Patent Publication Number: US-2005144086-A1

Title: Product recommendation in a network-based commerce system

Description:
CROSS REFERENCE TO RELATED APPLICATION  
      This application is a continuation of U.S. application Ser. No. 10/666,681 filed Sep. 18, 2003 and claims the benefit of the filing date of U.S. provisional application Ser. No. 60/420,199, filed Oct. 21, 2002 and U.S. provisional application Ser. No. 60/482,605, filed Jun. 25, 2003 and which are incorporated herein by reference. 
    
    
     FIELD OF THE INVENTION  
      The present invention relates generally to the field of electronic commerce, and more specifically to a method and system to recommend listings in a network-based commerce system.  
     BACKGROUND  
      More and more Internet users are realizing the ease and convenience of buying and selling online via a network-based commerce system. Certain such commerce systems are focused on person-to-person trading, and collectors, hobbyists, small dealers, unique listing seekers, bargain hunters, and other consumers, are able to buy and sell millions of listings at various online shopping sites. Such systems also support business-to-person and business-to-business commerce.  
      The success of a networked-based commerce system may depend upon its ability to provide a user-friendly environment in which buyers and sellers can conduct business efficiently. Current network-based commerce systems have certain limitations in the manner in which they present information to users.  
     SUMMARY OF THE INVENTION  
      A method and system of providing listing recommendations to users of a network-based commerce system including a plurality of listings arranged in a plurality of divisions is described. The method includes identifying a division of the plurality of divisions based on user interaction with the network-based commerce system, and identifying at least one frequently used search term associated with the division. A link is provided to the user to listings associated with the frequently used search term. The search terms may be ranked by retrieving frequently used search terms from a first memory location and determining a number of listings in each division associated with each frequently used search term. Each frequently used search term may then be ranked based on the number of listings in each division.  
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
      The invention is now described, by way of example, with reference to the accompanying diagrammatic drawings in which the same reference numerals indicate the same or similar features.  
       FIG. 1  is block diagram illustrating an exemplary network-based commerce system, in accordance with the invention.  
       FIG. 2  is a database diagram illustrating an exemplary database, maintained by, and accessed via, a database engine server, which at least partially implements and supports the network-based commerce system.  
       FIG. 3A  is a diagram illustrating popular search term location logic, according to an exemplary embodiment of the present invention, to determine and rank popular search terms to be utilized in recommending listings to users of the network-based commerce system.  
       FIG. 3B  is a diagram illustrating popular search term presentation logic, according to an exemplary embodiment of the present invention, to provide recommendations to users of a network-based commerce system.  
       FIG. 4A  provides an exemplary embodiment of a popular search term table.  
       FIG. 4B  provides an exemplary embodiment of a popular search term ranking table.  
       FIG. 4C  is a popular search term list provided to illustrate an example of the contents of the popular search term ranking table.  
       FIG. 5  is a flowchart illustrating a method, according to an exemplary embodiment of the present invention, of determining popular search terms to be included within a group of preliminary search terms utilized by a network-based commerce system in generating recommendations to a user interacting with listings in the network-based commerce system.  
       FIG. 6  provides an exemplary embodiment of a preliminary popular search term table.  
       FIG. 7  provides an exemplary embodiment of a filtered popular search term table.  
       FIG. 8  is a flowchart illustrating a method, according to an exemplary embodiment of the present invention, of filtering popular search terms utilized by a network-based commerce system in generating recommendations to a user interacting with listings in the network-based commerce system.  
       FIG. 9  is a flowchart illustrating a method, according to an exemplary embodiment of the present invention, of assigning each of the popular search terms to a category in the network-based commerce system.  
       FIG. 10  provides an exemplary embodiment of an approved popular search term table.  
       FIG. 11  is a flowchart illustrating a method, according to an exemplary embodiment of the present invention, of providing a user with the opportunity to view listings based on the supply and demand of the listings in the network-based commerce system.  
       FIG. 12A  is a user interface, according to an exemplary embodiment of the present invention, to display selectable popular search terms to a user.  
       FIG. 12B  illustrates an exploded view of a groups window included within the user interface of  FIG. 12A .  
