Patent Publication Number: US-2017357698-A1

Title: Navigating an electronic item database via user intention

Description:
BACKGROUND 
     Typically, users may search through an electronic item database by specifying a search query formed of keywords. The search engine will then locate items that match the search query in some respect, e.g., the keywords may be contained in an item title or description. The items returned by the search engine may be ranked based on relevance to the search query. For example, an item matching the keywords in the title may be a better match than an item merely matching the keywords in the description. Similarly, an item having more occurrences of the keywords in the description may be ranked higher than an item with fewer occurrences of the keywords. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Many aspects of the present disclosure can be better understood with reference to the following drawings. The components in the drawings are not necessarily to scale, with emphasis instead being placed upon clearly illustrating the principles of the disclosure. Moreover, in the drawings, like reference numerals designate corresponding parts throughout the several views. 
         FIGS. 1A and 1B  are pictorial diagrams of example user interfaces according to various embodiments. 
         FIG. 2  is a schematic block diagram of a networked environment according to various embodiments of the present disclosure. 
         FIGS. 3A-3C  are pictorial diagrams of example user interfaces rendered by a client device in the networked environment of  FIG. 2  according to various embodiments of the present disclosure. 
         FIGS. 4A-4D  are pictorial diagrams of alternative example user interfaces rendered by a client device in the networked environment of  FIG. 2  according to various embodiments of the present disclosure. 
         FIG. 5  is a flowchart illustrating examples of functionality implemented as portions of an item search and navigation application executed in a computing environment in the networked environment of  FIG. 2  according to various embodiments of the present disclosure. 
         FIG. 6  is a schematic block diagram that provides one example illustration of a computing environment employed in the networked environment of  FIG. 2  according to various embodiments of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     The present disclosure relates to navigating an electronic item database based on an intention specified by the user. It may be difficult for a user to form an effective search query when searching for items in an electronic item database. A concise search query may broadly result in an unmanageably large result set of items. With large result sets, users may have to peruse numerous pages in order to find a desired item. Numerous items may match the broad search query but may not be relevant to how the user wishes to use the item. Unfortunately, the user may not have enough subject-specific knowledge in order to add limiting keywords in a productive way. 
     Electronic search interfaces may provide refinement tools in order for the user to select or deselect specific attributes that items in the result set must match. For example, where items are available in different colors, a color refinement tool may allow a user to specify one or more color attributes that the resulting items must match. Consequently, a user is able to refine or limit the scope of the search, thereby reducing the result set of items to a smaller quantity. 
     Nonetheless, with many types of items, particularly items of a technical nature, it may be difficult for a user to know which refinement attributes are important. That is, a user may not know, or may not have the particular expertise to know, which refinement tools should be used, and moreover, which potential attributes should be selected or deselected. Such an item navigation experience can frustrate users, who may then turn to a sales associate or a technical expert at a brick-and-mortar analogue in order to locate a desired item. 
     Various embodiments of the present disclosure leverage a user&#39;s intention with respect to an item in order to navigate an electronic item database. Specifically, user intention can be used to limit a result set of items by preselecting, in an electronic search interface, a collection of refinement attributes that pertain to the user intention. A subset of the available refinement tools may be utilized, as some refinement tools may pertain to characteristics that are irrelevant to the user intention. Users may specify their intention by answering one or more questions featured prominently in the electronic search interface. Upon the user answering the question(s), the refinement tools may be updated to show which attributes are preselected, and an updated result set of items may be rendered. 
     Turning to  FIG. 1A , shown is one example of a user interface  100  corresponding to an electronic search interface rendered by a browser. In particular, the user interface  100  presents an electronic search interface for items that are tents. For example, a user may have entered a search query for “tents” or may have navigated to a category of “tents” in an item classification tree. The user interface  100  includes some explanatory text  103  that acts as an introduction to one or more questions directed at ascertaining the user&#39;s intention. Here, the explanatory text  103  asks the user—“how do you intend to use this tent?” 
     Several user interface components  106  below the explanatory text  103  elicit the user intention, thereby allowing the user to specify an intention. In this example, the options provided are “car camping,” “backpacking,” “expedition or mountaineering,” and “other.” When a user selects a particular user intention via the user interface components  106 , the user interface  100  is dynamically updated to feature items that are most relevant to the user&#39;s intention. In this example, the user interface components  106  are radio buttons, but other user interface components such as checkboxes, drop-down boxes, sliders, hyperlinks, selectable images, etc., may be used in other embodiments. 
