Patent Publication Number: US-2022230150-A1

Title: Application recommendation engine

Description:
CLAIM OF PRIORITY 
     This Application is a continuation of U.S. application Ser. No. 16/951,632, filed Nov. 18, 2020, which is a continuation of U.S. application Ser. No. 12/416,051, filed Mar. 31, 2009, now U.S. Pat. No. 10,984,397, each of which is hereby incorporated by reference in its entirety. 
    
    
     TECHNICAL FIELD 
     This application relates to the field of online commerce and, in one exemplary embodiment, to methods and systems of recommending software to users engaged in online commerce. 
     BACKGROUND 
     With the widespread acceptance of the Internet as a ubiquitous, interactive communication and interaction platform, online commerce conducted over the Internet has become commonplace in a variety of business environments, including auctions and fixed price item sales. A number of online marketplaces are utilized by merchants as an important, if not a primary, distribution channel for products. These “power sellers” typically list a large number of items to be sold or auctioned each day. 
     In order to manage a high volume of sales, power sellers rely on marketplace and payment applications. Some of these applications are provided by the marketplace itself, whereas others are written and sold by third party software developers. In order to discover these applications, especially third party ones, sellers typically have to search the Internet for them. Also, application writers must advertise their programs to make them easier to locate. As a result, sellers may not be aware of applications that could improve their ability to sell items, and they may not know which available applications will best meet their needs. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
       Embodiments of the present invention are illustrated by way of example, and not limitation, in the figures of the accompanying drawings, in which like reference numbers indicate similar elements. 
         FIG. 1  is a network diagram depicting a client-server system, within which one example embodiment of an application recommendation engine may be deployed. 
         FIG. 2  is a block diagram illustrating multiple applications that, in one example embodiment, are provided as part of the networked system. 
         FIG. 3  is a block diagram illustrating sources of user sales data in an example embodiment. 
         FIG. 4  is a block diagram illustrating pieces of listing data associated with an auction in one embodiment. 
         FIG. 5  is a block diagram illustrating component modules of a. recommendation engine, according to an example embodiment. 
         FIG. 6  is a block diagram illustrating an application recommendation engine, according to an example embodiment. 
         FIG. 7  is a block diagram illustrating an impact module of an application recommendation engine, according to one embodiment. 
         FIG. 8  is a flow chart of a method of recommending applications according to various example embodiments. 
         FIG. 9  is a flow chart of a method of profiling users from user sales data and listings in one example embodiment, 
         FIG. 10  is a flow chart of a method of determining the impact an application will have on a user&#39;s sales, according to an example embodiment. 
         FIG. 11  is a flow chart of a method of recommending an application to a user after impacts have been determined, according to an embodiment. 
         FIG. 12  is an image of an application recommended to a user within a user interface in an example embodiment. 
         FIG. 13  is a diagrammatic representation of a machine in the example form of a computer system within which set instructions for causing the machine to perform any one or more of the methodologies discussed herein may be executed. 
     
    
    
     DETAILED DESCRIPTION 
     In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of some example embodiments. It will be evident, however, to one skilled in the art that the present invention may be practiced without these specific details. Further, well-known instruction instances, protocols, structures, and techniques have not been shown in detail. 
     Some example embodiments enable business applications designed to improve the effectiveness of selling items on a network-based. trading environment to be recommended to appropriate sellers. Sellers are profiled based on the type and volume of items they sell in addition to detailed sales metrics. Their sales metrics are tracked for periods in which the sellers use specific applications in order to determine any gains that a particular application has on that type of seller. To recommend an application to a user, the user&#39;s profile is compared to the database of sellers to find similar profiles, and applications which best meet the user&#39;s needs are recommended. 
     In one example embodiment, a profile module calculates metrics from listings related to a specific user in order to create a sales profile of the user. An impact module assesses an impact on the calculated metrics by a number of applications, and a recommendation module suggests at least one of the applications based on the assessed impacts. A display module graphically displays the chosen application(s) to the user on a display device, and an update module updates a relevant portion of the user&#39;s listings using the application(s) chosen by the user. 
