select an item to activate the View Complete the Look Product Selection Detail function  4372  and display the full detail page for the item. Another function activated when a user visits the full product detail page  442  is Product Alternatives  4360 . The Product Alternatives area  4361  of the product detail page  442  displays to the user other products of the same type deemed by the system to be very similar in style to the selected product and of potential interest to the user. The user may select an item to activate the View Product Alternative Selection Detail function  4362  and display the full detail page for the item. 
     Vendor Neutrality—Referring to  FIG. 27  and  FIG. 29 , when viewing individual full product detail pages  442  on the StyleSeek site  400 , the user selects and clicks a buy button  4330 , which is typically presented to the right of the product image, to connect directly with the seller of the product. Upon connection with the product seller, the user may elect to complete a purchase of a recommended product. The method and system is implemented in a manner that maintains neutrality in recommendations to users to ensure that any recommendations are driven by user-centric preferences and not vendor-centric sales preferences. Hence, to maintain this neutral position on product selection and matching to users, the system is designed to connect users directly with the sellers of products of interest. Consequently, since the method and system are neither driven nor influenced by a desire to reduce existing product inventory, the method and system ensures purity in its recommendations, independent of current economic drivers. Consequently, the method and system provides direct connectivity to sellers. Of course, in another aspect, the system may be configured to provide recommendations limited to certain brands, thus supporting the use of the system as a marketing tool for a specific retailer or designer. 
     BRANDS Module—Next, referring to  FIG. 30  and  FIG. 31 , the function and operation of the BRANDS module  5000  is disclosed. Selection of the BRANDS web page tab  450  activates the Display Brands Related to Active StyleDNA function  5100 , causing the system to display an assortment of brands which the method and system have deemed most relevant to the user&#39;s style, based upon the user&#39;s unique StyleDNA  40 . The user can directly add an item to the user&#39;s MyDNA Holding Area  2140  by hovering over the brand item  5110  and invoking the Add Brand Selection to MyDNA Holding Area function  5200 . Selecting and clicking any brand item  5110  will activate the View Brand Selection Detail function  5300 , displaying the item&#39;s own brand detail page  452  that includes representative collages, blog reviews, and products. 
     Referring to  FIG. 33 , the brand detail page  452  presents the user with further information about the selected brand, as well as several functions. The user may select a Visit Brand Website hyperlink  5330  to access the external website, if available, associated with the selected brand. Furthermore, the brand detail page  452  contains sections for Brand Products  5340 , displaying images with information and links to product detail pages  442  for products of the selected brand. Also shown is a section allowing the user to View Articles Featuring Specific Brand Products  5350 , which aggregates links to articles from the site related to the selected brand. 
     Active StyleDNA and Personalization—A user is able to manage his StyleDNA  40  throughout the StyleSeek website  400 . Referring to  FIG. 26 , we show an exemplary screen where the user has selected and activated a different StyleDNA  40 , and thus, the image associated with that particular StyleDNA  40  is now shown in the clickable image  2852  as the currently active StyleDNA  40 . In addition, the display of items  3110  displayed to the user has likewise changed to reflect the activation of the other StyleDNA  40 . 
     To further manage his StyleDNA  40 , the user may also select a StyleDNA link  531  above the search bar on the main StyleSeek web page  420 , or from any page globally on the website, to access the Manage StyleDNA page  530 . 
     The resulting StyleDNA  40  developed by the method and system through interaction with a user personalizes the user&#39;s StyleSeek experience. The user&#39;s StyleDNA  40  tells the system what products and content are likely most relevant to display to the user. In one aspect, a user&#39;s StyleDNA  40  is represented by six images. These six images are determined by the system to be the six strongest graphical representations of the user&#39;s personal style, selected from the system&#39;s database of items or products. In additional aspects, a user can select more than six images to represent the user&#39;s individual style. Additionally, the method and system likewise allow a user to create and experiment with alternative images to create additional StyleDNA  40 . 