     The user interface  100  also includes several refinement tools  109 . The refinement tools  109  allow a user to specify certain refinement attributes that the items should match in order to be shown. The types of refinement attributes that are selectable within the refinement tools may depend on associated attributes within an item classification tree. In this example, the available refinement tools  109  allow for specification of attributes relating to “seasons,” “trail weight,” “pole material,” “design type,” “number of doors,” and “sleeping capacity.” These types of attributes are mostly specific to tents, so it is understood that different attributes may be shown for items that are generators, sleeping bags, backpacks, etc., for example. 
     The item display area  112  shows a selection of items from the electronic item database that match the search query. In addition, the result set of items may be filtered or ranked based at least in part on item title length, user feedback or review rating, or other attributes. Such attributes may be employed to present the user the most significant results out of the result set, rather than simply all items that match the keywords of the search query. The item display area  112  may be scrollable or paginated in order to accommodate a large quantity of items in a result set. Selecting any item in the item display area  112  may cause a detail page user interface to be rendered with additional information specific to the selected item. The item display area  112  may show item title, item manufacturer, item price, user rating, number of offers, or other information about the respective items. 
     Moving on to  FIG. 1B , the user interface  100  has been updated according to a user intention of “backpacking,” as specified via the user interface components  106  ( FIG. 1A ). The explanatory text  103  has been updated to introduce the results corresponding to the selected user intention of “backpacking.” One or more user interface components (e.g., the back arrow  115 ) may allow the user to return to specify a different user intention. 
     The item display area  112  has been dynamically updated according to the selected user intention to show a subset of the result set of items or a different result set of items. The items shown may be the same result set or may be ranked in a different order. A new result set may be specifically chosen as matching a set of refinement attributes corresponding to the selected user intention. As shown in the expanded refinement tools  109 , certain refinement attributes are automatically specified when the user intention of “backpacking” is selected. For example, under the “trail weight” refinement tool, the attributes of “under 3 pounds,” “3 to 4.9 pounds,” and “5 to 7.9 pounds” are automatically selected, and these attributes are emphasized in the user interface  100 . Here, emphasizing corresponds to bold text, but italics, color, underlining, etc., may be used with emphasized text in other embodiments. 
     Notably, “8 to 11.9 pounds” and “12 pounds &amp; above” are not selected in the refinement tools  109 . This is because, for the user intention of backpacking, it is desirable to have a tent that is under eight pounds as it must be carried for a long distance. A user could choose to manually select this combination of refinement attributes, but in many areas involving technical items, users do not know the best combinations of attributes for searching an electronic item database. 
     Accordingly, combinations of attributes may be preselected using various approaches. In a first approach, a combination of attributes corresponding to a user intention may be manually curated by, for example, technical experts, popular critics, popular designers, editors, or others who may have the judgment to pair particular selections of item attributes with user intentions. In a second approach, determining combinations of attributes may be automated over time by soliciting intent data from users and tracking user selections of attributes over time to refine a machine learning model. In a third approach, keywords signaling user intentions regarding an item may be identified from user reviews of the item, and a combination of attributes may be extracted as the attributes in common from the items ordered to fulfill the same user intention. Further, rather than selecting a combination of attributes to refine a previous search, some embodiments may utilize a predetermined change to a particular browse node in an item classification tree or a change of keywords for an item search. 
     Also, a user could choose to select other attributes or deselect one or more of the selected attributes, and the item display area  112  will be dynamically updated again to show a different result set of items. However, in various embodiments, the same automatically selected refinement attributes will remain emphasized in the user interface  100  despite being deselected, as long as the user intention remains active, in order to remind the user which attributes were recommended according to the user intention. For example, if the user were to select “3 season” under “seasons,” the item display area  112  would be updated to show only those tents that are also three-season tents, but the “3 season” attribute will not be emphasized. Conversely, if the user were to deselect the “5 to 7.9 pounds” attribute, the item display area  112  would be updated to exclude tents over 4.9 pounds, yet the “5 to 7.9 pounds” attributes would remain emphasized as bolded text. In the following discussion, a general description of the system and its components is provided, followed by a discussion of the operation of the same. 
     With reference to  FIG. 2 , shown is a networked environment  200  according to various embodiments. The networked environment  200  includes a computing environment  203  and one or more client devices  206 , which are in data communication with each other via a network  209 . The network  209  includes, for example, the Internet, intranets, extranets, wide area networks (WANs), local area networks (LANs), wired networks, wireless networks, cable networks, satellite networks, or other suitable networks, etc., or any combination of two or more such networks. 
     The computing environment  203  may comprise, for example, a server computer or any other system providing computing capability. Alternatively, the computing environment  203  may employ a plurality of computing devices that may be arranged, for example, in one or more server banks, computer banks, or other arrangements. Such computing devices may be located in a single installation or may be distributed among many different geographical locations. For example, the computing environment  203  may include a plurality of computing devices that together may comprise a hosted computing resource, a grid computing resource, and/or any other distributed computing arrangement. In some cases, the computing environment  203  may correspond to an elastic computing resource where the allotted capacity of processing, network, storage, or other computing-related resources may vary over time. 
