Patent Publication Number: US-10783167-B1

Title: System for classifying items based on user interactions

Description:
BACKGROUND 
     To facilitate location of different types of items within an online marketplace or catalog, items may be classified using a structure of labels, such as a hierarchical tree. 
    
    
     
       BRIEF DESCRIPTION OF FIGURES 
       The detailed description is set forth with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different figures indicates similar or identical items or features. 
         FIG. 1A  depicts an implementation of a system that may be used to determine classification data to be used for classification of items. 
         FIG. 1B  depicts example user interfaces illustrating use of the system of  FIG. 1A . 
         FIG. 2  is a block diagram depicting example classification data that may be modified using example user interaction data. 
         FIG. 3  depicts a scenario illustrating a method for modifying classification data based on user interaction data. 
         FIG. 4  depicts a scenario illustrating a method for determining potentially inaccurate classification labels or items based on user interaction data. 
         FIG. 5  depicts a scenario illustrating a method for determining multiple sets of classification data based on user demographic characteristics. 
         FIG. 6  illustrates example classification tree structures associated with classification data and possible modifications to the tree structures. 
         FIG. 7  is a block diagram depicting an implementation of a computing device within the scope of the present disclosure. 
     
    
    
     While implementations are described in this disclosure by way of example, those skilled in the art will recognize that the implementations are not limited to the examples or figures described. It should be understood that the figures and detailed description thereto are not intended to limit implementations to the particular form disclosed but, on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope as defined by the appended claims. The headings used in this disclosure are for organizational purposes only and are not meant to be used to limit the scope of the description or the claims. As used throughout this application, the word “may” is used in a permissive sense (i.e., meaning having the potential to) rather than the mandatory sense (i.e., meaning must). Similarly, the words “include”, “including”, and “includes” mean “including, but not limited to”. 
     DETAILED DESCRIPTION 
     Online marketplaces and catalogs that offer a large number of items for purchase may sort the items into various categories to facilitate location of the items by users. In some cases, each category may include one or more sub-categories, which in turn may also include sub-categories, such that the items are classified using a hierarchical tree structure. For example a user searching for men&#39;s formal shoes may access a first node of a tree related to clothing and fashion, a second node related to men&#39;s clothing, a third node related to shoes, and a fourth node related to formal shoes. As another example, a user searching for women&#39;s boots may access the first node related to clothing and fashion, a second node related to women&#39;s clothing, a third node related to shoes, and a fourth node related to boots. In some cases, a user accessing the various labels of a classification tree may also supply a search query, which may be used to filter the labels and items presented to the user or to bypass certain labels to access sub-labels that correspond to the search query. 
     Due to the large quantity of items available through online marketplaces and similar entities, an online marketplace may use an automated process to classify and apply labels to newly-added items. For example, item data associated with a pair of men&#39;s running shoes may indicate characteristics that correspond to various hierarchical labels within a classification tree, such that the running shoes may be automatically classified within a node of the tree where other similar items are also classified. However, in some cases, a classification structure may include similar or redundant labels or hierarchies. For example, an online catalog may include running shoes classified within a first hierarchy of nodes—fashion, men&#39;s fashion, shoes, and athletic shoes—as well as a second hierarchy of nodes—sporting equipment, shoes, running shoes, and men&#39;s running shoes. Due to a complex hierarchy of redundant nodes, an automated classification process may inaccurately classify new items or classify new items in a confusing manner that generates an adverse customer experience. As another example, a classification structure may include very similar nodes, such as separate labels for toy action figures and bendable action figures. An automated classification process may inaccurately classify items using these labels due to the similarities in the item data associated with such items. 
     Described in this disclosure are techniques for modifying the classification structure into which new items are automatically classified based on user interactions, which may indicate the specific classification labels that are important or useful to customers, and the labels that are less useful, redundant, or erroneous. Classification data, used to automatically or manually classify items, may include various labels that may be associated with the items. For example, the classification data may include a hierarchical tree of labels. A parent label may indicate a broad category applicable to a large number of items, such as “Clothing”, while a child label associated with the parent label may include a sub-category of items, such as “Shoes”. Additional child labels may indicate more specific sub-categories, such as “Running Shoes”. To facilitate location of items, at least a subset of the labels may be accessible to users. For example, a user may be presented with a user interface displaying one or more parent labels. The user may select the label “clothing”, responsive to which various child labels associated with the clothing label may be output to the user. The user may then select the label “shoes”, responsive to which child labels associated with the shoes label may be output, and so forth. In some implementations, item data associated with one or more items associated with a selected label may also be presented. For example, after selecting the label “shoes”, a user may be presented with item data associated with a large number of items, each associated with the shoes label. Then, after selecting the label “running shoes”, the user may be presented with item data associated with a smaller number of items—the items associated with both the shoes and running shoes labels. 
     User interaction data associated with the labels may be used to determine the importance, accuracy, or utility of the labels. For example, the user interaction data for a particular label may indicate a count of instances that user input selecting that label was received. The user interaction data may also indicate counts of instances that user input selecting one or more parent or child labels associated with the particular label was received. In some implementations, the user interaction data may indicate a length of time during which one or more users interacted with the particular label, such as a dwell time or a viewing time during which a user interface associated with the particular label was displayed. In other implementations, the user interaction data may indicate a manner in which the particular label was accessed. For example, a user may access a particular label by first selecting a parent label, then the particular label. In other cases, the user may provide a search query responsive to which the particular label may be provided as one possible output. 
     The user interaction data may be used to determine an indication of importance or usefulness associated with at least a subset of the labels. For example, a large count of instances that a label was selected may indicate a high degree of importance or usefulness. Similarly, a large length of time spent by users accessing a label may indicate a high degree of importance or usefulness. Conversely, a small count of accesses or a short length of time spent accessing a label may indicate low importance or usefulness. In some implementations, the user interaction data may be used to generate an importance score for one or more of the labels. Labels that have an associated importance score greater than a threshold value may be included as classification data. The classification data may be used for automatic classification of subsequent items that are added to a marketplace or catalog. For example, item data associated with a newly-added item may indicate characteristics of the item. Based on correspondence between the item data and at least a subset of the labels included in the classification data, those labels may be applied to the item to classify the item. 
     In some implementations, the user interaction data may be used to add, remove, or reorganize labels within the classification data. For example, if a count of instances that users access a child label is within a threshold quantity or percentage of a count of instances that user&#39;s access a parent label, this may indicate that the child label is of greater importance the parent label. Continuing the example, if eighty percent of the users that access the “Shoes” parent label also access the “Running Shoes” child label, while a very small number of users access other child labels associated with the shoes parent label, this user interaction may indicate that the majority of users seeking to purchase shoes are primarily interested in running shoes, while the shoes parent label and other child labels associated with the shoes label are of lesser importance. Based on this determination, one or more of the other child labels may be removed from the classification data, or the shoes parent label may be removed from the classification data. In other cases, the classification data may be reorganized based on the relative importance of the labels. For example, the classification data may be modified such that the running shoes label is a parent label, while “other shoes” is a child label associated with the running shoes label. 
     In some implementations, modifications to the classification data may be suppressed from output to users. For example, the classification data may be used to automatically classify items by associating labels therewith, while users may view a fixed set of labels independent of the classification data. In such a case, the labels output to users may be mapped to different labels associated with the classification data. In other implementations, modifications to the classification data may be output to users. For example, as user interactions change over time, the labels that are presented for access by users may be modified. Continuing the example, during spring and summer months, labels associated with swimwear and summer clothing may receive a larger number of accesses than labels associated with winter clothing. Based on this user interaction data, users may be provided with a classification hierarchy in which labels associated with summer clothing are presented at higher levels in the classification hierarchy than labels associated with winter clothing. 