     According to some example embodiments, assessing the impact of an application on one of the metrics involves comparing sales profiles of sellers who have used the application to the user sales profile in order to discover which sellers have profiles similar to those of the user. Next, a prior sales metric is calculated for a first set of listings that were created prior to the use of the application, and an updated sales metric is calculated for a second set of listings created during the use of the application. The first and second sets of listings are compared with one another to assess the impact that the application had on the sales metric. 
     In one example embodiment, the user sales profile includes user sales data, which includes listing categories, listing keywords, application subscriptions, and a volume of sales. The metrics comprise at least one of profit margins, an average selling price, Detailed Seller Ratings (DSRs) and feedback, inventory turnover rate, accuracy of listings, communication with customers, average time to delivery, or shipping charges. 
     In some example embodiments, the listings consist of sales on a network-based marketplace and the applications are business applications designed to improve the effectiveness of selling items on a network-based. trading environment. 
     Additionally, in an example embodiment, the recommendation module may rank and recommend applications based on application ratings or user preferences. 
     The application recommendation engine connects a user to applications using context-sensitive user profile data. By assessing the impact that various applications have had on other sellers with a profile similar to the user&#39;s profile, appropriate applications can be recommended to the user that are most relevant to the user&#39;s needs. 
     Further details regarding the various example embodiments described above will now be discussed with reference to the figures accompanying the present specification. 
     Platform Architecture 
       FIG. 1  is a network diagram depicting a client-server system  100 , within which one example embodiment of an application recommendation engine  132  may be deployed. 
     A networked system  102 , in the example forms of a network-based marketplace or publication system, provides server-side functionality, via a network  104  (e.g., the Internet or Wide Area Network (WAN)) to one or more clients.  FIG. 1  illustrates, for example, a web client  106  (e.g., a browser, such as the Internet Explorer browser developed by Microsoft Corporation of Redmond, Wash. State), and a programmatic client  108  executing on respective client machines  110  and  112 . The client machines  110  and  112  have an associated display device  134  (e.g., a monitor) for viewing data. 
     An Application Program Interface (API) server  114  and a web server  116  are coupled to, and provide programmatic and web interfaces respectively to, one or more application servers  118 . The application servers  118  host one or more marketplace applications  120 , payment applications  122 , and the application recommendation engine  132 . The application servers  118  are, in turn, shown to be coupled to one or more database servers  124  that facilitate access to one or more databases  126 . 
     The one or more marketplace applications  120  may provide a number of marketplace functions and services to users that access the networked system  102 . The payment applications  122  may likewise provide a number of payment services and functions to users. The payment applications  122  may allow users to accumulate value (e.g., in a commercial currency, such as the U.S. dollar, or a proprietary currency, such as “points”) in accounts, and then later to redeem the accumulated value for products (e.g., goods or services) that are made available via the marketplace applications  120 . While the marketplace applications  120  and payment applications  122  are shown in  FIG. 1  to both form part of the networked system  102 , it will he appreciated that, in alternative embodiments, the payment applications  122  may form part of a payment service that is separate and distinct from the networked system  102 . 
     Further, while the client-server system  100  shown in  FIG. 1  employs a client-server architecture, the present invention is, of course, not limited to such an architecture, and could equally well find application in a distributed, or peer-tip-peer, architecture system, for example. The various marketplace  120  and payment  122  applications could also be implemented as standalone software programs operating under separate hardware platforms, which do not necessarily have networking capabilities. 
     The web client  106  accesses the various marketplace  120  and payment applications  122  via the web interface supported by the web server  116 . Similarly, the programmatic client  108  accesses the various services and functions provided by the marketplace and payment applications  120  and  122  via the programmatic interface provided by the API server  114 . The programmatic client  108  may, for example, be a seller application (e.g., the TurboLister application developed by eBay Inc., of San Jose, Calif.) to enable sellers to author and manage listings on the networked system  102  in an offline manner, and to perform batch-mode communications between the programmatic client  108  and the networked system  102 . 
       FIG. 1  also illustrates a third party application  128 , executing on a third party server  130 , as having programmatic access to the networked system  102  via the programmatic interface provided by the API server  114 . For example, the third party application  128  may, utilizing information retrieved from the networked system  102 , support one or more features or functions on a website hosted by the third party. The third party website may, for example, provide one or more promotional, marketplace, or payment functions that are supported by the relevant applications of the networked system  102 . 