     Various applications and/or other functionality may be executed in the computing environment  203  according to various embodiments. Also, various data is stored in a data store  212  that is accessible to the computing environment  203 . The data store  212  may be representative of a plurality of data stores  212  as can be appreciated. The data stored in the data store  212 , for example, is associated with the operation of the various applications and/or functional entities described below. 
     The components executed on the computing environment  203 , for example, include an item search and navigation application  215  and other applications, services, processes, systems, engines, or functionality not discussed in detail herein. The item search and navigation application  215  is executed in order to facilitate the online selection of items over the network  209 . The item search and navigation application  215  may perform various backend functions associated with an electronic marketplace in order to facilitate the online selection of items as will be described. For example, the item search and navigation application  215  may generate network pages such as web pages or other types of network content that are provided to client devices  206  for the purposes of selecting items for purchase, rental, download, lease, or other form of consumption as will be described. 
     The item search and navigation application  215  may also generate search results  219  in response to receiving search criteria  220  from a client device  206  over the network  209 . To this end, the item search and navigation application  215  is configured to search an electronic item database  221  for items  224  that are associated with data that matches the search criteria  220 . The item search and navigation application  215  may apply one or more refinements received from the client device  206  or stored in connection with a user profile in order to filter or limit the search results  219 . The generated search results  219  may be included within a search result listing that is returned to the client device  206  for rendering in a user interface. In addition, for certain types of items or search queries, the item search and navigation application  215  is configured to facilitate navigation by way of user intention, whereby a user specifies an intention with respect to a desired item, and refinement attributes are selected automatically on behalf of the user. 
     The data stored in the data store  212  includes, for example, an electronic item database  221 , user data  225 , feedback data  226 , user intention navigation data  227 , machine learning model data  228 , and potentially other data. The electronic item database  221  includes information about a plurality of items  224  offered by one or more sellers in an electronic marketplace facilitated by the item search and navigation application  215 . As used herein, the term “item” may refer to products, goods, services, downloads, and/or any other item that may be offered for sale, lease, rental, or other forms of consumption. 
     In some cases, the items  224  may be organized within the electronic item database  221  into an item classification tree  234  (or taxonomy) of categories to facilitate browsing, which may be represented, for example, by a tree structure composed of browse nodes  235 . As a non-limiting example, a browse node  235  may correspond to “Crafts” with multiple child browse nodes  235  such as “Jewelry” and “Home Decor.” An item  224  may be associated with one or more such browse nodes  235 . 
     Each item  224  may be associated with item information  236 , attributes  237 , and/or other data. In some cases, an item  224  may be offered by multiple sellers in an electronic marketplace. The item information  236  may include title, description, weight, images, shipping classifications, user reviews, videos, and/or other information that may be used to describe an item  224 . The item attributes  237  correspond to metadata about the item  224  that allow for location of the item  224  by way of refinement tools  109  ( FIGS. 1A &amp; 1B ). The attributes  237  may be specified in a standardized way so as to allow for comparison and contrast of different items  224  across one or more attributes  237 . The attributes  237  may be specific to the type or category of item  224 . 
     The user data  225  may include various data about users of the electronic marketplace, including profile information, personalization information, demographic information, browsing history, order history, previous purchasing habits, and so on. The user data  225  may be used, in particular, by the item search and navigation application  215  to personalize search results  219  for a user. This may involve including or excluding particular items  224  in search results  219  or applying a user-specific ordering to the search results  219  based at least in part on relevance of the particular items  224  to the profile information of the specific user. For example, if a user has ordered a specific brand of tents in the past, tents associated with that brand may be ranked higher in the search results  219 . 
     The feedback data  226  corresponds to various forms of user feedback about items  224 . This can include textual reviews of items  224  and ratings of items  224 . Items  224  may be given overall ratings by users (e.g., 3.5 out of 5 stars), and/or the ratings may be given across specific dimensions (e.g., manufacturer packaging or ease of use). The ratings provided by individual users may be averaged or combined to determine a composite rating across all users. 
     The user intention navigation data  227  includes various data that enables providing search results  219  not only based upon conventional search criteria  220  such as a keyword search query but also based upon a user intention  238 . A user intention  238  corresponds to what the user intends with respect to a desired item  224 . In some examples, this may correspond to a use case, or how the user intends to use the item  224 . In other examples, this may correspond to a desired result or what the user seeks to be accomplished by the item  224 , particularly where the item  224  is a service. The user intentions  238  may be configured manually or by way of an automated discovery process as will be described. 