     In some implementations, user interaction data may be used to determine labels or items that include potentially inaccurate classifications. For example, if the length of time that users spend interacting with an output received responsive to selecting a label is less than a threshold value, this interaction may indicate that users who access the label are quickly navigating to other content rather than using the output. This interaction may occur if the items associated with a label are wrongly classified or if the content associated with the label is not useful to users for other reasons. Responsive to this user interaction, a notification associated with the label may be generated. For example, the notification may request human intervention by an administrator or other user associated with the classification data to determine whether items are improperly classified using the label or whether the label is potentially not useful for other reasons. The potentially erroneous label may be excluded from use classifying subsequent items until user input responsive to the notification is received. In some implementations, item data associated with the items classified using the label may also be accessed. Based on the item data, it may be determined whether the items associated with a particular label are homogeneous or heterogeneous. For example, if all items associated with the “shoes” label include shoes, the items would be homogeneous. However, if some of the items include socks, the items would be heterogeneous. The determination of heterogeneous items associated with labels where homogenous groups of items would be expected may also indicate that one or more items have been improperly classified, reducing the usefulness of the label to users. 
     In some implementations, user interaction data may be used to generate customized classification hierarchies for particular groups of users. For example, user interaction data may also include demographic data indicating characteristics of the users accessing particular labels. Continuing the example, demographic data may indicate that ninety-eight percent of users accessing a label associated with dishwasher drain plates are male, while female users that access this label do not interact with the resulting output for a significant length of time. Based on this data, the label associated with dishwasher drain plates may be determined to be of use or interest to male users, but of relative disinterest to female users. Subsequent male users that access the labels may be presented with a user interface that includes a label associated with dishwasher drain plates, while this particular label may be omitted from output when a female user accesses the labels. As another example, demographic data may indicate that users residing in Arizona during summer months consistently access labels associated with replacement parts for vehicle cooling systems, while users residing in Maine rarely access these labels. Based on this data, subsequent users that reside in Arizona may be presented with a user interface including such labels, while users in other regions may not. 
     By determining the particular classification labels that are useful or interesting to users, based on user interactions with the labels, the classification data used to apply labels to newly-added items may be streamlined. For example, by restricting the classification data to labels having a threshold importance score, determined based on user interactions, items may be automatically classified using labels that are more likely to be accessed by users, thereby providing a simplified user interface that improves user experiences and increase the likelihood that a user may locate and purchase items of interest. For example, a simplified user interface presented to a user may enable a user to simultaneously view a hierarchy of classification labels that, based on user interactions, have been determined to be of user or interest to a large number of users, alongside item data associated with items that pertain to selected classification labels. This may improve the ability of a computing device to present information and interact with a user by facilitating the ability of the user to quickly and accurately make purchasing decisions. Additionally, by excluding less useful, redundant, or erroneous labels from the classification data, the accuracy and efficiency of an automated classification process may be improved, enabling items to be classified using less processing time and other computing resources, improving the overall speed of the system. 
       FIG. 1A  depicts an implementation of a system  100  that may be used to determine classification data  102  to be used for classification of items. Classification data  102  may include a plurality of classification labels  104  which may be applied to items. For example, classification labels  104  may include alphanumeric descriptors, images, or other types of data that may be used to differentiate particular types of items from other types of items. Continuing the example, classification labels  104  may include labels such as “Clothing”, which may differentiate clothing-type items from other types of items, “Men&#39;s”, which may differentiate men&#39;s clothing items from women&#39;s or children&#39;s clothing items, “Shoes”, which may differentiate footwear from other types of clothing, and so forth. The classification labels  104  may include parent labels, indicative of categories of items, such as heterogeneous types of clothing, and child labels, indicative of sub-categories of items, such as shoes. Additional child labels may in turn be associated with the child labels, indicating additional categories of items, such as “Athletic” to differentiate athletic shoes from other types of shoes, such as outdoor shoes, and “Running” and “Cycling” to differentiate running shoes and cycling shoes from other types of athletic shoes.  FIG. 1A  also depicts example classification labels  104  such as “Outdoor”, to differentiate outdoor shoes from other types of shoes, and “Hiking” as one particular sub-category of outdoor shoes. 
       FIG. 1A  depicts example classification data  102 ( 1 ) that includes multiple classification labels  104  that may be part of a larger classification hierarchy. For example, while the example classification data  102 ( 1 ) includes eight classification labels  104  that relate to different types of clothing and shoes, other classification labels  104  may be associated with different types of shoes, different types of clothing, or different types of non-clothing items. The classification data  102 ( 1 ) may be used to classify items by applying particular classification labels  104  to particular items. For example, item data  106  indicative of characteristics of particular items may include an indication of one or more of the classification labels  104 . The classification data  102 ( 1 ) and item data  106  may be stored in association with one or more classification servers  108  or other types of computing devices. A user interface module  110  associated with the classification server(s)  108  may be used to generate a user interface  112  based on the classification data  102 ( 1 ) and the item data  106 . The user interface  112  may be provided to one or more user devices  114  for presentation. For example, the user interface  112  may present at least a portion of the classification labels  104  on a display or other type of output device. As user input is received, selecting or otherwise interacting with one or more of the classification labels  104 , additional classification labels  104  may be displayed. For example, the user interface  112  may present multiple parent labels, such as “Clothing” alongside other labels. Selection of the clothing label may cause child labels associated with the clothing label, such as “Men&#39;s”, to be displayed alongside other child labels associated with women&#39;s or children&#39;s clothing. Additional user input selecting labels may cause additional child labels to become accessible. 
     In some implementations, selection of one or more of the classification labels  104  may also cause item data  106  for the items associated with the selected classification label(s)  104  to be displayed. For example, selection of the “Running” label may cause item data  106  associated with different types of running shoes to be displayed in the user interface  112 . In some cases, the classification labels  104  may function as a filter. For example, selection of the “Shoes” label may cause item data  106  for a large number of items associated with this classification label  104  to be displayed. Selection of the “Running” label may cause one or more of the items to be removed from display, such that only the items associated with the running label are presented in the user interface  112 . 
     The classification server(s)  108  may determine user interactions  116 , such as user input and interactions with the user interface  112 . Specifically, a user interaction module  118  associated with the classification server(s)  108  may determine the user interactions  116  and store at least a portion of the user interactions  116  as user interaction data  120 . User interactions  116  may include selection, access, or other types of interaction with particular classification labels  104 . For example, the user interaction data  120  may indicate a number of times that one or more classification labels  104  were selected using a mouse device or other type of input device (e.g., “Clicks”). User interactions  116  may also include other indications of interactions with particular classification labels  104  such as the length of time that one or more users spend viewing or interacting with content presented in the user interface  112 . For example, the user interaction data  120  may include an average length of time (e.g., “Avg. Time”) spent by users that selected the “Running” label, during which the users viewed or otherwise interacted with item data  106  or other classification labels  104  presented in the user interface  112 . An average length of time may include a mean, mode, or median amount of time. For example, the average length of time may include a median time associated with the fiftieth percentile of users. Other user interactions  116  may include use of search queries or other features presented in the user interface  112 . For example, in some cases, a user may access a particular classification label  104  subsequent to providing a search query, while in other cases, the user may access the particular classification label  104  by first selecting one or more other classification labels  104  until the particular classification label  104  becomes accessible. 