     Import StyleDNA - Referring to  FIG. 13 , the method and system provides software functionality to allow a user to browse and import one or more Pre-created StyleDNA  43  into his account  700 . Once imported, additional StyleDNA  40  can be activated and used to browse the StyleSeek system for content, products, and brands reflective of that Pre-created StyleDNA  43 . Pre-created StyleDNA  43  can be based upon personal or speculative preferences of real or fictional characters such as Kanye West, Indiana Jones, Mike Tyson, an Ivy-league university professor, a southern California surfer, current or past Presidents, or other such notable personas or individuals. When leveraging an imported StyleDNA  40 , a user is able to view the entire StyleSeek website  400  and have a user experience as if the user were viewing the StyleSeek website  400  through the eyes of the person who created or is represented by the imported StyleDNA  40 . By enabling this experience, the method and system viscerally and subliminally allows a user to refine his personal preference, which may influence the creation, modification or refinement of his StyleDNA  40 . 
     To import a Pre-created StyleDNA  43 , a user navigates to his Manage StyleDNA page  530  and clicks “Browse”  536  to view a variety of importable Pre-created StyleDNA  43  profiles that the user can select for activation. Selecting the Browse link  536  launches the user to a new Browse web page  540 , as shown in  FIG. 18 . The user clicks on any profile that interests him to view its full importable Pre-created StyleDNA  43  in a Browse selection detail page  542 , shown in  FIG. 19 . If the user still wishes to import the specific Pre-created StyleDNA  43 , the user clicks the “Import this DNA” button  2622  to import the new Pre-created StyleDNA  43  to the user&#39;s account  700 . All imported StyleDNA  40 , whether Pre-created StyleDNA  43  or Shared StyleDNA  44 , are contained separately within the Imported StyleDNA module  2120 . StyleDNA  40  in Imported StyleDNA  2120  can be activated and used to browse the StyleSeek system, but cannot be modified by the user. 
     Referring to  FIG. 20 , the imported StyleDNA  40  will appear in the imported web page  550  of the user&#39;s Manage StyleDNA  530  page. The user can access the Imported web page  550  by selecting the Imported link  534  from within the StyleDNA site section  510 . Once imported, the imported StyleDNA  40  can be activated to start browsing the StyleSeek system and application as that imported profile. The imported StyleDNA  40  may also be selected and viewed in full by the user in an Imported selection detail page  552 . From other sections or pages of the StyleDNA site section  510 , the user may select the MyDNA link  532  to return to the Manage StyleDNA page  530 . 
     As with the user&#39;s original StyleDNA  40  inventory, a user may activate any imported and saved StyleDNA  40  by selecting the same image  2852  at the top left hand of the StyleSeek main web page  420 . Selecting the clickable image  2852  causes the presentation of a dropdown list of the user&#39;s available StyleDNA  40 , including any Pre-created StyleDNA  43  and User-shared StyleDNA  44 , from which the user can select any StyleDNA  40  and immediately be presented with updated results from the system. 
     A user can likewise share any of his own StyleDNA  40  with other users of the StyleSeek community. A user shares one or more of his StyleDNA  40  by first selecting any of the StyleDNA  40  listed on the user&#39;s Manage StyleDNA page  530  and then selecting the option to share the selected StyleDNA  40 . 
     The user invokes the function  3200  by clicking the button  3202  to add items to the user&#39;s MyDNA holding area  2140 , or, as the user adds and changes any images associated with his StyleDNA  40 , the system immediately provides access for the user to purchase the products associated with the images of any of the items he may have just added. This feature of the system supports the ability to satisfy the whims of the user, when circumstances, emotions or other motivators might cause the user to be highly motivated to purchase the product. As with products presented via application of a specific StyleDNA  40 , to purchase a product, a user clicks on the image  2142  associated with a product. The user is then taken to a detailed individual page associated with the product where the user is provided with a link to complete the purchase. 
     The method and system allows a user to develop multiple StyleDNA  40 . A user is not limited to the StyleDNA  40  generated by the first playing of the StyleGame  20 . The StyleGame  20  functionality is always accessible from the Manage StyleDNA page  530  and may be accessed by clicking the “RETAKE StyleGame” button  2502 . Retaking the StyleGame  20  causes the system to interactively create and add a new StyleGame StyleDNA  41  to the user&#39;s profile. The new StyleGame StyleDNA  41  does not overwrite the user&#39;s previous StyleDNA  40 . 