     The user intention navigation data  227  associates the user intentions  238  with selected refinement attributes  239  corresponding to the user intentions  238 . Certain types of items  224  may be more applicable to a given use case than other types of items  224 . The selected refinement attributes  239  are selected based upon attributes  237  in common of items  224  that are applicable to a use case or user intention  238 . In various embodiments, the selected refinement attributes  239  may be preconfigured for a given user intention  238  or may be determined through an automated discovery process for a user intention  238  as will be described. In some embodiments, preselected keywords for an item search or preselected browse nodes  235  for an item classification tree  234  may be employed in lieu of selected refinement attributes  239  for an item search following specification of a user intention  238 . 
     The user intention navigation data  227  may also include configuration data  240  that configures the user experience for navigation using intention. In particular, the configuration data  240  may include explanatory text and code for user interface elements that elicit the intention of the user. For example, the configuration data  240  may configure a series of one or more questions that elicit the user&#39;s intention. Further, the configuration data  240  may include various thresholds and parameters that can be used for filtering a result set of items  224  for relevance. For example, the configuration data  240  may configure the result set to be filtered to exclude (or rank lower) items  224  with very long item titles, items  224  that are associated with a relatively low user feedback rating, or items  224  with relatively few orders. 
     The machine learning model data  228  may correspond to data for one or more machine learning models used to ascertain for which types of items  224  that intention-based navigation should be used, which user intentions  238  should be options, and which selected refinement attributes  239  should be associated with the user intentions  238 . In this regard, a feedback model may be employed to determine effectiveness of the navigation, with an order or another type of user interaction being considered a successful outcome, while a lack of an order may be considered an unsuccessful outcome. Additional user intentions may be learned from the users (e.g., a user may be able to specify his or her intention via freeform text). Machine learning models may also be used in some cases to ascertain categories of attributes that are to be the basis of refinement tools  109  ( FIG. 1A ), such as “Seasons,” “Trail Weight,” etc., as shown in the example of  FIG. 1A . 
     The client device  206  is representative of a plurality of client devices that may be coupled to the network  209 . The client device  206  may comprise, for example, a processor-based system such as a computer system. Such a computer system may be embodied in the form of a desktop computer, a laptop computer, personal digital assistants, cellular telephones, smartphones, set-top boxes, music players, web pads, tablet computer systems, game consoles, electronic book readers, or other devices with like capability. The client device  206  may include a display  251 . The display  251  may comprise, for example, one or more devices such as liquid crystal display (LCD) displays, gas plasma-based flat panel displays, organic light emitting diode (OLED) displays, electrophoretic ink (E ink) displays, LCD projectors, or other types of display devices, etc. 
     The client device  206  may be configured to execute various applications such as a client application  254  and/or other applications. The client application  254  may be executed in a client device  206 , for example, to access network content served up by the computing environment  203  and/or other servers, thereby rendering a user interface  100  on the display  251 . To this end, the client application  254  may comprise, for example, a browser, a dedicated application, etc., and the user interface  100  may comprise a network page, an application screen, etc. The client device  206  may be configured to execute applications beyond the client application  254  such as, for example, email applications, social networking applications, word processors, spreadsheets, and/or other applications. 
     Referring next to  FIG. 3A , shown is a user interface  100  rendered by a client application  254  ( FIG. 2 ) executed in a client device  206  ( FIG. 2 ) in the networked environment  200  ( FIG. 2 ). The user interface  100  corresponds to the example of  FIG. 1A  with an explanatory dialog  300  shown relative to a user intention option  303 . When a user hovers a cursor over any of the user interface components  106  ( FIG. 1A ) that enable selection or specification of a user intention  238  ( FIG. 2 ), an explanatory dialog  300  may be rendered to provide an example use case or further explanation as to why a user would want to choose the particular user intention option  303 . 
     The explanatory dialog  300  may hint at the selected refinement attributes  239  ( FIG. 2 ) that accompany the particular user intention option  303 . Here, for example, the explanatory dialog  300  for “car camping” explains, “I&#39;m driving to a campsite. Comfort and utility are my top priorities.” This indicates that the selected refinement attributes  239  may be related to “comfort and utility.” In this example, the explanatory dialog  300  is shown as a pop-over window, but the explanatory dialog  300  may be shown using other user interface elements (e.g., modal windows, pop-up windows, and different regions of the user interface  100 ) in other examples. 
     Turning now to  FIG. 3B , shown is a user interface  100  rendered by a client application  254  ( FIG. 2 ) executed in a client device  206  ( FIG. 2 ) in the networked environment  200  ( FIG. 2 ). The user interface  100  corresponds to the example of  FIG. 1A  with an explanatory modal window  306  shown relative to a refinement tool  309  of the refinement tools  109 . The refinement tool  309  allows refinement of the result set of items  224  ( FIG. 2 ) according to a particular type of attribute  237  ( FIG. 2 ). In this example, the attribute  237  corresponds to the season appropriateness of the tent items  224 . The user may select the “&gt;” component to expand the refinement tool  309  to see specific attribute options, which may be represented as checkboxes, drop-down boxes, radio buttons, or other user interface elements. 