     The user interaction data  120  may be used to determine particular classification labels  104  that are useful or interesting to users, and classification labels  104  that are not of use or interest. For example, classification labels  104  that are accessed a large number of times or that are accessed for a significant length of time may be of high use, while classification labels  104  that are accessed a small number of times or for a short length of time may be of low use. As another example, classification labels  104  that are accessed directly, such as by navigating the user interface  112  through selection of the classification labels  104  may be of greater use or interest than classification labels  104  associated with a search query. In some cases, the relationships between classification labels  104  may also be used to determine the usefulness thereof. For example, if a large portion of users that access a parent label then subsequently access a particular child label, within a short length of time, the parent label may be of low use or interest, while the child label is of higher use or interest. In some implementations, user interaction data  120  may also include an indication of times at which particular user interactions  116  occurred. For example, when determining whether a particular classification label  104  is of interest or use based on previous user interaction data  120 , user interactions  116  that occurred during a time period similar to the current time may have a greater weight than other user interactions. Continuing the example, due to seasonal variations in user interactions  116 , during summer months, user interaction data  120  associated with previous summer months may have a greater weight than user interaction data  120  associated with other time periods. 
     A classification module  122  associated with the classification server(s)  108  may modify the classification data  102 ( 1 ) based on the user interaction data  120 . For example, if the number of accesses for the “Running” child label is at least 85 percent as large as the number of accesses for the “Athletic” parent label, this may indicate that the athletic label is not useful to users, because the majority of users searching for athletic shoes are searching for running shoes. If the number of accesses for the “Running” label is at least 75 percent as large as the number of accesses for the “Shoes” parent label, this may indicate that a significant portion of the users searching for shoes are searching for running shoes. If the number of accesses for the “Cycling”, “Outdoor”, and “Hiking” labels are below a threshold value, this may indicate that these labels are of limited use or interest to users. In some implementations, the user interaction data  120  may be used to generate an importance score associated with each classification label  104 , and the importance score may be compared with a threshold score. In other implementations, specific elements of the user interaction data  120  may be compared with one or more threshold values for these elements. 
     Based on the user interaction data  120  and one or more threshold values, algorithms, importance scores, and so forth, the classification module  122  may generate a modified set of classification data  102 ( 2 ) based on the initial classification data  102 ( 1 ).  FIG. 1A  depicts the modified classification data  102 ( 2 ) including the labels, “Clothing”, “Men&#39;s”, “Running Shoes”, and “Other Shoes”. Use of the “Athletic” and “Shoes” labels, from the initial classification data  102 ( 1 ) may be eliminated due to count of accesses for the “Running” label exceeding one or more thresholds relative to the count of accesses for the athletic and shoes labels. Due to the small number of accesses for the “Cycling”, “Outdoor”, and “Hiking” labels, use of these classification labels  104  may be eliminated in favor of the “Other Shoes” label. 
     In some implementations, the modified classification data  102 ( 2 ) may be suppressed from output in a user interface  112 . For example, users may continue to view and access the “Cycling”, “Outdoor”, and “Hiking” labels, however, these labels may be mapped to the “Other Shoes” label of the classification data  102 ( 2 ). New items that are added to the online marketplace or catalog may be classified using the classification labels  104  or the classification data  102 ( 2 ), such that the modified classification data  102 ( 2 ), having a smaller number of classification labels  104 , expedites the classification process and improves the accuracy thereof. In other implementations, one or more portions of the modified classification data  102 ( 2 ) may be provided to the user interface  112 . For example, the classification labels  104  associated with the modified classification data  102 ( 2 ) may be output in place of the classification labels  104  associated with the initial classification data  102 ( 1 ). 
     As additional user interactions  116  associated with the classification labels  104  are determined and stored as user interaction data  120 , the classification module  122  may continue to modify the classification data  102 ( 2 ). For example, as user interactions  116  change seasonally, the user interaction data  120  may indicate larger or smaller numbers of accesses for particular classification labels  104 . Based on the user interaction data  120 , the classification module  122  may add, remove, or reorganize the classification labels  104 . 
       FIG. 1B  depicts example user interfaces  112  illustrating use of the system of  FIG. 1A . For example, a first user interface  112 ( 1 ) may include a first section including a first set of classification labels  104 ( 1 ) depicted adjacent to a second section depicting item data  106  associated with one or more items that correspond to selected labels from the set of classification labels  104 ( 1 ). For example, the item data  106  may include images  124  and descriptions  126  of various items associated with selected classification labels  104 . Continuing the example, a first user interface  112 ( 1 ) may include a large number of classification labels  104 ( 1 ) for selection by a user. Due to the large number of classification labels  104 ( 1 ), a user of an e-commerce system may experience difficulty locating a particular label of interest, or may be uncertain regarding which of the particular labels within the set of classification labels  104 ( 1 ) may include a particular type of item that the user is seeking. For example, a user intending to purchase a pair of men&#39;s running shoes may select a first classification label  104  associated with fashion-related items, a second classification label  104  associated with men&#39;s fashion items, a third classification label  104  associated with shoes, a fourth classification label associated with athletic shoes, and a fifth classification label associated with running shoes. The classification label  104  “Running” may in turn have multiple child labels associated therewith. Additionally, classification labels  104  relating to other types of athletic shoes may also possibly include items of interest to a user searching for running shoes, however the presence of classification labels  104  for four additional types of athletic shoes (e.g., “Team Sports”, “Cross-Training”, “Golf”, and “Cycling) may prevent efficient searching, by a user, of each category of items. 
       FIG. 1B  depicts a second user interface  112 ( 2 ) that may be presented after use of the system  100  of  FIG. 1A  to modify the classification data  102 . In the second user interface  112 ( 2 ), a second set of classification labels  104 ( 2 ) includes a smaller number of classification labels  104 , which may facilitate more efficient and accurate purchasing decisions by users. For example, when using the second user interface  112 ( 2 ) to access item data  106  associated with men&#39;s running shoes, a user may select classification labels  104  associated with fashion items, men&#39;s fashion items, and then running shoes. The smaller number of classification labels  104  within the second set may enable a user to more quickly and accurately locate items of interest, with less uncertainty regarding which classification labels  104  may apply to particular items. Additionally, a user may search for items of interest in other categories more efficiently by selecting from among the smaller number of other labels, such as the “Other Athletic Shoes” label. As such, generation of a user interface  112 ( 2 ) using streamlined classification data  102  may improve the ability of a computing device to display information and interact with a user by facilitating the ability of the user to quickly and accurately make purchasing decisions when contemplating an online purchase or rental of an item, or acquisition of media content. 
       FIG. 2  is a block diagram  200  depicting example classification data  102 ( 1 ) that may be modified using example user interaction data  120 . As discussed with regard to  FIG. 1A , classification data  102 ( 1 ) may include one or more classification labels  104 ( 1 ). Classification labels  104 ( 1 ) may include alphanumeric data, audio data, image data, or other types of data that may be associated with items to facilitate differentiation of particular types of items from other types of items. For example, classification labels  104 ( 1 ) may include text data that describes types of items, such as “Clothing”, “Electronics”, “Sporting Goods”, and so forth. In some implementations, classification labels  104 ( 1 ) may include metadata that may be determined by a computing device but may not necessarily be understandable to a human user. For example, the classification labels  104 ( 1 ) may include flags, labels, field information, and so forth that is not output for access by a user, but that may be used by a computing device to classify and differentiate items from other items. 
     The classification data  102 ( 1 ) may also include label relationships  202 ( 1 ). For example, one or more of the classification labels  104 ( 1 ) may be related to other classification labels  104 ( 1 ) by a parent, child, or sibling relationship. Continuing the example,  FIG. 1A  depicts classification labels  104  in which the label “Clothing” is a parent label with regard to the label “Men&#39;s”, which is in turn a parent label with regard to the label “Shoes”, and so forth. The labels “Running” and “Cycling” are child labels with regard to the label “Athletic”, and are sibling labels with regard to one another. 