       FIG. 13  shows a diagrammatic representation of a machine in the exemplary form of a computer system within which a set of instructions, for causing a machine to perform any one of the methodologies discussed herein, may be executed.  
    
    
     DETAILED DESCRIPTION  
      A method and system automatically to recommend listings, and rank search terms, in a network-based commerce system is described. The recommended listings and ranked search terms may be based on supply and demand of the listings. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present invention. It will be evident, however, to one skilled in the art that the present invention may be practiced without these specific details.  
      Terminology  
      For the purposes of the present specification, the term “listing” may refer to any description, identifier, representation or information pertaining to a listing, service, offering or request that is stored within a network-based commerce system or facility. In one embodiment, the listings may include products (e.g., goods and/or services) and the listing may be an auction or fixed-price offering, an advertisement, or a request for a listing or service.  
      The term “listing recommendation” includes any instance of a listing (or information about associated listings) being presented to a user by a network-based commerce system. The word “term” includes any criteria, textual, numeric, visual, audible or otherwise, submitted by users searching a network-based commerce system. It is to be appreciated that the word “term” and the word “phrase” may be used interchangeably and shall be taken to include search terms using multiple words or characters. Thus, a search entries such as “men&#39;s clothing” and “shirts” would both be referred to a search terms. Broadly, any entry by a user into a search field may thus define a search “term” or “phrase”.  
      The phrase “frequently used search term” is intended to include, for example, terms that are frequently entered by users when conducting searches for listings. The frequently used search terms need not be limited to terms used in a specific network-based commerce facility or system but may include terms used in other facilities. Thus, frequently used search terms include popular terms that would generally be associated by users with one or more listings. Thus, the words “frequently used” and the word “popular” may be used synonymously.  
      Transaction Facility  
       FIG. 1  is block diagram illustrating an exemplary network-based commerce system  10 . While an exemplary embodiment of the present invention is described within the context of the network-based commerce system  10 , the invention may find application in many different types of computer-based, and network-based, facilities (commerce, transaction or otherwise).  
      The exemplary network-based commerce system  10  includes one or more of a number of types of front-end servers that each includes at least one Dynamic Link Library (DLL) to provide certain functionality. Page servers  12  deliver web pages (e.g., mark-up language documents), picture servers  14  dynamically deliver images to be displayed within Web pages, listing servers  16  facilitate category-based browsing of listings, and search servers  20  handle search requests to the network-based commerce system  10  and facilitate keyword-based browsing of listings. ISAPI servers  18  provide an intelligent interface to a back-end of the network-based commerce system  10 . E-mail servers  22  provide, inter alia, automated e-mail communications to users of the network-based commerce system  10 . Administrative application functions  32  facilitate monitoring, maintaining, and managing the network-based commerce system  10 . API servers  13  provide a set of functions for querying and writing to the network-based commerce system  10 . API functions are called via HTTP transport protocol and information may be sent and received using a standard XML data format. Applications utilized to interact (e.g., upload transaction listings, review transaction listings, manage transaction listings, etc.) with the network-based commerce system  10  may be designed to use the API servers  13 . Such applications may be in HTML form or may be a CGI program written in C++, Perl, Pascal, or any other programming language. Exemplary API functions are more fully described in co-pending U.S. patent application Ser. No. 09/999,618, incorporated herein by reference.  
      The page servers  12 , API servers  13 , picture servers  14 , listing servers  16 , ISAPI servers  18 , search servers  20 , e-mail servers  22  and a database engine server  26  may individually, or in combination, act as a communication engine to facilitate communication between, for example, a client machine  38  and the network-based commerce system  10 ; act as a transaction engine to facilitate transactions between, for example, the client machine  38  and the network-based commerce system  10 ; and act as a display engine to facilitate the display of listings between, for example, the client machine  38  and the network-based commerce system  10 .  
      The network-based commerce system  10  may also include one or more of a number of types of back-end servers. The back-end servers are shown, by way of example, to include the database engine server  26 , a search index server  24  and a credit card database server  28 , each of which may maintain and facilitate access to a respective database. In one embodiment, the back-end servers are included within a storage area network (SAN).  