     As part of the refinement tool  309 , a component  312  allows the user to obtain further information about the refinement tool  309 . When the user selects the component  312 , either by active selection or hovering, the explanatory modal window  306  is shown to provide further information. The explanatory modal window  306  may be closed by the user when the user is finished. Alternatively, the user may select the component  315  to learn additional information, which may be shown in a new window or as a replacement for the user interface  100 . 
     Continuing to  FIG. 3C , shown is a user interface  100  rendered by a client application  254  ( FIG. 2 ) executed in a client device  206  ( FIG. 2 ) in the networked environment  200  ( FIG. 2 ). The user interface  100  corresponds to the example of  FIG. 1A  with an explanatory modal window  318  shown after selecting the component  315  ( FIG. 3B ) to learn additional information. The explanatory modal window  318  may provide a tabbed interface with respective tab components to learn additional information about each of the categories associated with the refinement tools  109  ( FIG. 1A ). Videos and other rich multimedia may accompany text in order to provide detailed information. In one embodiment, the explanatory modal window  318  may be shown as a lightbox relative to the user interface  100 , with the rest of the user interface  100  being grayed, dimmed, or otherwise deemphasized. 
     Moving on to  FIG. 4A , shown is a user interface  400  rendered by a client application  254  ( FIG. 2 ) executed in a client device  206  ( FIG. 2 ) in the networked environment  200  ( FIG. 2 ). The user interface  400  corresponds to the content shown in the example of the user interface  100  of  FIG. 1A  but rendered in an alternative format for limited-area displays  251  ( FIG. 2 ) that are touchscreens such as with mobile devices. 
     In the user interface  400 , the explanatory text  103  and the user interface components  106  are present, and the user may scroll or page to see an initial result set of items  224  ( FIG. 2 ). The user may select the component  403  to see the information about the user intention presented in  FIG. 3A  via the explanatory dialog  300 . Also, the user may select the component  406  to see a user interface that includes the refinement tools  109  ( FIG. 1A ), which may be referred to as filters. 
     Continuing to  FIG. 4B , shown is another example of a user interface  400  rendered by a client application  254  ( FIG. 2 ) executed in a client device  206  ( FIG. 2 ) in the networked environment  200  ( FIG. 2 ). The user interface  400  corresponds to the content shown in the example of the user interface  100  of  FIG. 1B  but rendered in an alternative format for limited-area displays  251  ( FIG. 2 ) that are touchscreens such as with mobile devices. The user interface  400  of  FIG. 4B  includes an item display area  112  that may be scrolled or paged to see more items  224  ( FIG. 2 ) in the result set. 
     The user interface  400  also includes explanatory text  103  and a back arrow  115  to return to the user interface  400  of  FIG. 4A . Also, the user may select the component  406  to see a user interface that includes the refinement tools  109  ( FIG. 1A ), which may be referred to as filters. A user interface element  409  informs the user that a number of filters have automatically been applied to refine the result set of items  224  in response to the user&#39;s selection of an intention. The user interface element  409  may be ephemeral and shown only when the user interface  400  is first updated. The user interface element  409  may fade or become hidden a short time after rendering in one embodiment. 
     Referring next to  FIG. 4C , shown is another example of a user interface  400  rendered by a client application  254  ( FIG. 2 ) executed in a client device  206  ( FIG. 2 ) in the networked environment  200  ( FIG. 2 ). The user interface  400  of this example presents the refinement tools  109  for the user interface  400  of the example of  FIG. 4B . For example, if the user were to select the component  406  in  FIG. 4B , the example of  FIG. 4C  would be rendered. The text  415  informs the user that seven filters have been applied to the result set of items  224  ( FIG. 2 ). The user interface  400  includes a component  418  that, when selected, returns the user interface  400  to that of  FIG. 4B . 
     For a subset of the refinement tools  109 , one or more refinement tools  309  include selected attribute information  421  informing the user which attributes in the refinement tool  309  are automatically selected. In this example, “under 3 pounds,” “3 to 4.9 pounds,” and “5 to 7.9 pounds” are automatically selected in response to the user intention of backpacking. 
