Abstract:
Described are methods to accommodate user interests in content available in information-rich software environments. As a dynamic process, user profiles are surreptitiously obtained by discerning and collating user interest based upon patterns of search and selection of displayed descriptor-based content. Based upon encapsulated user profiles, content characteristics of interest to users may be discerned, and content display altered to optimize individual user interest potential.

Description:
TECHNICAL FIELD 
     The invention relates to methods and apparatus for assessing displayed content on data processing systems, collating user interest in displayed content, and altering content to optimize user interest potential. 
     BACKGROUND OF THE INVENTION 
     Content-rich software environments face the daunting task of optimally organizing information of interest to individual users. The ideal solution would be self-organizing display based upon expressed user interest. Relying upon user surveys is impractical, as content may be too diverse or change frequently, and users are anyway often reluctant or unwilling to answer questionnaires for a variety of reasons, including tediousness and privacy concerns. There has been an unmet need to organize and alter display content for individuals so as to optimize user interest potential based upon surreptitious observation of patterns of expressed interest in content. 
     SUMMARY OF THE INVENTION 
     An essential aspect of this invention is providing descriptors of displayed content such that user exploration of content reveals interest in or avoidance of certain content. Another aspect is attribution of the relative display value of content. Another aspect is encapsulation of user interest and disinterest (valence) based upon selection or absence of selection of content. Another aspect is encapsulation of user valence in content based upon search nomenclature. Another aspect is indirect discernment of individual user valence for the separate characteristics of displayed content. Another aspect is grouping of users by characteristics of expressed interest, and aggregation of characteristics of interest to users. Another aspect are ways to arrange content display to optimize user interest and convenience based upon observed patterns. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     FIG. 1 is a block diagram of computers suitable for practicing the invention. 
     FIG. 2 depicts information sites. 
     FIG. 3 depicts aspects of a content package. 
     FIG. 4 depicts categorization. 
     FIG. 5 depicts an example display page template. 
     FIG. 6 depicts display page components. 
     FIG. 7 depicts display value factors. 
     FIG. 8 is a table of graphic color types. 
     FIGS. 9 a ,  9   b  depict quantitative methods for content package display value. 
     FIG. 10 depicts a user profile. 
     FIG. 11 depicts valence. 
     FIG. 12 depicts user profiling methods. 
     FIGS. 13 a ,  13   b  depict quantitative methods for valence. 
     FIG. 14 depicts quantitative methods for aggregation analyses. 
     FIG. 15 depicts an example of descriptors. 
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     FIG. 1 is a block diagram of a client computer  300  connected to a server computer  310  through a network  309 . A client computer  300 , more simply called a client or a computer, comprises at least a CPU  302 ; storage  303 , which comprises memory  304  and optionally one or more devices with retention medium(s)  305  such as hard disks, diskettes, compact disks, or tape; a display device  301 ; and one or more input devices  307 , such a keyboard and/or mouse. A display device  301  is capable of dynamically displaying different displays of information. The aggregate of information displayed on a display device is called display content  10 . As a client  300  in a network, a computer  300  also comprises a device for connection to a network  306 . A server computer  310 , more simply called a server, comprises at least a CPU  312 ; storage  313 , which may comprise memory  314 , and possibly one or more devices with retention medium(s)  315  such as hard disks, diskettes, compact disks, or tape; and a device for connection to a network  316 . Related to the invention, a client  300  primarily receives data. A user is a user of a client computer  300 . Related to the invention, a server  310  primarily transmits data to be received by one or more clients  300 . A network  309  may be any means by which one or more clients  300  are connected to one or more servers  310  for data transfer. A client-server environment is a setup whereupon one or more clients  300  are connected to one or more servers  310  through a network  309 . 
     FIG. 2 depicts an information site  40 . An information site  40  comprises a set of display pages  10 , including at least one initial display, called a home page  49 , and other pages (42-48) linked to each other (example internal link  51 ) and, directly or indirectly, to the home page  49 . A display page  10  is also called a multiple content display  10 , as each display page  10  displays multiple packages of content  11  that correspond to separate units of information. 
     A topically and geographically identifiable subset of content  11  on a display page  10  is called a content package  1 . As shown in FIG. 3, a content package  1  comprises two aspects: descriptor  2  and display  3 . 