     Marketplace Applications 
       FIG. 2  is a block diagram illustrating multiple applications that, in one example embodiment, are provided as the marketplace applications  120  of the networked system  102  (see  FIG. 1 ). 
     The one or more marketplace applications  120  may be hosted on dedicated or shared server machines (not shown) that are communicatively coupled to enable communications between the server machines. The applications themselves are communicatively coupled (e.g., via appropriate interfaces), either directly or indirectly, to each other and to various data sources, so as to allow information to be passed between the applications or so as to allow the applications to share and access common data. The applications may furthermore access the one or more databases  126  via the one or more database servers  124 , both shown in  FIG. 1 . 
     The networked system  102  may provide a number of publishing, listing, and price-setting mechanisms whereby a seller may list (or publish information concerning) goods or services for sale, a buyer can express interest in or indicate a desire to purchase such goods or services, and a price can be set for a transaction pertaining to the goods or services. To this end, the marketplace applications  120  are shown to include at least one publication application  240  and one or more auction applications  242  which support auction-format listing and price setting mechanisms (e.g., English, Dutch, Vickrey, Chinese, Double, Reverse auctions, etc. known independently in the art). The various auction applications  242  may also provide a number of features in support of such auction-format listings, such as a reserve price feature whereby a seller may specify a reserve price in connection with a listing and a proxy-bidding feature whereby a bidder may invoke automated proxy bidding. 
     A number of fixed-price applications  244  support fixed-price listing formats (e.g., the traditional classified advertisement-type listing or a catalogue listing) and buyout-type listings. Specifically, buyout-type listings (e.g., including the Buy-It-Now (BIN) technology developed by eBay Inc., of San Jose, Calif.) may be offered in conjunction with auction-format listings, and allow a buyer to purchase goods or services, which are also being offered for sale via an auction, for a fixed-price that is typically higher than the starting price of the auction. 
     Store applications  246  allow a seller to group listings within a “virtual” store, which may be branded and otherwise personalized by and for the seller. Such a virtual store may also offer promotions, incentives, and features that are specific and personalized to a relevant seller. 
     Reputation applications  248  allow users that transact, utilizing the networked system  102 , to establish, build, and maintain reputations, which may be made available and published to potential trading partners. Consider that where, for example, the networked system  102  supports person-to-person trading, users may otherwise have no history or other reference information whereby the trustworthiness and credibility of potential trading partners may be assessed. The reputation applications  248  allow a user, for example through feedback provided by one or more other transaction partners, to establish a reputation within the networked system  102  over time. Other potential trading partners may then reference such a reputation for the purposes of assessing credibility and trustworthiness. 
     Personalization applications  250  allow users of the networked system  102  to personalize various aspects of their interactions with the networked system  102 . For example a user may, utilizing an appropriate one of the personalization applications  250 , create a personalized reference page from which information regarding transactions to which the user is (or has been) a party, may be viewed. Further, the personalization applications  250  may enable a user to personalize listings and other aspects of their interactions with the networked system  102  and other parties outside of the networked system  102 . 
     The networked system  102  may support a number of marketplaces that are customized, for example, to specific geographic regions. A version of the networked system  102  may be customized for the United Kingdom, whereas another version of the networked system  102  may be customized for the United States. Each of these versions may operate as an independent marketplace, or may be customized (or internationalized) presentations of a common underlying marketplace. The networked system  102  may, accordingly, include a number of internationalization applications  252  that customize information (and/or the presentation of information) by the networked system  102  according to predetermined criteria (e.g., geographic, demographic, or marketplace criteria). For example, the internationalization applications  252  may be used to support the customization of information for a number of regional websites that are operated by the networked system  102  and that are accessible via a respective web server  116 , (see  FIG. 1 ). 
     Navigation of the networked system  102  may be facilitated by one or more navigation applications  254 . For example, a search application (as an example of a navigation application) may enable key word searches of listings published via the networked system  102 . A browse application may allow users to browse various category, catalogue, or inventory data structures according to which listings may be classified within the networked system  102 . Various other navigation applications may be provided to supplement the searching and browsing applications. 