      The network-based commerce system  10  may be accessed by a client program, such as a browser  36  (e.g., the Internet Explorer distributed by Microsoft Corp. of Redmond, Wash.) that executes on the client machine  38  and accesses the network-based commerce system  10  via a network such as, for example, the Internet  34 . Other examples of networks via which a client may access the network-based commerce system  10  include a wide area network (WAN), a local area network (LAN), a wireless network (e.g., a cellular network), a Public Switched Telephone Network (PSTN) network, or the like.  
      Database Structure  
       FIG. 2  is a database diagram illustrating an exemplary database  30  (see also  FIG. 1 ), maintained by and accessed via the database engine server  26 , which at least partially implements and supports the network-based commerce system  10 . In one embodiment, the database engine server  26  maintains two databases. A first database may be maintained for listing (or offering) information that is not included within a virtual “store”, and a second database may store offerings that are presented via virtual “stores” supported by the network-based commerce system  10 . In one embodiment the structure of these databases may be substantially the same, but may differ in that the tables of the “store” database may include a number of additional fields to facilitate the virtual “stores”. A general discussion of the basic structure of a single database  30  is presented below, but is also applicable when two (or more) databases are present.  
      The database  30  may, in one embodiment, be implemented as a relational database, and include a number of tables having entries, or records, that are linked by indices and keys. In an alternative embodiment, the database  30  may be implemented as a collection of objects in an object-oriented database.  
      A user table  54  (see  FIG. 2 ) may contain a record for each user of the network-based commerce system  10 . A user may operate as a seller, buyer, or both, when utilizing the network-based commerce system  10 . The database  30  may include listings tables  60  that may be linked to a user table  54 . The listings tables  60  may include a seller listings table  52  and a bidder listings table  58 . A user record in the user table  54  may be linked to multiple listings that are being, or have been, listed or offered for sale via the network-based commerce system  10 . A link may indicate whether the user is a seller or a bidder (or buyer) with respect to listings for which records exist within the listings tables  60 .  
      The listings may be arranged into divisions that, in one embodiment, are in the form of categories. Accordingly, the database  30  also includes one or more category tables  47 . Each record within the category table  47  may describe a respective category. Thus, in one embodiment, the system  10  provides the capability to arrange listings in one or more categories. These categories may be navigable (e.g. browsed) by a user of the network-based commerce system  10  to locate listings in specific categories. Thus, categories provide a mechanism to group and thus browse listings, in addition to locating listings using an alphanumeric search mechanism provided by the search servers  20 . In one embodiment, the category table  47  describes multiple, hierarchical category data structures, and includes multiple category records, each of which describes the context of a particular category within each one of the multiple hierarchical category structures. For example, the category table  47  may describe a number of real, or actual, categories to which listing records, within the listings tables  60 , may be linked.  
      The database  30  also includes one or more attributes tables  49 . Each record within an attributes table  49  may describe a respective attribute. In one embodiment, the attributes table  49  describes multiple, hierarchical attribute data structures, and includes multiple attribute records, each of which describes the context of a particular attribute within the multiple hierarchical attribute structures. For example, the attributes table  49  may describe a number of real, or actual, attributes to which listing records, within the listings tables  60 , may be linked. Also, the attributes table  49  may describe a number of real, or actual, attributes to which categories, within the category table  47 , may be linked.  
      The database  30  also includes a note table  46  populated with note records that may be linked to one or more listing records within the listings tables  60  and/or to one or more user records within the user table  54 . Each note record within the note table  46  may include, inter alia, a comment, description, history or other information pertaining to a listing being offered via the network-based commerce system  10 , or to a user of the network-based commerce system  10 .  
      A number of other exemplary tables are also shown to be linked to the user table  54 , namely a user past aliases table  48 , a feedback table  50 , a feedback details table  53 , a bids table  55 , an accounts table  64  and an account balances table  62 . The database  30  is also shown to include a batch table  42 , a batch listings table  40 , a listings wait table  44 , and a merchandising query table  45 .  