     Other classification data  204 ( 1 ) may include metadata, such as a history of previous modifications to the classification data  102 ( 1 ) including, dates and times that such modifications occurred, particular users or user interaction data  120  that caused the modifications, and so forth. Other classification data  204 ( 1 ) may also include indications of items with which particular classification labels  104 ( 1 ) are associated, search queries or other user input that caused the output of particular classification labels  104 ( 1 ) and so forth. 
     As discussed with regard to  FIG. 1A , user interaction data  120  may include indications of user interactions  116  determined with regard to particular classification labels  104 . For example, while  FIG. 2  depicts a single set of user interaction data  120  that may be determined with regard to a particular classification label  104 , similar sets of user interaction data  120  may be determined for any number of other classification labels  104 . The user interaction data  120  may include a count of accesses  206  for a particular classification label  104 . An access may include a selection of the classification label  104 , such as by using a mouse device, touch sensor, keyboard, microphone, or other input device. In some implementations, an access may include use of a search query including terms that correspond to a particular classification label  104 . For example, a search query containing the term “running shoes” may be included as an access associated with the classification label  104  “Running Shoes”. Counts of accesses  206  for a particular classification label  104  may be used to determine a level of utility or interest associated with the particular classification label  104 . For example, if a classification label  104  is accessed a large number of times, the classification label  104  may be more useful than another classification label  104  that is accessed a small number of times. 
     The user interaction data  120  may also include indications of access times  208  associated with a particular classification label  104 . For example, when a user selects a particular classification label  104 , a user interface  112  may be provided that includes additional classification labels  104  for access, such as child labels associated with the particular classification label  104 . The user interface  112  may also include item data  106  indicative of one or more items associated with the particular classification label  104 . An access time  208  may include a length of time that the user interface  112  is displayed or otherwise output before a user accesses a subsequent classification label  104 , a portion of the item data  106 , another element of the user interface  112 , or until the user navigates away from the user interface  112  or discontinues the application that is displaying the user interface  112 . The access times  208  may include an average time determined for multipole users. For example, multiple users may access the “Running Shoes” classification label  104 , then browse the item data  106  associated with various running shoes items for an average time of 300 seconds. Access times  208  for a particular classification label  104  may be used to determine a level of utility or interest associated with the particular classification label  104 . For example, if users interact with the content associated with a classification label  104  for a large length of time, the classification label  104  may be more useful than another classification label  104  associated with smaller access times  208 . 
     User interaction data  120  may also include one or more counts of parent accesses  210 , which may include a count of instances that a parent label associated with the particular classification label  104  was accessed. The user interaction data  120  may further include one or more counts of child accesses  212 , which may include counts of instances that one or more child labels associated with the particular classification label  104  were accessed. The relationship between the count of accesses  206  for the particular classification label  104  and the accesses for parent and child labels may indicate the utility or interest associated with the particular classification label  104 , the parent label, and the child label(s). For example, if the count of child accesses  212  exceeds the count of accesses  206  for the particular classification label  104 , or is within a threshold quantity or percentage thereof, this may indicate that the majority of users that access the particular classification label  104  find greater utility or interest in the child label. 
     User interaction data  120  may further include counts of search query accesses  214  associated with a particular classification label  104 . In some cases, a classification label  104  may be accessed directly, such as by accessing an online marketplace or catalog and selecting the classification label  104 , or by selecting one or more other classification labels  104  which cause the particular classification label  104  to become accessible. In other cases, a classification label  104  may be accessed indirectly, such as by providing a search query having terms that may correspond to one or more classification labels  104 . Counts of search query accesses  214  for a particular classification label  104  may indicate a level of utility or interest associated with at least a portion of the counts of accesses  206 . For example, users that access a classification label  104  through a search query may be less interested in the particular classification label  104  than users that navigate to the classification label  104  by selecting links or other classification labels  104  within a user interface  112 . 
     Other interaction data  216  may include counts of accesses for sibling labels or other classification labels  104  within the classification data  102 . Other interaction data  216  may also include user data indicative of the user accounts accessing the classification labels  104  or demographic data indicative of characteristics of the users. For example, particular types of users may exhibit a tendency to access classification labels  104  in a particular manner, such as by using search queries, and to view content presented in a user interface  112  for a smaller length of time than other users. User data and demographic data may be used to modify the weight applied to particular accesses of a classification label  104  when determining the utility or interest associated with the classification label  104 . 
     The classification module  122  of the classification server(s)  108  may be used to modify the classification data  102 ( 1 ), to form modified classification data  102 ( 2 ), based on the user interaction data  120 . For example, the classification module  122  may include a scoring module  218 , which may determine an importance score  220  indicative of the utility or importance associated with a particular classification label  104 . The importance score  220  for a particular classification label  104  may be determined based on at least a portion of the user interaction data  120  for that classification label  104  and one or more scoring algorithms  222 . The scoring algorithms  222  may indicate various weights and relationships between one or more elements of the user interaction data  120  that may be used to determine the importance score  220 . For example, the scoring algorithms  222  may assign a weight of 10 to each access of the count of accesses  206  for a particular classification label  104  and a weight of 1 to each second of access time  208  associated with the particular label. The scoring algorithms  222  may further subtract a score of 5 for each count of search query accesses  214  for the particular classification label  104 . 
     A label determination module  224  associated with the classification module  122  may determine the modified classification data  102 ( 2 ) based on one or more of the importance score  220  or the user interaction data  120 . For example, the label determination module  224  may determine correspondence between the importance scores  220  for various classification labels  104  and one or more threshold values  226 . Classification labels  104  having an importance score  220  that exceeds a threshold value  226  may be included in the modified classification data  102 ( 2 ). In some implementations, the label determination module  224  may access one or more classification rules  228  that indicate the manner in which the classification data  102 ( 1 ) may be modified based on the user interaction data  120  and threshold values  226 . For example, in some cases, generation of an importance score  220  may be omitted, and the classification rules  228  may relate to the user interaction data  120  directly. For example, the classification rules  228  may indicate that classification labels  104  having a count of accesses  206  or access time  208  that exceeds a threshold value  226  are to be included in the modified classification data  102 ( 2 ), without calculating an importance score  220  using the scoring algorithms  222 . 
     As another example, the classification rules  228  may indicate that a classification label  104  is to be included in the modified classification data  102 ( 2 ) if the counts of accesses  206  for that classification label  104  exceeds a first threshold value  226 , unless the count of child accesses  212  for a child label associated with the classification label  104  exceeds a second threshold value  226  indicative of a percentage of the counts of accesses  206 . Continuing the example, the counts of accesses  206  for the classification label  104  “Athletic”, associated with athletic shoes may include 180 accesses, which exceeds a threshold value  226  of 150. However, the count of accesses  206  for the child label “Running” may include 160 accesses, which is greater than eighty percent of the count of accesses  206  for the “Athletic” label. In such a case, the classification rules  228  may indicate that the classification data  102 ( 1 ) is to be reorganized such that the “Running” label is a parent label with regard to the “Athletic” label. In other cases, the classification rules  228  may indicate that the “Athletic” label is to be excluded from the modified classification data  102 ( 2 ). In some implementations, the threshold values  226  may be determined based on user input or preexisting default values. In other implementations, the threshold values  226  may be determined, using machine learning techniques, based on changes in user interaction data  120  over time. For example, threshold values  226  may vary dynamically over time, based on median values (e.g., associated with the fiftieth percentile of users) determined from user interaction data  120  at particular times. Continuing the example, certain classification labels  104 , such as classification labels  104  associated with winter clothing, may be accessed more frequently during particular times of year, such as winter months, and infrequently during other months. Similarly, other classification labels  104  associated with other seasonal items may be accessed more and less frequently during particular times of year. 