     In order to make listings available via the networked system  102  as visually informing and attractive as possible, the marketplace applications  120  may include one or more imaging applications  256 , which enable users to upload images for inclusion within listings. The imaging applications  256  also operate to incorporate images within viewed listings. The imaging applications  256  may also support one or more promotional features, such as image galleries that are presented to potential buyers. For example, sellers may pay an additional fee to have an image included within a gallery of images for promoted items. 
     Listing creation applications  258  allow sellers conveniently to author listings pertaining to goods or services that they wish to transact via the networked system  102 , and listing management applications  260  allow sellers to manage such listings. Specifically, where a particular seller has authored and/or published a large number of listings, the management of such listings may present a challenge. The listing management applications  260  provide a number of features (e.g., auto-relisting, inventory level monitors, etc.) to assist the seller in managing such listings. One or more post-listing management applications  262  also assist sellers with a number of activities that typically occur post-listing. For example, upon completion of an auction facilitated by one or more auction applications  242 , a seller may wish to leave feedback regarding a particular buyer. To this end, the post-listing management applications  262  may provide an interface to the reputation applications  248 , so as to allow the seller conveniently to provide feedback regarding multiple buyers to the reputation applications  248 . 
     Dispute resolution applications  264  provide mechanisms whereby disputes arising between transacting parties may be resolved. For example, the dispute resolution applications  264  may provide guided procedures whereby the parties are guided through a number of steps in an attempt to settle a dispute. In the event that the dispute cannot be settled via the guided procedures, the dispute may be escalated to a third party mediator or arbitrator. 
     A number of fraud prevention applications  266  implement fraud detection and prevention mechanisms to reduce the occurrence of fraud within the networked system  102 . 
     Messaging applications  268  are responsible for the generation and delivery of messages to users of the networked system  102 . Such messages include, for example, advising users regarding the status of listings at the networked system  102  (e.g., providing “outbid” notices to bidders during an auction process or to provide promotional and merchandising information to users). Respective ones of the messaging applications  268  may utilize any one of a number of message delivery networks and platforms to deliver messages to users. For example, the messaging applications  268  may deliver electronic mail (e-mail), instant message (IM), Short Message Service (SMS), text, facsimile, or voice (e.g., Voice over IP (VoIP))) messages via the wired (e.g., the Internet), Plain Old Telephone Service (POTS), or wireless (e.g., mobile, cellular, WiFi, WiMAX) networks. 
     Merchandising applications  270  support various merchandising functions that are made available to sellers to enable sellers to increase sales via the networked system  102 . The merchandising applications  270  also operate the various merchandising features that may he invoked by sellers, and may monitor and track the success of merchandising strategies employed by sellers. 
     The networked system  102  itself, or one or more parties that transact via the networked system  102 , may operate loyalty programs that are supported by one or more loyalty/promotions applications  272 . For example, a buyer may earn loyalty or promotions points for each transaction established and/or concluded with a particular seller, and be offered a reward for which accumulated loyalty points can be redeemed. 
       FIG. 3  is a block diagram illustrating sources of user sales data  300  in an example embodiment. The user sales data  300  includes categories  302  in which the user has listed items for sale, such as the art category  304  or the books category  306 . The user sales data  300  also includes the user&#39;s current application subscriptions  308 , which may include application  1 - 310  and application  2 - 312 . Keywords  314  are searchable descriptive terms associated with the user&#39;s listings, such as paintings  316  and prints  318 . Additionally, the user sales data  300  includes sales volume statistics  320 . These sales volume statistics  320  include total amount of items sold, items sold per month, items sold per year, etc. The user sales data  300  is one tool useful for user segmentation and user profiling, which consists of identifying similar types of users and treating them similarly. 