     A content descriptor  2  is taxonomic data related to a content package  1 . It is essential for the invention that a content package  1  has descriptor data  2 . In the preferred embodiment, a descriptor  2  comprises categorization  4  and attributes  5 . Categorization  4  is an ordering of content. As shown in FIG. 4, categorization  4  may be hierarchical  30  or non-hierarchical  31 . Hierarchical categorization  30  may comprise categories  32  and subcategories  34 , while non-hierarchical categorization  31  has only atomic categories  35 . The lowest level category in hierarchical categorization  30  is an atomic category  36 . In the preferred embodiment, categorization  4  comprises at least some hierarchical categories  30 . In an alternative embodiment, categorization  4  may be entirely non-hierarchical  31 . Attributes  5  qualify categorization  4 . Attributes  5  may have their own categories. Attributes  5  may be atomized to possible options. Attributes  5  are nonexclusive, and the same attribute may apply to different categories. A content package  1  typically has several attributes  5 , sometimes even multiple options of the same attribute  5  (such as color). In an alternative embodiment, descriptor data  2  may lack categorization  4  or attributes  5 . 
     Content display  3  is a bounded visual display of information related to a content package  1 . A content display  3  may comprise text  6 , including title  8  and/or description  9 , and/or visual and/or audio elements  7 . Visual and/or audio elements  7  are for simplicity&#39;s sake referred to as image  7 . A simple content display  3  may be only a text title  6  or image  7 . 
     FIG. 14 is an example of descriptors  2  for jewelry  200 . Jewelry categories  201 , which are hierarchical  30 , may comprise earring  202 , bracelet  209 , necklace  210  and ring  211 . Earring subcategories  34  may comprise pierced  203  and clip  208 . Pierced earrings  203  may comprise hanging  204  and post  207  categories  34 . Hanging pierced earrings  204  may comprise French wire  205  and lever back  206  atomic categories  36 . Categories of jewelry attributes may comprise metal  213 , stone  226 , price  227 , brand  228  and ethnicity  229 . Attributes  5  may have categories, such as metal  213  in this example. Metals commonly used in jewelry include gold  214 , silver  224  and platinum  225 . Gold  214  is an attribute  5  unto itself, even though the characteristics of gold  214  may be further specified. For example, a consumer may prefer gold  214  to silver  224 , regardless of further specification. Gold  214  has two defining categories of attribute: color  220  and purity  214 . Common gold purities  215  comprise 24 karat  216 ,  18  karat  217 ,  14  karat  218 , and the least expensive commonly available in the U.S., 10 karat  219 . Gold colors  220  comprise yellow  222 , white  221  and rose  223 . In the figure, price  227  is shown as a single attribute, but, in the case of jewelry  200 , has distinguishable variants: high and low. Most often consumers have a preference for lower-priced goods and services of the same function, but certain goods such as jewelry also posses to some status value, or “snob appeal”, where consumers may actually prefer higher-priced items. In this instance there may be two atomized price attributes  5 : low price and high price. A user of a jewelry information site may express interest in high-priced or low-priced jewelry almost exclusively; in the invention, a user expressing interest  62  in both high- and low-priced jewelry effectively eliminates price  227  as a discerning attribute qualifying that user&#39;s interests. 
     Typically, a set of templates  55  are used for display pages  10  in an information site  40 . FIG. 5 depicts an example template  55 . A template  55  for a page  10  typically comprises at least some consistent content packages  11 . A menu  53 , for example, is typically in the same place, as users would find it disconcerting to see a menu  53  in different places on different pages  10 , or sometimes provide a menu  53  and sometimes not, though all pages  10  , typically pages  10  indirectly linked to the home page  49 , may not have a menu  53 . Tabs  52  have become a common way to provide quick access and delineate multiple home pages  49  in a site  40  that comprises different categories  4  of content  11 . Invisible placement guides, such as columns  57 , may be used in a template  55  to structure content package  1  display which varies on a per page  10  basis. Search capability  59  is common for information sites  40 , and a valuable assist in constructing a user profile  63 . 
     It is essential for the invention that at least one display page  10  in an information site  40  comprise multiple content packages  11  available for user selection. Content packages  11  on a page  10  are more simply called display content  11  or, most simply, content  11 .FIG. 6 depicts that the set of feature elements  9  overlaps the set of content packages  11 . Feature elements  9  may provide a venue for presentation of content packages  11 , but content packages  11  also incorporate feature elements  9 . In other words, a content package  1  may both have feature elements  9  and may be given heightened or lessened display value  33  by feature elements  9  external to the content package  1 . For example, a menu item  54 , as depicted in FIG. 4, may be a relatively nondescript text title  8 , but is given augmented display value  33  by virtue of incorporation into a menu  53 . 