     In some cases, user interaction data  120  for a newly-added classification label  104  may not exist, or insufficient user interaction data  120  may be present to determine an accurate importance score  220 . In some implementations, classification labels  104  that were added to the classification data  102  within a threshold amount of time, such as within the previous thirty days, may be included in classification data  102  and used to classify items independent of the user interaction data  120  associated with the new classification label(s)  104 . After the classification label  104  has existed for at least the threshold amount of time, the user interaction data  120  associated with that classification data  102  may be used to determine whether the classification label  104  may be subsequently included in or excluded from the classification data  102 . 
       FIG. 3  depicts a scenario  300  illustrating a method for modifying classification data  102  based on user interaction data  120 . At  302 , one or more classification labels  104  from initial classification data  102 ( 1 ) may be applied to items based on item data  106 . For example, one or more classification servers  108  may receive first item data  106 ( 1 ) associated with a new item to be added to an online marketplace or catalog. Continuing the example, the new item may be a type of running shoes, and the item data  106 ( 1 ) may be indicative of terms or characteristics associated with running shoes. The classification server(s)  108  may access initial classification data  102 ( 1 ) indicative of classification labels  104 , one or more of which may correspond to the item data  106 ( 1 ). For example, one or more classification labels  104  of the classification data  102 ( 1 ) may include terms indicative of running shoes, such as “Shoes”, “Athletic”, and “Running”. Based on the correspondence between at least a subset of the classification labels  104  and the item data  106 ( 1 ), the classification server(s)  108  may apply the subset of the classification labels  104  to the item data  106 ( 1 ), generating second item data  106 ( 2 ) that includes the classification labels  104 . For example,  FIG. 3  depicts three classification labels  104  that are applied to the item data  106 ( 1 ). Other classification labels  104  that do not correspond to the item data  106 ( 1 ), such as classification labels  104  associated with other types of shoes (e.g., “Cycling” and “Golf”), or other types of non-shoe or non-clothing items, may not be applied to the item data  106 ( 1 ) to generate the modified item data  106 ( 2 ). 
     At  304 , user interaction data  120  associated with the classification labels  104  may be determined. For example, a user interface  112  including at least a subset of the classification labels  104  may be provided to one or more user devices  114 . The user devices  114  may interact with the classification labels  104 , and user interactions  116  associated with these interactions may be determined. User interactions  116  may include, for example, user input selecting particular classification labels  104 , time spent viewing or otherwise interacting with content associated with particular classification labels  104 , and so forth. For example,  FIG. 3  depicts example user interaction data  120  that indicates a count of accesses  206  for each depicted classification label  104  of the classification data  102 ( 1 ) (e.g., “Clicks”), and a viewing time associated with each classification label  104  (e.g., “Time”). 
     At  306 , an indication of importance for the classification label(s)  104  may be determined based on the user interaction data  120 . For example, as described with regard to  FIGS. 1 and 2 , the classification server(s)  108  may generate one or more importance scores  220 , based on the user interaction data  120  and one or more scoring algorithms  222 . In some implementations, the importance scores  220  may include numerical values, as depicted in the example importance scores  220  of  FIG. 3 . In other implementations, importance scores  220  may include other types of text, Boolean results, and so forth. 
     At  308 , the classification data  102 ( 1 ) may be modified based on the indication(s) of importance. For example, the classification server(s)  108  may generate a modification determination  310  based on the importance sores  220 , one or more threshold values  226 , one or more classification rules  228 , and so forth. Continuing the example,  FIG. 3  depicts a modification determination  310  in which the importance scores  220  of the labels for “Cycling” and “Golf” are less than a threshold score, and therefore will be removed from the classification data  120  and consolidated into a general label. Additionally, in the depicted modification determination  310 , while the importance scores  220  for the “Shoes” and “Athletic” labels may exceed a threshold value  226 , the importance score  220  for the child label “Running” exceeds a threshold value  226  of at least 80% of the importance scores  220  for the “Shoes” and “Athletic” labels. Therefore, the classification data  102  may be organized such that the “Running” label is in a parent position rather than a child position. 
     At  312 , one or more classification labels  104  from the modified classification data  102 ( 2 ) may be applied to items based on item data  106 ( 3 ). For example, after modification of the classification data  102 ( 2 ), item data  106 ( 3 ) associated with a subsequent new item may be determined, such as a different pair of running shoes. The modified classification data  102 ( 2 ) may include the labels “Running Shoes” and “Other Shoes” as parent labels. Based on correspondence between the item data  106 ( 3 ) and the modified classification data  102 ( 2 ), only a single classification label  104  may correspond to the item data  106 ( 3 ), and thus, only that single classification label  104  would need to be applied to generate modified item data  106 ( 4 ). Modification of the classification data  102  may avoid use of redundant, extraneous, or non-useful classification labels  104  while prioritizing classification labels  104  that are of use or interest, based on user interaction data  120 . 
       FIG. 4  depicts a scenario  400  illustrating a method for determining potentially inaccurate classification labels  104  or items based on user interaction data  120 . At  402 , based on user interaction data  120  associated with a classification label  104 , it may be determined that a median (e.g., fiftieth percentile) time spent accessing the classification label  104  is less than a threshold value  226 . For example, the user interaction data  120  may indicate one or more access times  208  associated with a classification label  104 . The access time(s)  208  may include multiple individual access times  208  associated with different users, an average access time  208  (e.g., a mean, mode, or median) or total access time  208  determined based on the individual access times  208  of the users, and so forth. One or more threshold values  226  may include corresponding individual, average, or total threshold access times  208 . If users spend less than a threshold amount of time viewing or otherwise interacting with the content associated with a classification label  104 , this may indicate that the content is improperly classified or is not useful or interesting to users for other reasons. For example,  FIG. 4  depicts example user interaction data  120  with an access time  208  of  15  for the label “Cycling” (e.g., “Time: Cycling”), which is less than a threshold value  226  of  20 . Based on these values, the classification server(s)  108  may generate an accuracy determination  404  indicating that the “Cycling” label is potentially inaccurate, or that one or more items associated with the “Cycling” label are improperly classified or otherwise erroneous. 
     In some implementations, the classification server(s)  108  may also access item data  106  indicative of the items associated with the classification label  104 . The item data  106  may also be used to generate the accuracy determination  404 . For example, the item data  106  may indicate that the items associated with the classification label  104  are heterogeneous in nature, which may indicate that one or more items are improperly classified. Alternatively, the item data  106  may indicate that the items associated with the classification label  104  are homogeneous, which may indicate that the short access times  208  associated with the classification label  104  are occurring for reasons other than misclassified items. 
     At  406 , the classification server(s)  108  may generate a notification  408  indicating that the classification label  104  or one or more items associated with the classification label  104  are potentially misclassified. In some implementations, the notification  408  may also include an indication of one or more particular items associated with the classification label  104  that are potentially misclassified or otherwise not useful or interesting, based on user interaction data  120  and the item data  106  associated with the items. The notification  408  may be provided to an administrator device  410  or another computing device associated with a user, device, or process that may review or correct potentially inaccurate classification labels  104  or items. In other implementations, the notification  408  may be stored in association with the classification server(s)  108  for subsequent review by a human user or automated process. 