       FIG. 4  is a block diagram illustrating pieces of listing data  400  associated with an auction or fixed sale of an item in one embodiment. The listing data  400  represents the item being sold and adds information that is used to compile statistics to calculate sales metrics and the user sales data  300 . The listing data  400  includes a title  402  for the listing that is displayed to potential buyers as a summary of what the item is. The description  404  includes text and graphics that describe the item being sold and any instructions that the seller has, such as payment options, shipping costs, etc. The category  406  is the section of the online marketplace where the item is being sold. The category is useful for both searching for items and for compiling the user sales data  300 . The user identifier  408  is a unique text string for each user on the marketplace that identifies who the seller is. Potential buyers can search for previous sales made by the same seller or contact the seller by knowing the user identifier  408 . The sales format status  410  indicates whether the listing is a fixed price sale, a type of auction, or potentially both, Auctions include a start of auction time  416  that normally begins when a seller listings an item. However, sellers may also choose a start of auction time  416  in the future, which is useful for selling multiple items. Fixed priced sales have a purchase price  412  that a buyer must pay for the item, whereas auctions have a minimum bid  418 . If a buyer places a bid on the item and no one outbids the buyer before the auction complete time  420 , the buyer&#39;s bid is the winning bid  414  and the auction is complete. Potential buyers will see the current highest bid  422  when viewing the listing and may outbid the original buyer before the auction complete time  420 . The shipping region  42 . 4  is where the seller is willing to ship an item and may also include shipping services and the item&#39;s origin. 
       FIG. 5  is a block diagram illustrating component modules of an application recommendation engine  132  ( FIG. 1 ) according to an example embodiment. The application recommendation engine  132  includes a profile module  502  for calculating metrics and creating user sales profiles, an impact module  504  for assessing impacts on the metrics by applications, and a recommendation module  506  to recommend applications based on the impacts. The application recommendation engine  132  also includes an update module  510  for updating the listings with the recommended application. 
       FIG. 6  is a block diagram illustrating the interaction between the modules of an application recommendation engine  132  ( FIG. 1 ), according to an example embodiment. A user&#39;s listings  604  are retrieved from a listing database  602  and sent to a calculation module  606  to compute sales metrics  608 . The sales metrics  608  include metrics such as profit margins, average selling prices, Detailed Seller Ratings (DSRs), feedback, inventory turnover rates, accuracy of listings, communication with customers, average time to delivery, and shipping charges. The user sales data  300  (see  FIG. 3 ) is retrieved from a sales database  610 . The profile module  502  (see  FIG. 5 ) uses the sales metrics  608  and the user sales data  300  to create a user sales profile  614 , which is fed into the impact module  504  (see  FIG. 5 ). The impact module  504  also retrieves applications, seller information, and listings from an application database  616 , a seller database  618 , and the listing database  602  in order to calculate impacts on the sales metrics  608  for the applications. These impacts  620 ,  622 , and  624  are used by the recommendation module  506  (see  FIG. 5 ) to determine one or more recommendations  626  for the user, such as application  3   628  and application  4   630 . The recommendations  626  are communicated to the user, and the user has a choice of adding the application(s) or not. If an application is chosen, the update module  510  (see  FIG. 5 ) is responsible for updating the user&#39;s listings  604  with the application. 
       FIG. 7  is a block diagram illustrating the impact. module  504  (see  FIG. 5 ) of the application recommendation engine  132  (see  FIG. 1 ), according to an example embodiment. For each application  310  (see  FIG. 3 ) retrieved from the application database  616 , the profile comparison module  708  retrieves seller predetermined profiles  702 ,  704 , and  706  from the seller database  618  and compares the predetermined profiles with the user sales profile  614 . For each of the sellers  702 ,  704 , and  706  with a similar profile to the user in question, the seller&#39;s associated listings are retrieved from the listing database  602 . For example, if the profile of the first seller is similar to the profile of the user, the first seller listings are retrieved from the listing database  602  in the seller  1  listing block  710 . The seller&#39;s prior listings  712  and listings using the application in question  714  are compared by the listing comparison module  716 , which results in an impact  620  for that application. 
       FIG. 8  is a flow chart of a method  800  of recommending applications according to some example embodiments. While operations of the method  800  are described below as being performed by specific components, modules, or systems of the client-server system  100  (see  FIG. 1 ), it will be appreciated that these operations need not necessarily be performed by the specific components identified, and could be performed by a variety of components and modules, potentially distributed over a number of machines. Alternatively, at least certain ones of the variety of components and modules described herein can be arranged within a single hardware, software, or firmware component. 