     FIG. 7 depicts factors of display value  15 . Content package display value  33  is a relative measure of visual prominence of a content package  1  on a display page  10 . High display value is visible prominence, while low display value is obscurity. Display value  33  is also called scoop  33  (because it catches the eye). The factors determining display value  15  comprise layout  16  and vividness  17 , which collectively constitute feature elements  9 . Layout  16  is overall arrangement of content packages  11 . Aspects of layout  16  include location  18 , size  19  and grouping  20 . Location  18  is the relative location on a page  10 . Size  19  is the relative area of a content package  1  on a page  10 . Grouping  20  relates to visible organization of content packages  11 . Vividness  17  includes delineation  21 , color  22  and shape  23 . Delineation  21  refers to whether and how a content package  1  is differentiated from others around it  11 . Color  25  comprises hue  26 , saturation  27  and value  28 . Shape  21  is the regularity of a content package  1 . Illustrative factor display value indicators  37  are also shown in FIG.  7 . Specific factor display values  37  may be affected by cultural and individual preferences, and especially usage in context. 
     There are consistencies in calculations related to scoop  33  used in the preferred embodiment. First, a scale used in measurement should be consistent throughout a site  40 , and must be consistent within the scope of equivalent measurements, but the scale employed is otherwise optional, as relative, not absolute, derived values are used. Second, weights are used in calculations to adjust relative factors. Third, alternative embodiments may employ different factors or scales or values or steps or calculation. 
     Content package scoop  33  is a composite of display value factors  15 . The invention employs a quantitative method to ascribe content package scoop  33 . The preferred embodiment uses, where convenient, direct display value factors  15  or, if direct measurement is not readily available, quantifiable surrogates of display value factors  15 ; alternative embodiments may do likewise. In the preferred embodiment, for example, shape  23  is not directly incorporated in calculating scoop  33 . 
     Nominal scoop  333  equals layout  16  plus vividness  17 . Layout  16  equals location  18  plus size  19 . Vividness  17  equals delineation  21  plus graphic intensity  120  plus animation  121 . 
     In the preferred embodiment, obscurity distance  122  for a content package  1  is the distance from the bottom right corner of a display page  10  to the bottom right corner of the content package  1 . The preferred embodiment assumes the top-left corner of a page is the best page location, all other display value factors  15  being constant. An alternative embodiment may make a different assumption about best page location, and adjust obscurity distance  122  measurement accordingly. Location factor  123  is obscurity distance  122  divided by the highest obscurity distance  124  for content on the page  11 . The content package  1  with the best location would have the highest obscurity distance  124 . Relative location  125  is location factor  123  divided by the sum of all location factors  126 . Location  18  is location factor  123  times location weight  127 . 
     The bounding area  128  for a content package  1  is its width  129  times height  130 . Size factor  131  is bounding area  128  divided by the largest bounding area of content packages on the page  132 . Relative size  133  is size factor  131  divided by the sum of all size factors  134 . Size  19  is relative size  133  times size weight  135 . 
     Grouping  20  is a measure of visual prominence based upon a content package  1  being incorporated into an overarching visual structure. Grouping  20  is one if an item is grouped, zero if not. In the preferred embodiment, if a content package  1  is a menu item  54  or a tab  52 , grouped  136  is one; otherwise zero. An alternative embodiment may have another grouping mechanism that should be considered to set grouping  20 . While grouping is a layout factor  16 , it also provides delineation  21 . In the preferred embodiment, grouping  20  is incorporated into quantitative calculation of delineation  21 . In an alternative embodiment, grouping  20  may be factored into scoop  33  differently. 
     Delineated  136  equals grouping  20  plus headlining  137  plus bordered  138 . An alternative embodiment may include underlining or other delineation means in calculating delineated  136 . If the content package  1  has a headline, the value of headlined  137  is one; otherwise zero. If the content package  1  has a bordered  138  around it, the value of bordered  138  is one; otherwise zero. Relative delineation  139  is delineated  136  divided by the sum of all delineated  140 . Delineation  21  is relative delineation  139  times delineation weight  141 . 