     Turning now to  FIG. 4D , shown is another example of a user interface  400  rendered by a client application  254  ( FIG. 2 ) executed in a client device  206  ( FIG. 2 ) in the networked environment  200  ( FIG. 2 ). The user interface  400  of this example presents an expansion of a refinement tool  309  for the user interface  400  of the example of  FIG. 4C . Additional explanatory text  424  offers more information to the user about the particular refinement tool  309  and the associated attributes that are preselected. As in the example of  FIG. 1B , several attributes are preselected for the user in response to the user intention. These are shown in this example as checkboxes that are already filled in, along with bolded text adjacent to the checkboxes. It is understood that alternative user interface components (e.g., drop-down boxes, radio buttons, etc.) as well as alternative forms of emphasis (e.g., underlining, italics, all capital letters, larger font size, etc.) may be used in other embodiments. 
     Referring next to  FIG. 5 , shown is a flowchart that provides one example of the operation of a portion of the item search and navigation application  215  according to various embodiments. It is understood that the flowchart of  FIG. 5  provides merely an example of the many different types of functional arrangements that may be employed to implement the operation of the portion of the item search and navigation application  215  as described herein. As an alternative, the flowchart of  FIG. 5  may be viewed as depicting an example of elements of a method implemented in the computing environment  203  ( FIG. 2 ) according to one or more embodiments. 
     Beginning with box  503 , the item search and navigation application  215  generates user intentions  238  ( FIG. 2 ) and selected refinement attributes  239  ( FIG. 2 ). Alternatively, the item search and navigation application  215  obtain manually curated data corresponding to user intentions  238  and selected refinement attributes  239 . In one embodiment, the item search and navigation application  215  may perform an analysis on the electronic item database  221  ( FIG. 2 ) to identify types of items  224  ( FIG. 2 ) for which user intention-based navigation would be helpful. For example, the item search and navigation application  215  may identify types of items  224  that are associated with a number of category-specific attributes  237  ( FIG. 2 ). 
     The item search and navigation application  215  may determine user intentions  238  or a plurality of item usages, for example, by analyzing feedback data  226 . In this regard, the item search and navigation application  215  may extract a user intention  238  from user reviews about an item  224 . If a user who buys a tent then writes a review that mentions, “I bought this tent for backpacking,” the item search and navigation application  215  may extract the user intention  238  via natural-language processing. Clustering algorithms may then be used to group the user intentions  238 , and synonyms or duplicates can be removed. A threshold may be applied to remove uncommon user intentions. 
     Next, the selected refinement attributes  239  associated with a user intention  238  may be determined. A group of items may be determined and associated with a particular item usage based at least in part on users expressing the particular item usage in user reviews and also ordering the item  224 . Once a user intention  238  is determined, a group of items  224  identified as being ordered to fulfill the user intention  238  may be examined for common item attributes  237 . Again, a threshold may be applied to remove attributes  237  that exist but are not prevalent or shared among a minimum quantity of items  224 . 
     In box  506 , the item search and navigation application  215  obtains search criteria  220  ( FIG. 2 ) from a client device  206  ( FIG. 2 ). For example, a user may visit a network page and then enter a keyword-based search query into a search box. Alternatively, the user may visit a category page that is associated with a specific search criterion  220  (e.g., a browse node  235  ( FIG. 2 ) in an item classification tree  234  ( FIG. 2 )). 
     In box  509 , the item search and navigation application  215  generates a result set of items  224  that match the search criteria  220  by executing a search on the electronic item database  221 . In box  512 , the item search and navigation application  215  may filter the result set to exclude or record items  224  based at least in part on relevance criteria. In one example, items  224  having an item title exceeding a maximum length may be excluded, as such a title may reflect a poor quality item listing that is not correctly associated with attributes  237 , where such attributes  237  are contained improperly in the title rather than item metadata. In another example, the result set may be filtered to exclude low-rated items  224  or items  224  that have not been ordered at least a minimum number of times. 
     In box  515 , the item search and navigation application  215  generates a user interface  100  ( FIG. 2 ) that presents the result set with intention-based navigation. Data encoding the user interface  100  (e.g., the search results  219 ) is sent to the client device  206  via the network  209  ( FIG. 2 ) for rendering upon the display  251  ( FIG. 2 ). The user interface  100  includes one or more user interface components  106  ( FIGS. 1A &amp; 4A ) configured to elicit a user selection from among a plurality of possible user intentions  238 . The user interface  100  may ask the user one or more questions, where the user intention  238  is determined from the answers to the questions. In one embodiment, a catch-all category may be shown (e.g., “other”), and the user may be prompted to enter his or her different user intention as freeform text. This user-supplied text string that corresponds to a non-predetermined user intention may be recorded and used for future analysis to identify user intentions  238 . 