     The method  800  commences at operation  802  and proceeds to operation  804 , where in an example embodiment, a user sales profile is created by combining user sales data  300  (see  FIG. 3 ) and sales metrics  608  from listings  604  (see  FIG. 6 ) related to a user. At operation  806 , impacts on the sales metrics  608  are assessed for a portion of the applications to determine which applications may have the most positive impact on the user, at least one of which is suggested at operation  808 . If the user chooses to add one of the chosen applications, the user&#39;s listings  604  are updated with the application at operation  810 . The method  800  ends at operation  812 . 
       FIG. 9  is a detailed flow chart of operation  804  (see  FIG. 8 ), in one embodiment, of profiling users from user sales data  300  (see  FIG. 3 ) and listings  604  (see  FIG. 6 ). The detailed operation  804  commences at operation  902  and proceeds to operation  904  where the user sales data  300  are retrieved from the sales database  610  (see  FIG. 6 ). At operation  906 , the user&#39;s listings  604  are retrieved from the listing database  602  (see  FIG. 6 ). Using the listings  604 , current values of the sales metrics  608  (see  FIG. 6 ) for the user are calculated to determine which areas a user can improve in order to maximize sales potential (operation  908 ). Some of the sales metrics  608  are calculated using standard statistical functions such as simple linear regression or correlations applied to the relevant data. Other metrics, such as the DSR, already have fixed values assigned based on the seller&#39;s listing history. At operation  910 , the user sales data  300  and sales metrics  608  are combined to form a user sales profile. The method of creating a user sales profile ends at operation  912 . 
       FIG. 10  is a detailed flow chart of operation  806  (see  FIG. 8 ), according to an example embodiment, of determining the impact applications will have on a user&#39;s sales. The relative performance of an individual user is measured against the average performance indices for comparable sellers in terms of user sales data  300 , and the difference before and after adoption of the given application is calculated. The absolute performance of an individual user is measured by calculating the weighted-average, before and after adoption of the application, across all sellers who use the given application. In an example embodiment, the impact includes both a relative performance impact and an absolute performance impact. 
     The detailed operation  806  commences at operation  1002  and proceeds to operation  1004  where a list of applications is retrieved from the application database  616  (see  FIG. 6 ). These applications may be the third party application  128  (see  FIG. 1 ) hosted on third party servers  130  (see  FIG. 1 ), or they may be marketplace applications  120  (see  FIG. 2 ) hosted within the networked system  102  (see  FIG. 1 ). Example applications may help a user manage large numbers of pending auctions, track expenses and profits, create a loyalty program, or automate shipping. In one example embodiment, a list of all applications within the client-server system  100  is retrieved from the application database  616 . Alternatively, the list may include only applications that match a user&#39;s preferences or meet other criteria, such as having a. high rating. For example, a user may only desire applications to improve shipping so only applications related to shipping would be retrieved from the application database  616 . 
     In one example embodiment, application explicit ratings are determined by user surveys before and after subscribing to an application. Before subscription, users indicate why they are subscribing to a particular application (measuring expectations) and after a period of time, the users are again surveyed to determine whether their expectations are satisfied. Each application receives a rating based on how well it meets expectations. Sellers may also rate other aspects of the application such as “ease of use,” “reliability,” and “availability of customer support.” Additionally, the percentage of sellers who renew their subscription to a given application is stored as the application implicit rating. Finally, an editor with access to the system assigns an editor rating to each application based on one or more the following criteria: security and privacy, availability of infrastructure, quality of customer service, ease of use, software quality, and value. 
     For each application on the list, the impact module  504  (see  FIG. 5 ) retrieves a list of sellers and the sellers&#39; profiles from the seller database  618  who are current or former subscribers of the application (operation  1006 ). At operation  1008 , the profiles of the retrieved sellers are compared to the user sales profile in order to generate a filtered list of sellers who have profiles similar to the user. Similarity is determined by matching user sales data  300  (see  FIG. 3 ) and weighing the differences between the sales metrics  608  (see  FIG. 6 ). At operation  1010 , for each seller on the filtered list, the seller&#39;s listings are retrieved from the listing database  602  (see  FIG. 6 ) and divided into two relevant categories: the prior listings  712  (see  FIG. 7 ) created before the use of the application and listings using the application in question  714  (see  FIG. 7 ). In some embodiments, these listings may be further filtered by user sales data  300  such as categories  302  or keywords  314  (both shown in  FIG. 3 ) in order to only compare the most relevant listings. Also, only listings created during a specific time frame may be chosen to avoid using stale data. 