     Graphic intensity  120  as a factor of vividness  17  is conceptually encapsulated in the preferred embodiment as a product of the area of content package display graphic image(s)  153  times the average color intensity of the graphic(s)  175 . The preferred embodiment uses the bounding boxes of graphics in a content package  1  as a measure of graphic area  153 . The graphic size for a single graphic image  150  is its graphic width  151  times graphic height  152 . Graphic area  153  is the sum of the graphic sizes of all single graphics  150  in a content package  1 . It is a common technique to create non-rectangular graphics using contiguous multiple rectangular graphics. Graphic factor  155  is graphic area  153  divided by the largest graphic area of content packages on the page  156 . Relative graphic  157  is graphic factor  155  divided by the sum of all graphic factors  158 . Graphic  159  is relative graphic  157  times graphic weight  170 . 
     Color  22  is a significant display value factor  15 . Relative color intensity of graphics on a page  10  adds another dimension to quantification of vividness  17 . Among hue  24 , saturation  25  and value  26 , value  26  is most significant, followed by saturation  25 , then hue  24 . For hue  24 , high to low value generally ranges from warm colors to cool colors (yellow &amp; red to green &amp; blue). For saturation  25  and value  26 , display value  33  positively correlates to saturation  25  and value  26  values. Color display value analysis for content  11  with graphic images is done for each color factor  17  using a consistent scale. In the preferred embodiment, an input color value  262  quantifies color value  172  for the graphic content of a content package  159 . FIG. 8 gives the preferred embodiment scale for color input value  262 . For a content package  1  with multiple graphics, color value  172  may be a weighted average (by area) of color values for each single graphic  150 . If graphic intensity would differ greatly between images within a content package  1 , separate graphic intensity analysis may be performed for separate graphics as appropriate and summed for content package graphic intensity  120 . 
     In an alternative embodiment, cumulative pixel by pixel scan analysis of bitmap graphics may be the basis for automated quantitative measure of color value  172 . In this alternative embodiment, pixel color intensity is a product of banded trade-off scales of color saturation  25  and value  26  times hue  24 , where hue  24  is quantified by relative hue display value. Color dispersion of a graphic is factored in as a measure of contrast (contrast being an aspect of delineation  21  that is scoop  33  enhancing) to quantify color value  172 . 
     Color factor  171  is color value  172  divided by the highest color value of content packages on the page  173 . Relative color  173  is color factor  171  divided by the sum of all color factors  174 . Color intensity  175  is relative color  173  times color weight  176 . 
     Graphic product  177  is graphic times color intensity  175 . Graphic intensity  120  is graphic product  177  times graphic product weight  178 . 
     Though not in the preferred embodiment, graphic intensity analysis may also be appropriate for vivid headline text. Such headline text is typically more vivid low scoop backgrounds, but less vivid than high scoop images. As an extension of this idea, in an alternative embodiment, an entire content package  1  may be subject to vividness  17  analysis using the methods described. 
     Animation  121  by itself yields significantly enhanced display value  33 . Animated  161  is one if an item is animated, 0 (zero) if not. Relative animation  160  is animated  161  divided by the sum of all animated  162 . Animation  121  is relative animation  160  times animation weight  164 . Animation  121  may not be germane for some sites  40 . 
     For optional feature elements  9 , the method of calculation in the preferred embodiment using relative factors and measures quantifies the concept of relative distinction. For example, the value of animation  121  as a feature element  9  distinguishing a content package  1  from others is diminished as more content packages on a page  11  have animation. 
     Normalized scoop  33  is a variant of nominal scoop  333 , mathematically convenient because the range of normalized scoop  33  is zero to one. Scoop  33  is derived by dividing nominal scoop  333  by the highest scoop on the page  163 . 
     Scoop for a given page  110  with the same feature elements  9  may be considered constant. Typically some feature elements  9  on a page  10  vary, even though the same template  55  may be used. This is because a content package  1  may have its own feature elements  9 , hence may have a unique scoop  33 . For approximation purposes, scoop for a given template  110  may be considered constant unless the scoops  33  of content packages  11  in the same location  18  vary significantly. 
     A display page  10  may have one or more content packages  11  with an external link  65 , that is, a link  65  to a page  58  on an external site  50 . Advertising for other sites  50  is an example. If a content package  1  is identifiably exogenous, it may be disregarded it for page scoop analysis  110 . Alternately, a content package  1  with an external link  65  may be incorporated if descriptors  2  exist that allow profile analysis, and that content package  1  is considered relevant. 