     In box  518 , the item search and navigation application  215  receives or otherwise determines a user intention  238 . For example, the user may select a particular radio button corresponding to a use case. The selection may be returned from the client device  206  to the item search and navigation application  215  via the network  209 . In another example, the user intention  238  may be determined implicitly from context (e.g., past search queries of the user, cookie data associated with the user). In box  521 , the item search and navigation application  215  identifies selected refinement attributes  239  that are associated with the specified user intention  238 . In some examples, the specified user intention  238  may be associated with specific search query keywords or a specific browse node  235  ( FIG. 2 ) in an item classification tree  234  ( FIG. 2 ). In box  524 , the item search and navigation application  215  determines an updated result set of items  224  that match the selected refinement attributes  239 . This may correspond to a subset of the previous result set of items  224  or a different set of items  224 . If a different set of items  224 , the item search and navigation application  215  may perform the filtering operation of box  512  on this different set of items  224  as well. 
     In box  527 , the item search and navigation application  215  causes the user interface  100  to be dynamically updated to present the updated result set. In this regard, the item search and navigation application  215  may send data encoding the updates to the user interface  100  via the network  209  to the client device  206  for rendering on the display  251 . The updated user interface  100  may be configured to emphasize the attributes in the refinement tools  109  ( FIGS. 1B, 4C , &amp;  4 D) that are automatically selected and enabled due to the user intention  238 . The selected refinement attributes  239  are emphasized relative to refinement attributes  239  that are in a set associated with the type of item  224  but are not automatically selected and enabled. For example, the selected refinement attributes  239  may be presented with bold text and checkboxes selected, while the non-selected refinement attributes may be presented with normal text and checkboxes not selected. Thereafter, the portion of the item search and navigation application  215  ends. 
     The user may continue to interact with the user interface  100 , including selecting different user intentions  238  or enabling or disabling various refinement attributes. While the same user intention  238  is active, the user may disable preselected refinement attributes or enable non-preselected refinement attributes. This will cause the result set of items  224  to be dynamically updated to show the items  224  that match the new combination of attributes  237 . However, despite the change in selected attributes, the selected refinement attributes  239 , and only those attributes, will remain emphasized in the user interface  100 . Thus, the enabled non-preselected attributes will not be emphasized, and the disabled preselected attributes will remain emphasized. This serves to remind the user as to which of the attributes were automatically preselected via the user intention selection. 
     With reference to  FIG. 6 , shown is a schematic block diagram of the computing environment  203  according to an embodiment of the present disclosure. The computing environment  203  includes one or more computing devices  600 . Each computing device  600  includes at least one processor circuit, for example, having a processor  603  and a memory  606 , both of which are coupled to a local interface  609 . To this end, each computing device  600  may comprise, for example, at least one server computer or like device. The local interface  609  may comprise, for example, a data bus with an accompanying address/control bus or other bus structure as can be appreciated. 
     Stored in the memory  606  are both data and several components that are executable by the processor  603 . In particular, stored in the memory  606  and executable by the processor  603  are the item search and navigation application  215  and potentially other applications. Also stored in the memory  606  may be a data store  212  and other data. In addition, an operating system may be stored in the memory  606  and executable by the processor  603 . 
     It is understood that there may be other applications that are stored in the memory  606  and are executable by the processor  603  as can be appreciated. Where any component discussed herein is implemented in the form of software, any one of a number of programming languages may be employed such as, for example, C, C++, C#, Objective C, Java®, JavaScript®, Perl, PHP, Visual Basic®, Python®, Ruby, Flash®, or other programming languages. 
     A number of software components are stored in the memory  606  and are executable by the processor  603 . In this respect, the term “executable” means a program file that is in a form that can ultimately be run by the processor  603 . Examples of executable programs may be, for example, a compiled program that can be translated into machine code in a format that can be loaded into a random access portion of the memory  606  and run by the processor  603 , source code that may be expressed in proper format such as object code that is capable of being loaded into a random access portion of the memory  606  and executed by the processor  603 , or source code that may be interpreted by another executable program to generate instructions in a random access portion of the memory  606  to be executed by the processor  603 , etc. An executable program may be stored in any portion or component of the memory  606  including, for example, random access memory (RAM), read-only memory (ROM), hard drive, solid-state drive, USB flash drive, memory card, optical disc such as compact disc (CD) or digital versatile disc (DVD), floppy disk, magnetic tape, or other memory components. 
     The memory  606  is defined herein as including both volatile and nonvolatile memory and data storage components. Volatile components are those that do not retain data values upon loss of power. Nonvolatile components are those that retain data upon a loss of power. Thus, the memory  606  may comprise, for example, random access memory (RAM), read-only memory (ROM), hard disk drives, solid-state drives, USB flash drives, memory cards accessed via a memory card reader, floppy disks accessed via an associated floppy disk drive, optical discs accessed via an optical disc drive, magnetic tapes accessed via an appropriate tape drive, and/or other memory components, or a combination of any two or more of these memory components. In addition, the RAM may comprise, for example, static random access memory (SRAM), dynamic random access memory (DRAM), or magnetic random access memory (MRAM) and other such devices. The ROM may comprise, for example, a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or other like memory device. 