     At  412 , the classification label  104  may be excluded from use for subsequent classifying of items. For example, initial classification data  102 ( 1 ) may include the “Cycling” label as a child label associated with the “Athletic” label, which is in turn a child label associated with the “Shoes” parent label. However, based on the accuracy determination  404 , the “Cycling” label may be inaccurate or non-useful for other reasons. Because interaction with the “Cycling” label is not currently useful to users, the classification server(s)  108  may generate modified classification data  102 ( 2 ) that excludes the “Cycling” label. Subsequent items that would correspond to the “Cycling” label may instead be associated with only the “Shoes” and “Athletic” labels. 
     At  414 , user input responsive to the notification  408  may be received, that indicates that the classification label  104  is usable to classify items. For example, a user or automated process associated with the administrator device(s)  410  may review the classification label  104  or the item data  106  associated therewith and determine that no corrective action is required. As another example, the user or automated process may modify the classification labels  104  associated with one or more items, the manner in which items are classified using the classification label  104 , and so forth. Independent of the manner in which the potential issue associated with the notification  408  is addressed, a response  416  may be received by the classification server(s)  108  indicating that the potential issue has been addressed or resolved. In some implementations, item data  106  indicative of the items associated with the classification label  104  may be accessed to determine whether the items are generally homogeneous or heterogeneous in nature. For example, if an automated process determines that the items are homogeneous in nature, this may indicate that the items are not improperly classified, and the response  416  to the notification  408  may be provided. If an automated process determines that the items are heterogeneous in nature, the classification label  104  may be flagged for additional review, such as by a human user. 
     At  418 , based on the response  416 , the classification label  104  may be included in the classification data  102  for subsequent classifying of items. For example, the “Cycling” label may be added to the modified classification data  102 ( 2 ) to generate third classification data  102 ( 3 ), which includes the “Cycling” label. Subsequent items that correspond to the “Cycling” label may have that classification label  104  applied thereto for classifying the items. 
       FIG. 5  depicts a scenario  500  illustrating a method for determining multiple sets of classification data  102  based on user demographic characteristics. At  502 , first user interaction data  120 ( 1 ) associated with users having a first set of demographic characteristics and second user interaction data  120 ( 2 ) associated with users having a second set of demographic characteristics may be determined. For example, first demographic data  504 ( 1 ) may be determined from a first set of users and second demographic data  504 ( 2 ) may be determined from a second set of users. Each set of demographic data  504  may indicate one or more characteristics of the associated set of users. Demographic data  504  may be determined based on user interactions  116  associated with a classification label  104 , that may be attributed to user account. For example, when accessing an online marketplace or catalog, a user may log into a user account associated with various information, such as a user name, address, sex, date of birth, and so forth. In other cases, information regarding a user may be determined using characteristics of the user interaction  116 , such as an Internet protocol (IP) or other network address associated with the communication, one or more characteristics of the user device  114 , and so forth. For example, the geographic location of a user device  114  accessing an online marketplace or catalog may be determined based on the IP address associated with the user device  114 . 
     Demographic data  504  determined from user interactions  116  may be used to classify and group users based on various elements of the demographic data  504 . For example, users may be grouped based on sex, age, geographic location, or other factors. In some implementations, machine learning may be used to determine groupings and associations of users having common demographic characteristics and common user interactions  116  with regard to one or more classification labels  104 . For example,  FIG. 5  depicts example first demographic data  504 ( 1 ) associated with users residing in Phoenix, Ariz., and second demographic data  504 ( 2 ) associated with users residing in Boston, Mass. While the depicted example demographic data  504  identifies locations associated with one or more users, in other implementations, demographic data  504  may include a set of characteristics common to a group of users, such as age, sex, location, and so forth. First user interaction data  120 ( 1 ) associated with the first demographic data  504 ( 1 ) may indicate that users residing in Phoenix more frequently accessed classification labels  104  related to air conditioners, and less frequently accessed classification labels  104  related to dishwashers. Second user interaction data  120 ( 2 ) associated with the second demographic data  504 ( 2 ) may indicate that users residing in Boston more frequently accessed classification labels related to dishwashers and less frequently accessed classification labels related to air conditioners. 
     At  506 , first classification data  102 ( 1 ) based on the first user interaction data  120 ( 1 ) and second classification data  102 ( 2 ) based on the second user interaction data  120 ( 2 ) may be determined. For example,  FIGS. 1-3  describe example methods by which classification data  102  may be determined based on user interaction data  120 .  FIG. 5  depicts example first user interaction data  120 ( 1 ) in which three classification labels  104 , “Appliances”, “Air Conditioners”, and “Parts/Accessories” have received a substantial number of accesses, such as greater than 200 “Clicks”. Therefore, the three classification labels  104  may be included in the first classification data  102 ( 1 ). However, the counts of accesses  206  for the “Parts/Accessories” child label may be within a threshold quantity or percentage of the “Air Conditioners” parent label. Because the count of accesses  206  for the “Dishwashers” label is below a threshold quantity, the “Dishwashers” label may be excluded. Thus, the first classification data  102 ( 1 ) may include a hierarchy of classification labels  104  that includes “Appliances”, “Air Conditioners”, and “A/C Parts”. While the “Dishwashers” label may be excluded from the classification data  102 , subsequent items that may have been classified using this label may be classified using the “Appliances” label. 
       FIG. 5  also depicts example second user interaction data  120 ( 2 ), in which the “Appliances”, “Air Conditioners”, and “Dishwashers” labels received at least a threshold quantity of accesses, such as 200 “clicks”, while the “Parts/Accessories” label did not. The second classification data  102 ( 2 ) may therefore exclude the “Parts/Accessories” label while including the other classification labels  104 . 
     At  508 , correspondence between user data  510  for a subsequent user access and the first demographic data  504 ( 1 ) may be determined. For example, when a user device  114  subsequently accesses an online marketplace or catalog associated with the classification server(s)  108 , user data  510  associated with the user or the user device  114  may be determined. Continuing the example, the user data  510  may include user account information associated with the user or data associated with the user device  114 , such as an IP address. Based on the user data  510 , it may be determined that the user device  114  is located in Arizona, which corresponds to the first demographic data  504 ( 1 ). 
     At  512 , based on the correspondence between the user data  510  and first demographic data  504 ( 1 ), a user interface  112  based on the first classification data  102 ( 1 ) may be provided to the user device  114 . For example, responsive to the determination that a user device  114  is located in Arizona, a user interface  112  that includes accessible classification labels  104  based on the first classification data  102 ( 1 ) (e.g., “Appliances”, “Air Conditioners”, “Parts”) may be generated and provided to the user device  114 . While  FIG. 5  depicts two example sets of classification data  102 , any number of sets of classification data  102  may be determined for any number of user characteristics that may be determined using demographic data  504 . For example, different sets of customized classification trees that may be used to browse available items may be presented to different user devices  114  based on the demographic information associated with the user devices  114 . 
     While  FIG. 5  depicts an example user interface  112  that may include elements of classification data  102 ( 1 ), item data  106 , and so forth, in other implementations, a user interface  112  may include a series of prompts or questions without visually presenting the classification data  102 ( 1 ) to a user device  114 . For example, the user interface  112  may include a first question, such as “Are you interested in purchasing clothing”, and receipt of user input indicating an affirmative answer to this question may be stored as user interaction data  120  indicating a user interaction  116  with the classification label  104  for “Clothing”. Based on the user interaction  116  associated with the first question, a second question relating to a child label associated with the “Clothing” label may be presented. Additional questions may be presented based on the user input received responsive to preceding questions. 