     Individual user profiles  63  are essential to the invention. A user profile  63 , depicted in FIG. 10, encapsulates a set of valences  66  to descriptors  64  (categories  68  and attributes  69 ) for a particular user. User profile descriptors  64  map to content package descriptors  2 . Valence  66 , also depicted in FIG. 11, is a measure of user interest  62  and possibly disinterest  61  to content descriptors  2 . In the preferred embodiment, valence  66  is a quantitative measure to each tracked descriptor  64 , but in a simpler alternative embodiment, valence  66  may be indicated by the presence of tracked descriptors  64  without quantitative measure. In the preferred embodiment, valence  66  may be positive or negative: positive valence is interest  62 , where higher (more positive) valence is stronger appeal; negative valence is disinterest  61 , where lower (more negative) valence is stronger aversion. Zero valence  60  is non-interest or unexpressed interest. In the preferred embodiment, disinterest  61  is tracked in a user profile  63 . In an alternative embodiment, only interest  62  may be tracked. 
     A user profile  63  may be stored in whole or part on a client  300  or server  310 , depending upon the embodiment. User identification may transpire using stored data on a client  300 , such as a cookie commonly used with Internet web site browsers, or through account authorization using a client  300  or server  310  based program. 
     As depicted in FIG. 12, a user profile  63  may be discovered through two methods  77 : direct  71  and indirect  76 . Direct profiling  71  is directly asking a user for interests  62 , and possibly disinterests  61 . A survey  73  or questionnaire is a direct profiling method  71 , where a user registers valence  66  directly, comprising interests  62  and possibly disinterests  61 . Indirect profiling  76  is incrementally discovering valence  66  through a user&#39;s actions. With multiple content packages  11  on a display page  10  available for selection  74 , a user reveals interests  62  through selection  74 , and by consistency through time, disinterests  61  by lack of selection  78 . Accumulating such choices is an indirect profiling method  76 . Both selection  74  and absence of selection  78  reveal interest  62  and disinterest  61  respectively, so in the preferred embodiment a user profile  63  is constructed by tracking both selection  74  and absence of selection  78 . Using indirect profiling  76  in the preferred embodiment, from an starting point of unexpressed interest (zero)  60  for any descriptor  64 , every time a user makes a content package selection  74 , the valence  66  of descriptors  64  in that user&#39;s profile  63  that apply to the content package selected  74  are credited, and valence  66  of descriptors  64  not selected  78  are debited. An alternative embodiment may only track selection  74 , and thus only affirmative selections  74  or choices are credited. Using direct profiling  71  in the preferred embodiment, the valence  66  of descriptors  64  in a user profile  63  that apply to an affirmative choice  74  are credited, and valence  66  of alternate descriptors  64  not chosen  78  are debited. The quantitative methods used with indirect profiling  76  in the preferred embodiment to alter descriptor valence  66  are given in FIGS. 13 a  and  13   b , and described below. Different embodiments may use different factors or steps to profile  77 . 
     Besides its utility to users, search  72  within an information site  40  is a coveted asset in constructing a user profile  63 , as a user reveals interest  62  directly. In the preferred embodiment, incremental valence  66  for search  72  is a product of search value  80  times search discount factor  81 . If a search term does not exactly match the descriptor  64 , a search term may be associated with a descriptor  64  using textual matching or a dictionary of associative words to descriptors  64  (textual fuzzy logic). If the search term / descriptor  64  match is exact, the search term discount factor  81  is one; if the match is not exact, the search term discount factor  81  may be set by default to 0.5 (one-half), or if fuzzy logic is used, a fractional surety measure of correctly associating the search term to a descriptor  64  may be used as search term discount factor  81 . 
     Proximity  93  is a measure of closeness of content package descriptors  2  on a display page  10 . Uniqueness is the inverse of proximity  93 . The greater the number of content packages  11  with overlap of descriptors  2 , the higher the proximity  93 ; the less overlap, the more unique. Proximity  93  (or uniqueness) provides a basis to measure discrimination  82 . Discrimination  82  is a measure of user selection in relative proximity  93 . In other words, discrimination  82  is a measure of user choosiness. There are a few steps in the preferred method to measure discrimination  82  for a descriptor  2  in a selected content package  87 . A descriptor&#39;s uniqueness  88  may be measured by dividing the number of content packages on the page not having the target descriptor  89  by the number of content packages on a page go. A content package&#39;s uniqueness sum  91  may be measured by cumulative summation of descriptor uniqueness  88  for each descriptor  2  applying to that content package  92 . Proximity  93 , which is relative to other content packages  11  on a page  10 , is the content package&#39;s uniqueness sum  91  divided by the number of content packages on the page go. Discrimination  82  is proximity  93  times discrimination weight  94 . 