     Also, the processor  603  may represent multiple processors  603  and/or multiple processor cores and the memory  606  may represent multiple memories  606  that operate in parallel processing circuits, respectively. In such a case, the local interface  609  may be an appropriate network that facilitates communication between any two of the multiple processors  603 , between any processor  603  and any of the memories  606 , or between any two of the memories  606 , etc. The local interface  609  may comprise additional systems designed to coordinate this communication, including, for example, performing load balancing. The processor  603  may be of electrical or of some other available construction. 
     Although the item search and navigation application  215  and other various systems described herein may be embodied in software or code executed by general purpose hardware as discussed above, as an alternative the same may also be embodied in dedicated hardware or a combination of software/general purpose hardware and dedicated hardware. If embodied in dedicated hardware, each can be implemented as a circuit or state machine that employs any one of or a combination of a number of technologies. These technologies may include, but are not limited to, discrete logic circuits having logic gates for implementing various logic functions upon an application of one or more data signals, application specific integrated circuits (ASICs) having appropriate logic gates, field-programmable gate arrays (FPGAs), or other components, etc. Such technologies are generally well known by those skilled in the art and, consequently, are not described in detail herein. 
     The flowchart of  FIG. 5  shows the functionality and operation of an implementation of portions of the item search and navigation application  215 . If embodied in software, each block may represent a module, segment, or portion of code that comprises program instructions to implement the specified logical function(s). The program instructions may be embodied in the form of source code that comprises human-readable statements written in a programming language or machine code that comprises numerical instructions recognizable by a suitable execution system such as a processor  603  in a computer system or other system. The machine code may be converted from the source code, etc. If embodied in hardware, each block may represent a circuit or a number of interconnected circuits to implement the specified logical function(s). 
     Although the flowchart of  FIG. 5  shows a specific order of execution, it is understood that the order of execution may differ from that which is depicted. For example, the order of execution of two or more blocks may be scrambled relative to the order shown. Also, two or more blocks shown in succession in  FIG. 5  may be executed concurrently or with partial concurrence. Further, in some embodiments, one or more of the blocks shown in  FIG. 5  may be skipped or omitted. In addition, any number of counters, state variables, warning semaphores, or messages might be added to the logical flow described herein, for purposes of enhanced utility, accounting, performance measurement, or providing troubleshooting aids, etc. It is understood that all such variations are within the scope of the present disclosure. 
     Also, any logic or application described herein, including the item search and navigation application  215 , that comprises software or code can be embodied in any non-transitory computer-readable medium for use by or in connection with an instruction execution system such as, for example, a processor  603  in a computer system or other system. In this sense, the logic may comprise, for example, statements including instructions and declarations that can be fetched from the computer-readable medium and executed by the instruction execution system. In the context of the present disclosure, a “computer-readable medium” can be any medium that can contain, store, or maintain the logic or application described herein for use by or in connection with the instruction execution system. 
     The computer-readable medium can comprise any one of many physical media such as, for example, magnetic, optical, or semiconductor media. More specific examples of a suitable computer-readable medium would include, but are not limited to, magnetic tapes, magnetic floppy diskettes, magnetic hard drives, memory cards, solid-state drives, USB flash drives, or optical discs. Also, the computer-readable medium may be a random access memory (RAM) including, for example, static random access memory (SRAM) and dynamic random access memory (DRAM), or magnetic random access memory (MRAM). In addition, the computer-readable medium may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or other type of memory device. 
     Further, any logic or application described herein, including the item search and navigation application  215 , may be implemented and structured in a variety of ways. For example, one or more applications described may be implemented as modules or components of a single application. Further, one or more applications described herein may be executed in shared or separate computing devices or a combination thereof. For example, a plurality of the applications described herein may execute in the same computing device  600 , or in multiple computing devices  600  in the same computing environment  203 . 
     Disjunctive language such as the phrase “at least one of X, Y, or Z,” unless specifically stated otherwise, is otherwise understood with the context as used in general to present that an item, term, etc., may be either X, Y, or Z, or any combination thereof (e.g., X, Y, and/or Z). Thus, such disjunctive language is not generally intended to, and should not, imply that certain embodiments require at least one of X, at least one of Y, or at least one of Z to each be present. 
     It should be emphasized that the above-described embodiments of the present disclosure are merely possible examples of implementations set forth for a clear understanding of the principles of the disclosure. Many variations and modifications may be made to the above-described embodiment(s) without departing substantially from the spirit and principles of the disclosure. All such modifications and variations are intended to be included herein within the scope of this disclosure and protected by the following claims.