      One embodiment of the invention relates to generating listing recommendations based on a combination of past bidding/purchasing history and popular search phrases or terms (economic demand for listings) at the network-based commerce system  10 . Popular search phrases or terms may be computed in a data warehouse. For example, the data warehouse may identify the most frequently used or popular search phrases or terms across, for example, a selected number of sites associated with the network-based commerce system  10 . Frequently used or popular search terms may be stored in the data warehouse as data indicating which searches are most popular. Popular search terms may then be periodically retrieved by a production facility, e.g., on a daily basis, where the production facility may project the popular search terms against an inventory of listings. The projection may be based on a search process for each category at each level. All popular search terms that match at least a predetermined or selected number of listings (e.g., 50) listed within a category may be stored together with an identity of the matched listings. Thus, each category may have some number of popular search terms (from 0 to a predetermined or selected number) assigned to it. Further, a measurement indication of the popularity of the frequently used or popular search term, in a particular category, may also be provided.  
       FIG. 3A  is a diagram illustrating popular search phrase or term location logic  66 , according to an exemplary embodiment of the present invention, to determine and rank popular search terms to be utilized in recommending listings to users of the network-based commerce system  10 . The recommendation may be based on, for example, supply and demand of the listings. The popular search term location logic  66  includes popular search term retrieval module  67 , a popular search term criteria determination module  68 , a popular search term popularity determination module  69 , a popular search term assignment module  70 , and a popular search term ranking module  71 .  
      The popular search term retrieval module  67  is provided to retrieve popular search term from a memory location. The popular search term criteria determination module  68  may determine if the popular search term meets one or more a predetermined or selected criterion. The popular search term popularity determination module  69  is provided to determine the number of listings that will be returned in response to a search utilizing the popular search term, wherein each category of the network-based commerce system  10  may be searched and the number of listings returned is determined per category. The popular search term assignment module  70  may assign the popular search term that returned, or is associated with, a predetermined or selected number of listings per category to a second memory location. The popular search term ranking module  71  may rank popular search terms within the second memory location. In one embodiment, the popular search term ranking module  71  ranks the popular search term per category against other popular search terms within the popular search terms category. The popular search terms may be ranked in ascending or descending order. The first and second memory location may be provided in any database included within the system  10 .  
       FIG. 3B  is a diagram illustrating popular search term presentation logic  74 , according to an exemplary embodiment of the present invention, to provide recommendations to users of the network-based commerce system  10  based, for example, on the supply and demand of listings within the network-based commerce system  10 . The popular search term presentation logic  74  includes a popular search term category identification module  75 , an assigned popular search term retrieval module  76 , a popular search term listing identification module  77 , and a popular search term display module  78 .  
      The popular search term category identification module  75  may identify a category associated with a listing or listings that a user is interacting with (e.g. browsing, searching or the like) in the network-based commerce system  10 . The assigned popular search term retrieval module  76  may retrieve a predetermined number of popular search terms assigned to the category. The popular search term listing identification module  77  may identify one or more listings in the identified category that would be returned in response to a search utilizing one or more popular search terms. The popular search term display module  78  is provided to display the popular search terms as a hyperlink to listings identified in response to the search utilizing each of predetermined number of popular search terms. In one embodiment, a predetermined number of listings are associated with the hyperlink. The popular search terms may be displayed within a user interface, as described below with reference to  FIGS. 12A and 12B .  
      In one embodiment, a record of each popular search term is stored in a Popular Search Term table  70 , an example of which is provided in  FIG. 4A . The Popular Search Term table  70  is shown, by way of example, to include a Search_Term field, a Date_Of_Entry field, a Time_Of_Entry field, and a Site_ID field.  
      In one exemplary embodiment, information relating to ranking popular search terms with regard to the frequency with which they are attempted or used, is stored in a Popular Search_Term Ranking table  80 , an example of which is provided in  FIG. 4B . The Popular Search_Term Ranking table  80  is shown, by way of example, to include a Rank field, a Search_Term field, a Searches_Attempted field, and a Site_ID field.  