     Just as a user may select a StyleDNA  40  by hovering, a user may likewise elect to delete any StyleDNA  40  at any time by hovering over a StyleDNA  40  in the Manage StyleDNA page  530  and clicking the “x” that appears. 
     Brand Selection Criteria—In one aspect, the computer-implemented method and system causes specific brands to be selected for inclusion in the database based on one or more criteria. Exemplary criteria include a strong representation of style, an online brand presence, and products available for sale through an online retail channel. A strong representation of style is deemed the most important criterion. Other criteria are relevant in providing convenience to the user of the site by allowing well-represented brands to be easily purchased. 
     Input Management Method—Now, in additional detail, the method by which various inputs  105  are managed and used to generate StyleDNA  40  and other outputs  205  are described. 
     1. Dimension Mapping—First, style characteristics are determined for each item. For example, in one aspect, key elements identified for men&#39;s fashion are: (1) structure (sharpness, rigidity); (2) color combinations; (3) texture (material softness, thickness); (4) anchoring and supplemental accessories (such as hats, glasses, ties, shoes, belts, watches, and hair style); and (5) history/origin of the style. These style characteristics form a basis for the item/brand/content vector  93 . 
     2. Inputs  105 —To compare users to brands and lifestyle items, an n-dimensional user vector  92  is created for the user with n being the number of style dimensions used. In one aspect, the number of style dimensions used is nine. The user vector  92  is created by first having the user select lifestyle images from pre-determined categories that are strongly correlated to the style dimensions. Each lifestyle or brand image will have its own image vector  91 . A variety of lifestyle elements and brands can be used for this purpose, but exemplary elements and brands from a preferred embodiment include Movies, Cars, Music/Bands, Magazines, Restaurants, Cities/Locations, Houses/Architecture, Lifestyle Items, Lifestyle Activities, and Alcohol Brands. 
     For example, in one aspect, the user is first shown nine images from popular movies. These images are representations or combinations of each of the nine style dimensions used for men&#39;s clothing. After selecting an image, the user is assigned the appropriate value(s) for those positions within an input array. 
     3. Outputs  205 —The user vector  92  is subsequently compared to individual items and brands via one or more computer algorithms wherein the user vector  92  is correlated against the item/brand/content vector  93 . The top matches are recommended to the user to allow for purchase, browsing, and comparison. At this point, the user can buy the items directly from the site or through pre-established affiliate networks via a referral system. The user can also be shown relevant brands, “looks”, and outfits that map to his style. 
     The user may opt to perform additional actions such as: (1) liking/disliking items or brands; (2) browse other users “digital closets” who have purchased, searched or own items; (3) “follow” other users based on their choice of style; (4) make comments; on items/brands/other user profiles and make recommendations to others; 
     (5) enter personal fit data and measurements, and add their favorite brands to their profile; (6) record comments regarding fit for particular items and brands; and (7) add items that they own or want to a digital closet that other users may view. 
     As the user goes through these actions, a secondary algorithm allows for simple additional recommendations such as “see items similar to this.” These recommendations may be based on other user actions and provide an additional level of personalization. 
     Additionally, in another aspect, the system will evaluate the likelihood that an article of clothing will physically fit a user based on his profile information, and the fit data gathered from other users. For instance if a shirt is owned by user A, and user B has similar body type dimensions (from his profile), the system will indicate that it is likely that the shirt will also fit user B. 
     4. Computer-implemented Algorithms—Calculating the correlation between a user vector  92  and item/brand/content vectors  93  is performed using a computer processor. The correlation may be a computationally expensive process and, hence, in an alternative embodiment, the method and system benefits from application to flexible cloud computing infrastructure to allow rapid response. Although the operative algorithm implemented may be fundamentally direct, the processing of the algorithm to transform inputs  105  from a user into relevant outputs  205  and associated recommendations to the user, requires significant computational and storage overhead. Iterating on tens of thousands of items, including all dimensions of the vector  93  for each item or brand, creates a significant computational and storage overhead. 