     At operation  1012 , sales metrics for the prior listings  712 . are calculated, and at operation  1014 , sales metrics for listings using the application in question  714  are calculated. The listing comparison module  716  (see  FIG. 7 ) computes the impact for that seller by comparing the difference between the seller&#39;s sales metrics for the set of prior listings  712  and the set of listings using the application in question  714  (operation  1016 ). At decision  1018 , a determination is made whether there are any remaining sellers on the filtered list, and operations  1010  through  1016  are repeated if so. If not, an aggregate impact for the application is compiled from all the sellers&#39; impacts. A determination is made whether there are remaining applications in the list of applications. If so, operations  1006  through  1020  are repeated. The method of determining the impact applications will have on a user&#39;s sales ends at operation  1024 . 
       FIG. 11  is a detailed flow chart of operation  808  (see  FIG. 8 ), according to one embodiment, of recommending an application to a user after impacts have been determined. The detailed operation  808  begins at operation  1102  and proceeds to operation  1104  where the application impacts  620 ,  622 , and  62 . 4  (each shown in  FIG. 6 ) and the ratings are compared to determine which applications will best improve the user&#39;s sales metrics. The absolute performance impacts, relative performance impacts, explicit rating, implicit rating, and editor rating are normalized and sent to a rating function R, which outputs a recommendation score for each application. 
     At operation  1106 , a user&#39;s preferences are retrieved in order to refine the recommendation process. For example, a user may be more concerned with improving profit margins or detailed seller ratings, whereas another user may care more about increasing sales volume. At operation  1102 , the user preferences are applied to the recommendation scores to recommend the best applications to the user. The best applications are the ones with the greatest positive recommendation score in areas that the user prefers to improve. The method of recommending applications ends at operation  1110 . 
       FIG. 12  is an image of an application recommended to a user within a user interface in an example embodiment. In this example, a user which has sold 10,000 items is being recommended the terapeak marketplace research application in order to get up to date information about inventory value, increase sales, and boost profits. For this application to be recommended, other sellers with similar user sales data  300  (see  FIG. 3 ) and sales metrics to the current user improved their sales and profits with terapeak. 
     Example Machine Architecture and Machine-readable Medium 
       FIG. 13  is a diagrammatic representation of a machine in the example form of a computer system  1300  within which instructions for causing the machine to perform any one or more of the methodologies discussed herein may be executed. In alternative embodiments, the machine operates as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the machine may operate in the capacity of a server or client devices in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine may be a personal computer (PC), a tablet PC, a set-top box (SIB), a Personal Digital Assistant (PDA), a cellular telephone, a web appliance, a network router, switch or bridge, or any machine capable of executing instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein. 
     The example computer system  1300  includes a processor  1302  (e.g., a central processing unit (CPU), a graphics processing unit (GPU) or both), a main memory  1304  and a static memory  1306 , which communicate with each other via a bus  1308 . The computer system  1300  may further include a video display unit  1310  (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)). The computer system  1300  also includes an alphanumeric input device  1312  (e.g., a keyboard), a user interface (UI) navigation device  1314  (e.g., a mouse), a disk drive unit  1316 , a signal generation device  1318  (e.g., a speaker) and a network interface device  1320 . 
     Machine-Readable Medium 
     The disk drive unit  1316  includes a machine-readable medium  1322  on which is stored one or more sets of instructions  1324  and data structures (e.g., software) embodying or utilized by any one or more of the methodologies or functions described herein. The instructions  1324  may also reside, completely or at least partially, within the main memory  1304  and/or within the processor  1302  during execution thereof by the computer system  1300 , the main memory  1304  and the processor  1302  also constituting machine-readable media. 