       FIG. 4C  shows an exemplary Popular Search Term list  88  that illustrates an example of the contents of the Popular Search Term Ranking table  80  (see  FIG. 4B ). The first column in the Popular Search Term list  88  provides a Rank  90  associated with the popular search terms included within the list  88 . The second column provides popular search terms  92  included within the list  88 . The third column provides a Number of Searches  94  attempted at the network-based commerce system  10  (or at multiple different systems) using the popular search terms  92 , for example, within a predetermined amount of time (e.g., the last two weeks). The fourth column provides a site identification or Site ID  96  associated with a site at which the popular search terms  92  are entered. In one embodiment, the network-based commerce system  10  may include multiple sites, wherein each site is identified by specific criterion (e.g., country, language, type of listings offered, etc.). In one embodiment, the Site ID  96  provides the identity of client machines where the network-based commerce system  10  exists within a peer-to-peer network.  
       FIG. 5  is a flowchart illustrating a method  100 , according to an exemplary embodiment of the present invention, of determining one or more popular search terms or phrases to be included within a group of preliminary search terms utilized by a network-based commerce system  10  in generating recommendations to a user interacting with listings in the network-based commerce system  10 .  
      At block  102 , a first popular search term is retrieved from the Popular Search Term Ranking table  80  (see  FIG. 4A ).  
      At decision block  104 , a determination is made as to whether the popular search term meets a predetermined threshold value or popularity. For example, a determination may be made as to whether the popular search term has been attempted or used a predetermined number of times (e.g., 10,000) within a designated or selected period of time (e.g., the previous two weeks). In one embodiment, only popular search terms that use no special characters (e.g., *, -, (,), etc.) are considered. In another exemplary embodiment, popular search terms that use special characters are also considered.  
      If the frequently used or popular search term does not meet the threshold value, then at decision block  106  a determination is made as to whether there are any other popular search terms in the Popular Search Term Ranking table  80 . If there are additional popular search terms in the Popular Search Term Ranking table  80 , then at block  108  the next popular search term is retrieved from the Popular Search Term Ranking table  80 . This process may be repeated until all popular search terms in the Popular Search Term Ranking table  80  have been considered.  
      Returning to decision block  104 , if a determination is made that the popular search term meets the threshold value (e.g., 10,000) then a determination is optionally made at decision block  110  as to whether the search term meets a length threshold (e.g., popular search term includes 3 or more words). However, in other embodiments of the invention a decision may be made at block  110  as to whether the popular search term meets a maximum length thereby to limit a maximum length of a popular search term. Restricting the length of a popular search term may ensure that the popular search term fits into an associated field of a navigation interface. However, in certain embodiments, no determination of the popular search term meeting a length threshold need be made.  
      At block  112 , popular search terms that meet the length threshold are stored in a Preliminary Popular Search Term table  114 .  FIG. 6  provides an exemplary embodiment of the Preliminary Popular Search Term table  114  that includes a Search_Term field.  
      The popular search phrases included within the Preliminary Popular Search Term table  114  may be filtered and then assigned to a category. In one embodiment, a record of each popular search term is stored in a Filtered Popular Search Term table  116 , an example of which is provided in  FIG. 7 .  
       FIG. 8  is a flowchart illustrating a method  120 , according to an exemplary embodiment of the present invention, of filtering popular search phrases or terms. At block  122 , a first popular search term is retrieved from the Preliminary Popular Search Term table  114 .  
      At block  124 , the popular search term is compared against a list of reference or filter words (e.g., Profane, Offensive, etc.). The list of filter words may be modified to add or remove filter words. The list of filter words may be stored in a table and, in one embodiment, the list of filter words is provided in a “dictionary” which is periodically updated (e.g. every 2 hours). A common dictionary (e.g. including words in multiple languages) may be provided for multiple international sites of the network-based commerce system  10 .  
      At decision block  126 , a determination is made as to whether the popular search term matches any of the words in the list of filter words.  
      At decision block  128 , if the popular search term does match one of the filter words, then a determination is made as to whether the end of the Preliminary Popular Search Term table  114  has been reached. If the end of the Preliminary Popular Search Term table  114  has been reached, the method  120  ends at block  130 . If the end of the Preliminary Popular Search Term table  114  has not been reached, at block  132 , the next popular search phrase or term is then retrieved.  
      Returning to decision block  126 , if a determination is made that the popular search term does not match any of the words in the list of filter words, then at block  134 , the popular search term is stored to the Filtered Popular Search Term table  116  (see  FIG. 7 ).  