     In another aspect, the results are cached. Where a user vector  92  may change over time and the number of possible image vectors  91  or item/brand/content vectors  93  can expand, another aspect incorporates preprocessing of all relevant vectors to expedite presentation of the output of those vectors to a user. Again, implementation of either of the cache or preprocessing will still benefit via implementation using a cloud infrastructure to allow access to computing and data storage resources as needed to ensure timely response to users. 
     In another aspect, the method and system supports pre-processing for a subset of possible vectors and approximating results based on these vectors. Thus, the method and system expedites the assessment through a large number of items to identify a smaller representative number for individual consideration. 
     Other algorithmic approaches may be used and implemented as features of the method and system, including matching on aligned dimensions. 
     Alternatively, an aspect of the method and system may be implemented without the requirement of approximating subsets. Instead, the system iterates across all matched items for every request, and then determines individual scores, avoiding an approximation approach. 
     Additionally, the method and system can leverage score lookup tables for each user. Such lookup tables are computationally intensive and require significant data storage space but would be applicable where the availability or cost of storage space is not a significant factor. 
     The method and system may also deploy a hybrid approach where actual user vectors  92  are clustered to provide feedback for refinement of the applicable algorithms used by the system to match user vectors  92  with item/brand/content vectors  93 . 
     Further, in another aspect of the method and system, different data structures may be implemented. For example, data structures such as KD-trees will provide efficient lookups of “nearby” items, even in multiple dimensions. However, this data structure can be difficult to coordinate across the entire software stack. Hash tables are another alternative to support pre-computation of results to shorten lookup times. In another aspect, the method and system stores the scores in the database with the items, allowing a SQL query to use the scores for sorting. In this way, the score table acts roughly as a hash table would for the end user: acting simply as a reasonably efficient key-value store. 
     Processing Environment - Now referring to  FIG. 34 , the method and system is implemented across a global network, generally supported by the Internet and the World Wide Web.  FIG. 34  illustrates a computer network or similar digital processing environment  6000  in which the method and system may be implemented. Client computer(s)/devices  6050  and server computer(s)  6060  provide processing, storage, and input/output devices executing application programs and the like. Client computer(s)/devices  6050  can also be linked through communications network  6070  to other computing devices, including other client devices/processes  6050  and server computer(s)  6060 . Communications network  6070  can be part of a remote access network, a global network (e.g., the Internet), a worldwide collection of computers, Local area or Wide area networks, and gateways that currently use respective protocols (TCP/IP, Bluetooth, etc.) to communicate with one another. Other electronic device/computer network architectures are suitable. 
       FIG. 35  is a diagram of the internal structure of a computer (e.g., client processor/device  6050  or server computers  6060 ) in the computer system of  FIG. 34 . Each computer  6050 ,  6060  contains system bus  6179 , where a bus is a set of hardware lines used for data transfer among the components of a computer or processing system. Bus  6179  is essentially a shared conduit that connects different elements of a computer system (e.g., processor, disk storage, memory, input/output ports, network ports, etc.) that enables the transfer of information between the elements. Attached to system bus  6179  is an Input/Output (I/O) device interface  6182  for connecting various input and output devices (e.g., keyboard, mouse, displays, printers, speakers, etc.) to the computer  6050 ,  6060 . Network interface  6186  allows the computer to connect to various other devices attached to a network (e.g., network  6070  of  FIG. 21 ). Memory  6185  provides volatile storage for computer software instructions  6192  and data  6194  used to implement an embodiment (e.g., object models, codec and object model library discussed above). Disk storage  6195  provides non-volatile storage for computer software instructions  6192  and data  6194  used to implement an embodiment. Central processor unit  6184  is also attached to system bus  6179  and provides for the execution of computer instructions. 