     While the machine-readable medium  1322  is shown in an example embodiment to be a single medium, the term “machine-readable medium” may include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more instructions or data structures. The term “machine-readable medium” shall also be taken to include any tangible medium that is capable of storing, encoding or carrying instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present invention, or that is capable of storing, encoding or carrying data structures utilized by or associated with such instructions. The term “machine-readable medium” shall accordingly be taken to include, but not be limited to, solid-state memories and optical and magnetic media. Specific examples of machine-readable media include non-volatile memory, including by way of example semiconductor memory devices, e.g., erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. 
     Transmission Medium 
     The instructions  1324  may further be transmitted or received over a communications network  1326  using a transmission medium. The instructions  1324  may be transmitted using the network interface device  1320  and any one of a number of well-known transfer protocols (e.g., Hypertext Transfer Protocol (HTTP)). Examples of communication networks include a local area network (LAN), a wide area network (WAN), the Internet, mobile telephone networks, Plain Old Telephone Service (POTS) networks, and wireless data networks (e.g., WiFi and WiMax networks). The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding, or carrying instructions for execution by the machine, and includes digital or analog communications signals or other intangible medium to facilitate communication of such software. 
     Modules, Components, and Logic 
     Certain embodiments are described herein as including logic or a number of components, modules, or mechanisms. A component is a tangible unit capable of performing certain operations and may be configured or arranged in a certain manner. In example embodiments, one or more computer systems (e.g., a standalone, client or server computer system) or one or more components of a computer system (e.g., a processor or a group of processors) may be configured by software (e.g., an application or application portion) as a component that operates to perform certain operations as described herein. 
     In various embodiments, a component may be implemented mechanically or electronically. For example, a component may comprise dedicated circuitry or logic that is permanently configured (e.g., as a special-purpose processor) to perform certain operations. A component may also comprise programmable logic or circuitry (e.g., as encompassed within a general-purpose processor or other programmable processor) that is temporarily configured by software to perform certain operations. It will be appreciated that the decision to implement a component mechanically, in dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software), may be driven by cost and time considerations. 
     Accordingly, the term “component” should be understood to encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired) or temporarily configured (e.g., programmed) to operate in a certain manner and/or to perform certain operations described herein. Considering embodiments in which components are temporarily configured (e.g., programmed), each of the components need not be configured or instantiated at any one instance in time. For example, where the components comprise a general-purpose processor configured using software, the general-purpose processor may be configured as respective different components at different times. Software may accordingly configure a processor, for example, to constitute a particular component at one instance of time and to constitute a different component at a different instance of time. 
     Components can provide information to, and receive information from, other components. Accordingly, the described components may be regarded as being communicatively coupled. Where multiple of such components exist contemporaneously, communications may be achieved through signal transmission (e.g., over appropriate circuits and buses) that connect the components. in embodiments in which multiple components are configured or instantiated at different times, communications between such components may be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple components have access. For example, one component may perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. A further component may then, at a later time, access the memory device to retrieve and process the stored output. Components may also initiate communications with input or output devices, and can operate on a resource (e.g., a collection of information). 
     Although certain specific example embodiments are described herein, it will be evident that various modifications and changes may be made to these embodiments without departing from the broader spirit and scope of the invention. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense. The accompanying drawings that form a part hereof, show by way of illustration, and not of limitation, specific embodiments in which the subject matter may be practiced. The embodiments are described and illustrated in sufficient detail to enable those skilled in the art to practice the teachings disclosed herein. Other embodiments may be used and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. This Detailed Description, therefore, is not to be taken in a limiting sense, and the scope of various embodiments is defined only by the appended claims, along with the full range of equivalents to which such claims are entitled. 
     Such embodiments of the inventive subject matter may be referred to herein, individually and/or collectively, by the term “invention” merely for convenience and without intending to voluntarily limit the scope of this application to any single invention or inventive concept if more than one is in fact disclosed. Thus, although specific embodiments have been illustrated and described herein, it should be appreciated that any arrangement calculated to achieve the same purpose may be substituted for the specific embodiments shown. This disclosure is intended to cover any and all adaptations or variations of various embodiments. Combinations of the above embodiments, and other embodiments not specifically described herein, will be apparent to those of skill in the art upon reviewing the above description.