      After filtering the popular search terms, a determination is made with regard to category assignment. As described below with reference to  FIG. 9 , in one embodiment category assignment is based on supply and demand of listings returned in response to searches within each category using each of the popular search phrases or terms.  
       FIG. 9  is a flowchart illustrating a method  140 , according to an exemplary embodiment of the present invention, of assigning each of the popular search terms to a category in the network-based commerce system  10 .  
      At block  142  a first popular search term from the Filtered Popular Search Term table  116  is retrieved.  
      At block  144  a first category of the network-based commerce system  10  is searched with the popular search term.  
      At decision block  146  a determination is made as to whether there are more than a predetermined number or occurrences of listings (e.g., 50 products and/or items in a particular category) returned as a result of the search utilizing the popular search term.  
      If less than the predetermined number of listings was returned at decision block  146 , then at decision block  148  a determination is made as to whether all categories within the network-based commerce system  10  have been searched using the popular search term. At decision block  148 , if all categories within the network-based commerce system  10  have been searched using the popular search term, then a determination is made at decision block  150  as to whether there are additional popular search terms in the Filtered Popular Search Term table  116 . If there are additional popular search terms, then at block  152  the next popular search term is retrieved from the Filtered Popular Search Term table  116  and the method  140  returns to block  144 . If the end of the Filtered Popular Search Term table  116  has been reached, then at block  160  the method  140  ends.  
      Returning to decision block  148 . If a determination is made that that all categories (or any number of selected categories or divisions) within the network-based commerce system  10  have not been searched using the popular search term, then at block  154  the next category within the network-based commerce system  10  is searched using the popular search term and the method  140  returns to decision block  146 .  
      Returning to decision block  146 , if more than a predetermined number or occurrences of listings within a category are returned, then at block  156  the filtered popular search term is assigned to an Approved Popular Search Phrase table  170  (see  FIG. 10 ). In addition, a record associated with the popular search phrase&#39;s Category Assignment field in table  170  is updated to reflect the identity of the category within which the listings (e.g., goods and/or services) were returned.  
      At block  158 , the popular search phrase or term is ranked against all other popular search terms associated within the category. In one exemplary embodiment, the popular search terms are ranked according to listings returned in response to a search using the popular search term. In one embodiment, the rank of a search term within a category is stored in a Rank_Within_Category field of the Filtered Popular Search Term table  116 .  
       FIG. 11  is a flowchart illustrating a method  180 , according to an exemplary embodiment of the present invention, of providing a user with the opportunity to view listings, for example, based on the supply and demand of the listings in the network-based commerce system  10 .  
      At block  182 , a category (or more than one category) in the network-based commerce system  10  is identified which is most closely related to an area or division of the system  10  within which the user is interacting (e.g., searching, browsing, etc.). In one embodiment, searches need not take place in a specific category. As a result, logic may be applied that will “guess” or ascertain what category within the network-based commerce system  10  is considered to be the most appropriate based on the user interaction.  
      An example of the assignment and identification of categories may be as follows when the popular search term or phrase is, for example, “paintball”: 
          Sports 
            Sporting Goods 
                Paintball 
                    Other Items     Markers     Barrels     Protective Gear     Tanks    
                    Other Sports    
               
               

      In one embodiment search terms corresponding to a category name are excluded. Thus, if a category name matches the search term, then the search term is excluded and thus not associated with that category and all children categories. Accordingly, in the present example, the popular search term “paintball” would only be associated with “Sports”, “Sporting Goods”, and “Other Sports” and the sub-category “Paintball” and its sub-category would be ignored.  
      At block  184 , the Approved Popular Search Term table  170  is accessed.  
      At block  186 , one or more popular search terms that are assigned to the category identified in block  182  are retrieved according to rank in ascending order up to a predetermined or selected number (e.g., 3) of popular search terms. In another exemplary embodiment, all popular search terms assigned to the category identified in block  182  are retrieved according to rank, in descending order, up to a predetermined number (e.g., 3) of popular search terms.  
      At block  188 , the popular search terms are displayed as links that, when selected by a user, return a predetermined number of listings, each associated with the popular search term selected. In one exemplary embodiment, the listings are returned as hyperlinks on a web page. Upon selection of the respective hyperlink, the user may view listings associated with the link. At block  190 , the method  180  ends.  