     In one aspect, the processor routines  6192  and data  6194  are a computer program product, including a computer readable medium (e.g., a removable storage medium, such as one or more DVD-ROM&#39;s, CD-ROM&#39;s, diskettes, tapes, hard drives, etc.) that provides at least a portion of the software instructions for the method and system. Computer program product can be installed by any suitable software installation procedure, as is well known in the art. In another embodiment, at least a portion of the software instructions may also be downloaded over a cable, communication and/or wireless connection. In other embodiments, the method and system programs are a computer program propagated signal product embodied on a propagated signal on a propagation medium  6007  (e.g., a radio wave, an infrared wave, a laser wave, a sound wave, or an electrical wave propagated over a global network, such as the Internet, or other network(s)). Such carrier medium or signals provide at least a portion of the software instructions for the routines/program  6192 . 
     In alternate aspects, the propagated signal is an analog carrier wave or digital signal carried on the propagated medium. For example, the propagated signal may be a digitized signal propagated over a global network (e.g., the Internet), a telecommunications network, or other network. In one embodiment, the propagated signal is a signal that is transmitted over the propagation medium over a certain time period, such as the instructions for a software application sent in packets over a network over a period of milliseconds, seconds, minutes, or longer. In another embodiment, the computer readable medium of computer program product is a propagation medium that the computer system may receive and read, such as by receiving the propagation medium and identifying a propagated signal embodied in the propagation medium, as described above for computer program propagated signal product. 
     The term “carrier medium” or transient carrier encompasses the foregoing transient signals, propagated signals, propagated medium, storage medium and the like. 
     While numerous aspects and embodiments of the inventive subject matter have been particularly shown and described with references to specific elements or features thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the scope of the inventive subject matter encompassed by the appended claims. 
     For example, the method and system may be implemented in a variety of computer architectures. The computer network of  FIG. 34  and  FIG. 35  is for purposes of illustration and not limitation of the inventive subject matter. 
     As contemplated herein, various aspects and embodiments of the inventive subject matter can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment containing both hardware and software elements. In one embodiment, the inventive subject matter is implemented in software, which includes but is not limited to firmware, resident software, microcode, and other forms. 
     Furthermore, embodiments of the inventive subject matter can take the form of a computer program product accessible from a computer-usable or computer-readable medium providing program code for use by or in connection with a computer or any instruction execution system. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. 
     The medium can be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system (or apparatus or device) or a propagation medium. Examples of a computer-readable medium include a semiconductor or solid-state memory, magnetic tape, a removable computer diskette, a random access memory (RAM), a read-only memory (ROM), a rigid magnetic disk and an optical disk. Some examples of optical disks include compact disc-read only memory (CD-ROM), compact disc read/write (CD-R/W) and DVD. 
     A data processing system suitable for storing and/or executing program code will include at least one processor coupled directly or indirectly to memory elements through a system bus. The memory elements can include local memory employed during actual execution of the program code, bulk storage, and cache memories, which provide temporary storage of at least some program code in order to reduce the number of times code are retrieved from bulk storage during execution. 
     Input/output or I/O devices (including but not limited to keyboards, displays, pointing devices, touch screens, gesture recognition interfaces, smart phones, kiosks, RFID identifiers, smart cards, etc.) can be coupled to the system either directly or through intervening I/O controllers. 
     Network adapters may also be coupled to the system to enable the data processing system to become coupled to other data processing systems or remote printers or storage devices through intervening private or public networks. Modems, cable modem and Ethernet cards are just a few of the currently available types of network adapters. 
     Thus, specific compositions and methods of the computer-implemented method and system for recommendation system input management have been disclosed. It should be apparent, however, to those skilled in the art that many more modifications besides those already described are possible without departing from the inventive concepts herein. The inventive subject matter, therefore, is not to be restricted except in the spirit of the disclosure. 
     Moreover, in interpreting the disclosure, all terms should be interpreted in the broadest possible manner consistent with the context. In particular, the terms “comprises” and “comprising” should be interpreted as referring to elements, components, or steps in a non-exclusive manner, indicating that the referenced elements, components, or steps may be present, or utilized, or combined with other elements, components, or steps that are not expressly referenced.