     Abstinence  85  is a measure of relative user apathy, the flip side of discrimination  82 , and is calculated similarly to discrimination  82 . Abstinence  85  applies only to those descriptors not selected  97 ; descriptors selected  87  are not used in calculation of abstinence  85 . Abstinence uniqueness sum  101  is the sum of descriptor uniqueness  88  for each descriptor not selected  102 . Relative abstinence  103  is the abstinence uniqueness sum  101  divided by the number of content packages on the page go. Like proximity  93 , relative abstinence  103  is a relative measure. Abstinence  85  is relative abstinence  103  times the abstinence weight  104 . 
     In calculating discrimination  82  and abstinence  85 , the preferred embodiment is to use only the lowest level of categorization  4  that applies to the target content package  1 . This precludes categorization  4  from being too heavily weighted in calculation relative to attributes  5 . In the preferred embodiment, for selection  74  related to a hierarchical categorization  30 , once the incremental valence  66  for a subcategory  34  descriptor  64  has been calculated, its next higher category  32  may be credited with one-half the incremental valence  66  related to discrimination  82  or abstinence  85 . One implication is that user profile descriptors  64  map to content package descriptors  2  accordingly. 
     Decisiveness is a measure of quickness in selection  74 , how well a user knows what s/he wants. Vacillation signifies browsing without strong interest, while decisiveness signifies intention. One way to measure decisiveness is to measure duration between page display and user selection  74 , and apply a metric based upon the complexity of a page  10 , using the number of content packages go as indicative of complexity. Quantitatively, decisiveness is of minor significance, but may be revealing. Like discrimination  82 , decisiveness is an augmentative factor. Decisiveness is not used in the preferred embodiment, but may be accounted for in an alternative embodiment. 
     Consumption  84  may signal different forms of success for different types of information sites  40 . For a sales site, consumption  84  may mean purchasing merchandise or a service represented by a content package  1 . For a sales site, in the preferred embodiment, consumption  84  is achieved if a purchase decision is positively indicated, even if that decision is later rescinded. After all, a high level of interest  62  was indicated, even if a trade-off was later made that eliminated or deferred purchase. For a non-sale site, consumption may mean reading (or viewing or hearing) a page  10  displayed as a result of selecting  74  a content package  1 . Consumption  84  applies to the descriptors  2  of the content package  1  selected  74  that allow consumption  84 . The meaning and measure of consumption  84  for a non-sales site may differ for different media being consumed: one metric for readable print or graphics, another for videos, another for audio, though all are accounted in time measurement. To measure consumption  84  for a readable page on a non-sales site in the preferred embodiment, first measure witness  95 , a measure of how much time a user spent on the page generated by content package selection  74  (on the previous page). In the preferred embodiment, measured witness  96  is the duration that a display page  10  is topmost (in other words, consumable). Set Witness  95  to the larger of the measured witness  96  or consumption requirement  98 . Divide witness  95  by consumption requirement  98 , which is the duration required to consume the information on the selected page, to get the consumption percentile  99 . Multiply the consumption percentile  99  by consumption weight  100  to yield consumption  84 . To measure consumption  84  for a video or audio, multiply the consumption weight  100  by consumption percentile  99 , which in this case is equivalent to the percent watched or listened to respectively. 
     Decisiveness and consumption  84  are reliant upon time measurements that may not be readily available in some embodiments. While decisiveness is not a particularly significant metric, consumption  84  is very significant. As a work-around to quantify consumption  84  if duration measurement as described is not readily available, use the duration between generation of the consumable page and a change in the topmost page (if available), or generation of the succeeding page (if change of page is not available) as measured witness  96 . Decisiveness may be measured similarly. In an alternative embodiment, where measured witness  96  is impractical, consumption may be assumed (set witness  95  to consumption requirement  98 ) if selection  74  is made. 