       FIG. 6  illustrates example classification tree structures associated with classification data and possible modifications to the tree structures. Specifically,  FIG. 6  depicts a first classification tree structure  600  that includes fourteen example classification labels  104  and a second classification tree structure  602  generated by performing various modifications to the first classification tree structure  600 . 
     A first classification label  104 ( 1 ) and second classification label  104 ( 2 ) may include parent labels, having one or more child labels associated therewith. For example, the first classification label  104 ( 1 ) and second classification label  104 ( 2 ) may be associated with general categories of items, such as “Electronics” or “Clothing”. The third classification label  104 ( 3 ) and fourth classification label  104 ( 4 ) are child labels with regard to the first classification label  104 ( 1 ). The fifth classification label  104 ( 5 ) and sixth classification label  104 ( 6 ) are child labels with regard to the second classification label  104 ( 2 ). Each child label may be associated with a sub-category of items. For example, the third classification label  104 ( 3 ) may be associated with “Cameras”, while the fourth classification label  104 ( 4 ) is associated with “Cellular Telephones and Accessories”. Continuing the example, the fifth classification label  104 ( 5 ) may be associated with “Boots”, while the sixth classification label may be associated with “Sneakers”. 
     The seventh classification label  104 ( 7 ) and eighth classification label  104 ( 8 ) are child labels with regard to the third classification label  104 ( 3 ), and the ninth classification label  104 ( 9 ) and tenth classification label  104 ( 10 ) are child labels with regard to the fourth classification label  104 ( 4 ). The eleventh classification label  104 ( 11 ) and the twelfth classification label  104 ( 12 ) are child labels with regard to the fifth classification label  104 ( 5 ), and the thirteenth classification label  104 ( 13 ) and fourteenth classification label  104 ( 14 ) are child labels with regard to the sixth classification label  104 ( 6 ). Each of the seventh through fourteenth classification labels  104  may be associated with more specific categories of items, such as “Video Cameras”, “Wireless Cameras”, “Phone Cases”, “Phone Chargers”, “Hiking Boots”, “Fishing Boots”, “Running Shoes”, and “Cycling Shoes”. 
     In some cases, the classification data  102  may be modified in a manner that results in an elimination  604  of one or more classification labels  104 . For example, if a count of accesses  206  for a particular parent label is below a threshold value, or if the count of accesses  206  for one or more child labels associated with that parent label is within a threshold quantity or percentage of that parent label, the parent label may be of limited use to users. Continuing the example, the second classification tree structure  602  illustrates an elimination  604  of the third classification label  104 ( 3 ). Subsequent to this elimination  604 , the seventh classification label  104 ( 7 ) and eighth classification label  104 ( 8 ) are child labels with regard to the first classification label  104 ( 1 ). 
     In other cases, the classification data may be modified in a manner that results in a reorganization  606  of one or more classification labels  104 . For example, a count of accesses  206  for a particular child label may exceed or fall within a threshold quantity or percentage of the accesses associated with a parent label. This may indicate that the child label is of greater use or interest to users. As a result, the classification labels  104  may be reorganized such that the child label is a parent label with regard to the label that was previously a parent to the child label.  FIG. 6  illustrates a reorganization  606  in which the tenth classification label  104 ( 10 ), which was originally a child label with regard to the fourth classification label  104 ( 4 ), has been moved such that it is a parent label with regard to the fourth classification label  104 ( 4 ) and a child label with regard to the first classification label  104 ( 1 ). 
     In some cases, modification of the classification data  102  may include an addition  608  of one or more classification labels  104 . For example, the thirteenth classification label  104 ( 13 ) may relate to “Running Shoes” and the fourteenth classification label  104 ( 14 ) may relate to “Cycling Shoes”. However, a large number of search queries, item purchases, or other interactions with the sixth classification label  104 ( 6 ), relating to “Sneakers”, may indicate a user interest in fashion sneakers. Because items associated with the sixth classification label  104 ( 6 ) are generally heterogeneous in nature (e.g., including many types of sneakers), the classification data  102  may be modified to add an additional fifteenth classification label  104 ( 15 ) associated with “Fashion Sneakers”. The fifteenth classification label  104 ( 15 ) may be a child label with regard to the sixth classification label  104 ( 6 ). 
       FIG. 7  is a block diagram  700  depicting an implementation of a computing device  702  within the scope of the present disclosure. The computing device  702  may include any number of classification servers  108 , user devices  114 , administrator devices  410 , or any other types of computing devices  702  including, without limitation, laptop computers or other personal computers, smartphones or other mobile devices, set-top boxes, tablet computers, wearable computers, automotive computers, servers, and so forth. 
     One or more power supplies  704  may be configured to provide electrical power suitable for operating the components of the computing device  702 . In some implementations, the power supply  704  may include a rechargeable battery, fuel cell, photovoltaic cell, power conditioning circuitry, and so forth. 
     The computing device  702  may include one or more hardware processor(s)  706  (processors) configured to execute one or more stored instructions. The processor(s)  706  may include one or more cores. One or more clocks  708  may provide information indicative of date, time, ticks, and so forth. For example, the processor(s)  706  may use data from the clock  708  to generate a timestamp, trigger a preprogrammed action, and so forth. 
     The computing device  702  may include one or more communication interface(s)  710 , such as input/output (I/O) interface(s)  712 , network interface(s)  714 , and so forth. The communication interfaces  710  may enable the computing device  702 , or components of the computing device  702 , to communicate with other computing devices  702  or components thereof. The I/O interface(s)  712  may include interfaces such as Inter-Integrated Circuit (I2C), Serial Peripheral Interface bus (SPI), Universal Serial Bus (USB) as promulgated by the USB Implementers Forum, RS-232, and so forth. 
     The I/O interface(s)  712  may couple to one or more I/O device(s)  716 . The I/O devices  716  may include any manner of input device or output device associated with the computing device  702  or with another computing device  702  in communication therewith. For example, I/O devices  716  may include touch sensors, keyboards, mouse devices, microphones, image sensors (e.g., cameras), scanners, displays, speakers, haptic devices, printers, motion sensors, location sensors, and so forth. In some implementations, the I/O device(s)  716  may be physically incorporated with a computing device  702  or may be externally placed. 
     The network interface(s)  714  may be configured to provide communications between the computing device  702  and other devices, such as the I/O devices  716 , routers, access points, wireless communication devices  104 , destination devices  106 , and so forth. The network interface(s)  714  may include devices configured to couple to one or more networks, including local area networks (LANs), wireless LANs, wide area networks (WANs), wireless WANs, and so forth. For example, the network interfaces  714  may include computing devices  702  compatible with Ethernet, Wi-Fi, Wi-Fi Direct, Bluetooth, Bluetooth Low Energy, ZigBee, Z-Wave, 3G, 4G, LTE, and so forth. 
     The computing device  702  may include one or more busses or other internal communications hardware or software that allows for the transfer of data between the various modules and components of the computing device  702 . 
     As shown in  FIG. 7 , the computing device  702  may include one or more memories  718 . The memory  718  may include one or more computer-readable storage media (CRSM). The CRSM may be any one or more of an electronic storage medium, a magnetic storage medium, an optical storage medium, a quantum storage medium, a mechanical computer storage medium, and so forth. The memory  718  may provide storage of computer-readable instructions, data structures, program modules, and other data for the operation of the computing device  702 . A few example modules are shown stored in the memory  718 , although the same functionality may alternatively be implemented in hardware, firmware, or as a system on a chip (SoC). 