       FIG. 12A  is a user interface  194 , according to an exemplary embodiment of the present invention, to display selectable popular search phrases or terms to a user. The user interface  194  is in the form of a web page that presents groups of popular search terms that may be relevant to a user. The groups are displayed in an exemplary groups window  196 . Within the groups window  196 , the popular search terms displayed at block  188  of  FIG. 11  are found under “Popular Searches”.  
       FIG. 12B  illustrates an exploded view of the relevant groups window  196  included within the user interface  194  of  FIG. 12A . In one embodiment, the “Popular Searches” section in the groups window  196  shows no more than the top three results and no less than two results.  
      As mentioned above, popular search terms or phrases may be ranked within categories based on the number of items that are returned when the popular search term is run against the database  30 . An exemplary result in a “Consumer Electronics Category” may be ranked as follows: 
          1. Playstation 2 (5997)     2. DVD (5124)     3. Digital Camera (336)     4. iPod (55)        

      However, in one embodiment, even though users may search the term “DVD” more often than the term “Playstation 2”, the term “DVD” may have a lower ranking if there are fewer listings in that category associated with the popular search term or phrase. Accordingly, in one embodiment, the popular search terms or phrases are dependent upon supply or the number of listings provided that are associated with the search term. In particular, the ranking of the popular search phrases or terms would change over time as listings are added and removed from the network-based commerce system  10 . For example, in a network-based action facility, as listings or items (e.g., products including goods and/or services) are sold, their associated listings would be removed from the inventory of listings and hence the suggested links (e.g. “Related Items”, “Popular Searches”, “Related Stores”, and/or any other listing related links) may vary based on supply (the number of current listings) as well as demand (because listings are removed once they have been sold). Thus, in general, the network-based commerce system  10  in one embodiment provides the user with listing recommendations based on economic principles of supply and demand. The listing recommendations may be based on the use of popular or frequently used search terms that are ranked, as described above.  
       FIG. 13  shows a diagrammatic representation of a machine in the exemplary form of a computer system  200  within which a set or sequence of instructions, for causing the machine to perform any one of the methodologies discussed above, may be executed. In alternative embodiments, the machine may comprise a network router, a network switch, a network bridge, Personal Digital Assistant (PDA), a cellular telephone, a web appliance, set-top box (STB) or any machine capable of executing a sequence of instructions that specify actions to be taken by that machine.  
      The computer system  200  includes a processor  202 , a main memory  206  and a static memory  208 , which communicate with each other via a bus  224 . The computer system  200  may further include a video display unit  212  (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)). The computer system  200  also includes an alphanumeric input device  214  (e.g., a keyboard), a cursor control device  216  (e.g., a mouse), a disk drive unit  218 , a signal generation device  222  (e.g., a speaker) and a network interface device  210 .  
      The disk drive unit  218  includes a machine-readable medium  220  on which is stored a set of instructions or software  204  embodying any one, or all, of the methodologies described above. The software  204  is also shown to reside, completely or at least partially, within the main memory  206  and/or within the processor  202 . The software  204  may further be transmitted or received via the network interface device  210 . For the purposes of this specification, the term “machine-readable medium” shall be taken to include any medium which is capable of storing or encoding a sequence of instructions for execution by the machine and that cause the machine to perform any one of the methodologies of the present invention. The term “machine-readable medium” shall accordingly be taken to included, but not be limited to, solid-state memories, optical and magnetic disks, and carrier wave signals. Further, while the software is shown in  FIG. 13  to reside within a single device, it will be appreciated that the software  204  could be distributed across multiple machines or storage media, which may include the machine-readable medium.  
      In the foregoing detailed description, the method and system of the present invention has been described with reference to specific exemplary embodiments thereof. It will, however, be evident that various modifications and changes may be made thereto without departing from the broader spirit and scope of the present invention. In particular, the separate blocks of the various block diagrams represent functional blocks of methods or apparatuses and are not necessarily indicative of physical or logical separations or of an order of operation inherent in the spirit and scope of the present invention. The present specification and figures are accordingly to be regarded as illustrative rather than restrictive.