     For indirect profiling  76 , scoop  33  is a discount factor for a content package  1 . It&#39;s relatively easy to see a content package  1  with high display value, harder to find an obscure one. Accordingly, selection  74  of a relatively obscure content package may signify more genuine interest  62 , and perhaps the converse. Distraction  83  is a measure of the influence of scoop  33  in prompting user selection  74  of a content package  1 . Page scoop  110  is the sum of all content packages&#39; scoops  33  on a page  10 . Relative scoop  99  is the target content package&#39;s scoop  33  divided by page scoop  110 . Distraction  83  is relative scoop 105 times distraction weight  107 . 
     Descriptor valence values  66  may cumulatively reach considerable disparity between interest  62  and disinterest  61 . In fact, to avoid potential user confusion, changes in display are not recommended until a threshold of disparity is reached. Variance of valence  66  may be adjusted using a smoothing function to keep valence values  66  within a specified range. 
     This invention facilitates valuating valance  66  to descriptors  2 / 64  and content  1 . Descriptor valence aggregation  111  is the net summation of users&#39; valence  66  to a single content descriptor  2 . Descriptor valence aggregation  111  is calculated by summing valence  66  for a descriptor  2  from each user profile  63 . Content valence aggregation  113  is the net summation of descriptor valence aggregation  111  for each descriptor  2  in a real or hypothetical content package  1 . Content valence aggregation  113  yields statistical insight into which offered content achieves significant user valence  66 , and may provide factor analysis about which content descriptors  2  have particular valence  66 . Content valence aggregation  113  provides a statistical decision base for shifting content mix  11  on a site  40 . Content valence aggregation  113  also provides a statistical decision base upon which new content  11  with favorable characteristics may be envisioned. 
     This invention facilitates categorizing users based upon valence  66  to content  11 . User aggregation analysis flips valence aggregation analysis on its head by looking at which users are interested in particular content rather than which particular content interests users. Descriptor user aggregation  115  is compiling list of users with valence  66  to a single content descriptor  2 . Using descriptor user aggregation  115  as a springboard, content user aggregation  117  is compiling a list of users with valence  66  for each descriptor  2  in a real or hypothetical content package  1 . A cutoff threshold level of valence  66  may be specified for user aggregation analysis so that users with marginal valence  66  are not inappropriately corralled into a grouping. This applies to descriptor aggregation  115  and content user aggregation  117 . For descriptor user aggregation  115 , threshold cutoff applies to a user&#39;s valence  66  for a particular descriptor  2 . For content user aggregation  117 , threshold cutoff may be applied in a variety of ways, including weighting of single descriptors  2  based upon an assigned descriptor ordinal, or mixed weighting of multiple descriptors  2 . In one embodiment, descriptor aggregation  115  provides sufficient threshold cutoff for content user aggregation  117 . 
     In the preferred embodiment, both interest  62  and disinterest  61  are considered in the aggregation analyses described above. In alternative embodiments, only interest  62  or disinterest  61  may be considered. 
     The mutual exclusivity of descriptors  2  is an issue in the aggregation analyses described above. In the preferred embodiment, categorization  4  provides the basis for aggregation analyses. Attributes  5  are modifiers to categories  4 , and cross category boundaries, but in context of categorization  4  may provide additional specification resolution, so an alternative embodiment may consider attributes  5  as well as categorization  4 , in which case attributes  5  may be subsumed in the category  4  to which they apply. The descriptors  2  that serve as a basis for analysis should be mutually exclusive so as to avoid duplication: duplicate interest  62  accreditation or disinterest  61  debiting. If overlap exists in categorization  4 , the overlap may be accounted for using statistical factoring and weighting. The level of hierarchical categorization  30  examined may vary depending upon the degree of resolution required for the analysis (assuming hierarchical categorization  30  is used). 
     This invention provides the basis to alter page  10  content and display based upon valence  66  to descriptors  2 . Suggested content display changes based upon valence  66  include arranging menu items  54  and tabs  52  such that items of interest  62  are at optimal location  18 ; altering layout  16  of content packages  11  and otherwise changing scoop  33 ; and altering content  11  on a page  10 . The nominal approach may be to elevate scoop  33  of content  11  with expressed interest  62  and obfuscate avoided  61  content, but a temporary ‘testing’ strategy may at times be employed to validate previously disclosed valence  66 : enhance scoop  33  of disinterested  61  content  11  while obfuscating content  11  of interest  62 . Altering layout  16  or content  11  may be expeditiously accomplished by using templates  55 . The purpose of the invention is to guide organization and display of content  11  to optimize a user&#39;s utility of an information site  40 , not preclude choice.