     The memory  718  may include one or more operating system (OS) modules  720 . The OS module  720  may be configured to manage hardware resource devices such as the I/O interfaces  712 , the network interfaces  714 , the I/O devices  716 , and to provide various services to applications or modules executing on the processors  706 . The OS module  720  may implement a variant of the FreeBSD operating system as promulgated by the FreeBSD Project; UNIX or a UNIX-like operating system; a variation of the Linux operating system as promulgated by Linus Torvalds; the Windows® operating system from Microsoft Corporation of Redmond, Wash., USA; or other operating systems. 
     A data store  722  and one or more of the following modules may also be stored in the memory  718 . The modules may be executed as foreground applications, background tasks, daemons, and so forth. The data store  722  may use a flat file, database, linked list, tree, executable code, script, or other data structure to store information. In some implementations, the data store  722  or a portion of the data store  722  may be distributed across one or more other devices including other computing devices  702 , network attached storage devices, and so forth. 
     A communication module  724  stored in the memory  718  may be configured to establish communications with user devices  114 , administrator devices  410 , or other computing devices  702 . 
     The memory  718  may also store the user interface module  110 . The user interface module  110  may be configured to determine a particular user interface  112  to provide to a user device  114  based on classification data  102 . For example, the classification data  102  may include multiple classification labels  104  and label relationships  202  indicative of parent, child, and sibling relationships between the classification labels  104 . Continuing the example, based on the classification labels and label relationships  202 , the user interface  112  may include one or more accessible parent labels that, when accessed, cause one or more child labels to become accessible, and so forth. In some implementations, item data  106  may also be included in the user interface  112 . For example, when a classification label  104  is accessed, item data  106  indicative of the items associated with that classification label  104  may be determined and provided in the user interface  112 . 
     In some implementations, the user interface module  110  may also be configured to determine user data  510  associated with a user device  114  or user interaction  116  and determine correspondence between the user data  510  and demographic data  504 . For example, one or more sets of classification data  102  may correspond to particular sets of demographic data  504 . Responsive to the determination of user data  510  that corresponds to particular demographic data  504 , a user interface  112  based on the classification data  102  associated with that demographic data  504  may be provided. 
     The memory  718  may also store the user interaction module  118 . The user interaction module  118  may be configured to determine user interactions  116  associated with the classification labels  104  presented in a user interface  112 . For example, the user interaction module  118  may determine each instance that a particular classification label  104  is selected. The user interaction module  118  may also determine the length of time that content associated with a particular classification label  104  has been viewed or otherwise accessible to a user device  114 . In some implementations, the user interaction module  118  may be configured to determine search queries responsive to which particular classification labels  104  or item data  106  are output. The user interaction module  118  may also be configured to determine label relationships  202  based on the classification data  102  and determine counts of parent accesses  210  and counts of child accesses  212  for one or more classification labels  104 . The user interaction module  118  may store at least a subset of the determined user interactions  116  as user interaction data  120 . 
     The memory  718  may additionally store the classification module  122 . The classification module may be configured to generate classification data  102  or modify existing classification data  102  based on user interaction data  120 . For example, the classification module  122  may determine correspondence between one or more elements of user interaction data  120  for a particular classification label  104  and one or more threshold values  226 . Based on the correspondence, the particular classification label  104  may be included in or excluded from the classification data  102 . One or more classification rules  228  may indicate the manner in which classification data  102  is to be generated or modified based on correspondence between the user interaction data  120  and the threshold values  226 . In some implementations, the classification module  122  may be configured to determine an importance score  220  for a particular classification label  104 , then determine correspondence between the importance score  220  and a threshold score. The importance score  220  may be based on the user interaction data  120  and one or more scoring algorithms  222 , which may associate various weights, constants, or other factors with elements of the user interaction data  120  to determine a numerical value or other type of value. 
     In some implementations, the classification module  122  may be configured to access demographic data  504  associated with one or more groups of users and determine particular user interaction data  120  associated with each set of demographic data  504 . The classification module  122  may then generate multiple sets of classification data  102  based on each set of user interaction data  120 . As discussed previously, based on user data  510  associated with a subsequent user device  114  or user interaction  116 , the user interface module  110  may then determine demographic data  504  that corresponds to the user data  510  and generate a user interface  112  based on the classification data  102  associated with the demographic data  504 . 
     Other modules  726  may also be present in the memory  718 . For example, encryption modules may be used to encrypt and decrypt communications between computing devices  702 . A user account module may be configured to determine user data  510  associated with various user devices  114  and store demographic data  504  determined from the user data  510 . Machine learning modules may be configured to determine relationships between the user interaction data  120  associated with particular elements of demographic data  504  or to determine one or more threshold values  226 . Data analysis modules may be configured to provide reports or other data indicative of at least a portion of the user interaction data  120  determined with regard to particular items or classification labels  104 . 
     Other data  728  within the data store  722  may include user input data, such as configurations and settings associated with computing devices  702 . Other data  728  may also include security data, such as encryption keys and schema, access credentials, and so forth. Other data  728  may additionally include one or more algorithms used to determine threshold values  226 . For example, threshold values  226  may vary dynamically as user interaction data  120  changes over time. Continuing the example, threshold values  226  may include median values based on user interaction data  120  for a particular time period. As another example, threshold values  226  may be determined based on user interaction data  120  determined for a historic time period that corresponds to the current time. Continuing the example, user interaction data  120  determined during a previous winter season may be used to determine a threshold value  226  for use during a current winter season. 
     In different implementations, different computing devices  702  may have different capabilities or capacities. For example, classification servers  108  may have significantly more processor  706  capability and memory  718  capacity compared to the processor  706  capability and memory  718  capacity of user devices  114 . 
     The processes discussed in this disclosure may be implemented in hardware, software, or a combination thereof. In the context of software, the described operations represent computer-executable instructions stored on one or more computer-readable storage media that, when executed by one or more hardware processors, perform the recited operations. Generally, computer-executable instructions include routines, programs, objects, components, data structures, and the like that perform particular functions or implement particular abstract data types. Those having ordinary skill in the art will readily recognize that certain steps or operations illustrated in the figures above may be eliminated, combined, or performed in an alternate order. Any steps or operations may be performed serially or in parallel. Furthermore, the order in which the operations are described is not intended to be construed as a limitation. 
     Embodiments may be provided as a software program or computer program product including a non-transitory computer-readable storage medium having stored thereon instructions (in compressed or uncompressed form) that may be used to program a computer (or other electronic device) to perform processes or methods described in this disclosure. The computer-readable storage medium may be one or more of an electronic storage medium, a magnetic storage medium, an optical storage medium, a quantum storage medium, and so forth. For example, the computer-readable storage media may include, but is not limited to, hard drives, floppy diskettes, optical disks, read-only memories (ROMs), random access memories (RAMs), erasable programmable ROMs (EPROMs), electrically erasable programmable ROMs (EEPROMs), flash memory, magnetic or optical cards, solid-state memory devices, or other types of physical media suitable for storing electronic instructions. Further, embodiments may also be provided as a computer program product including a transitory machine-readable signal (in compressed or uncompressed form). Examples of transitory machine-readable signals, whether modulated using a carrier or unmodulated, include, but are not limited to, signals that a computer system or machine hosting or running a computer program can be configured to access, including signals transferred by one or more networks. For example, the transitory machine-readable signal may comprise transmission of software by the Internet. 
     Separate instances of these programs can be executed on or distributed across any number of separate computer systems. Although certain steps have been described as being performed by certain devices, software programs, processes, or entities, this need not be the case, and a variety of alternative implementations will be understood by those having ordinary skill in the art. 
     Additionally, those having ordinary skill in the art will readily recognize that the techniques described above can be utilized in a variety of devices, environments, and situations. Although the subject matter has been described in language specific to structural features or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described. Rather, the specific features and acts are disclosed as exemplary forms of implementing the claims.