Patent Publication Number: US-2011078017-A1

Title: Systems and methods for rating an originator of an online publication

Description:
TECHNICAL FIELD 
     The subject matter relates to the field of online publishing. More specifically, but not by way of limitation, claimed subject matter discloses techniques for rating originators of information published in a network. 
     BACKGROUND 
     All types of content, including advertisements, art, media, literary works, editorials, and the like, are made available for private and public consumption via computer networks such as the Internet. Those who receive the information published on networks such as the Internet may or may not be familiar with the originators of the information. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Some embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings in which: 
         FIG. 1  is a network diagram depicting an example of a network, within which example embodiments may be deployed; 
         FIG. 2  is a block diagram illustrating multiple modules that may be employed by a networked system, in accordance with an example embodiment; 
         FIG. 3  is a block diagram illustrating examples of Web interfaces, in accordance with an example embodiment; 
         FIG. 4  is a block diagram illustrating a further example of a Web interface, in accordance with an example embodiment; 
         FIG. 5  is an entity-interaction diagram illustrating examples of tables, in accordance with example embodiment; 
         FIG. 6  is a table illustrating an example of a publication rating table, in accordance with an example embodiment; 
         FIG. 7  is a table illustrating an example of a rating rules table, in accordance with an example embodiment; 
         FIG. 8  is a table illustrating an example of an originator rating table, in accordance with an example embodiment; 
         FIG. 9  is a flow diagram illustrating an example of a method for rating an originator of a publication, in accordance with an example embodiment; and 
         FIG. 10  shows a diagrammatic representation of machine in the example form of a computer system, in accordance with an example embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     Example systems and methods for rating an originator of a publication are described. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of example embodiments. It will be evident, however, to one skilled in the art that the claimed subject matter may be practiced without these specific details. 
     Online publications such as online classified advertisements may be posted via the Internet on Web pages hosted by a networked publication system. Some sellers of goods and/or services who utilize the publication system may rarely post an advertisement for an item for sale, while other sellers may regularly post advertisements for items. Potential buyers may interact with and provide user input to Web pages presenting the posted advertisements, and in some example embodiments, the user inputs may be tracked and later used to calculate ratings for advertisements. Some user input types may include user selections on Web pages to view advertisements, watch advertisements, or recommend advertisements. 
     Various example embodiments disclosed herein describe calculating a publication rating that rates the effectiveness of each posted advertisement based on the number and type of user inputs associated with each of the advertisements. In some example embodiments, a seller may post several advertisements, and the publication rating associated with the seller&#39;s posted advertisements may be used to calculate an originator rating, which may rate the trustworthiness of the seller. Example structures and methodologies for practicing the claimed subject matter are described in more detail below. 
       FIG. 1  is a network diagram depicting an example of a network  100 , within which example embodiments may be deployed. A networked system  102 , in the example form of a network-based publication or marketplace system, may provide 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 . 
     A module interface  114  (e.g., an Application Program Interface (API) server) and a web interface  116  (e.g., a web server) are communicatively coupled to, and provide interfaces to, system machines  118 . The system machines  118  host one or more publication modules  120  and transaction modules  122 . The system machines  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 publication modules  120  and the transaction modules  122  may exist in a production environment, where the modules  120  and  122  provide functions and services associated with actual commercial or non-commercial activity relating to subject matter of value and real users or entities. Alternatively or additionally, the publication modules  120  and the transaction modules  122  may exist in a testing environment (e.g., testing of API calls) associated with fictitious commercial activity relating to fictitious subject matter and fictitious users or entities. 
     In various example embodiments, the publication modules  120  may provide a number of marketplace functions and services to users that access the networked system  102 . The transaction modules  122  may in some embodiments provide a number of payment services and functions to the users. The transaction modules  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 publication modules  120 . 
     While the publication and transaction modules  120  and  122  are shown in  FIG. 1  to form part of the networked system  102 , it will be appreciated that, in alternative embodiments, the transaction modules  122  may form part of a payment service that is separate and distinct from the networked system  102 . For some example embodiments, parties may settle payments without the use of transaction modules  122  or networked payment service, such as by settling payment by email, conventional mail, or in person. 
     Further, while the networked system  100  shown in  FIG. 1  employs client-server architecture, the present subject matter is, of course, not limited to such an architecture, and could equally well find application in a distributed, or peer-to-peer, architecture system, for example. The various publication and transactions modules  120  and  122  could also be implemented as standalone software programs, which do not necessarily have networking capabilities. 
     The programmatic client  108  may access the various services and functions provided by the publication and transaction modules  120  and  122  via the module interface  114 . In some example embodiments, the programmatic client  108  may allow a user operating the client machine  112  to originate online publications. The online publications may be of any type, including online publications for classified advertisements or auction item listings. For example, programmatic client  108  may 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 off-line manner, and to perform batch-mode communications between the programmatic client  108  and the networked system  102 . 
     The web client  106  may access the various publication and transaction modules  120  and  122  via the web interface  116 . For some example embodiments, a user of the client machine  110  may use the Web client  106  to view online publications (e.g., classified advertisements) originated by a user of the client machine  112  and/or originated by other sources. In an example embodiment, the user of the client machine  110  may view, via the Web client  106 , ratings of online publications and/or ratings of online publication originators that have been generated by the publication modules  120  and the transaction modules  122 . As discussed in more detail below, the ratings may relate, for example, to an effectiveness of an online publication or to trustworthiness of an originator. 
     The following discussion below includes descriptions of example structures and functions of the various modules that may be operated by the system machines  118 . 
       FIG. 2  is a block diagram illustrating publication and transaction modules  120  and  122  of  FIG. 1  that, in some example embodiments, are provided as part of the networked system  102  of  FIG. 1 . Although some particular modules are described, it may be noted that a fewer or greater number of modules  120  and/or  122  may be employed by the networked system  102 , and that more or less functionality may be provided by the example modules  120  and  122 . For example, some networked systems  102  may provide payment service functionality. 
     The modules  120  and  122  may be communicatively coupled (e.g., via appropriate interfaces) to each other and to various data sources, so as to allow information to be passed between the modules  120  and  122  or so as to allow the modules  120  and  122  to share and access common data. The modules  120  and  122  may furthermore access one or more databases  126 , for example, via the database server(s)  124  of  FIG. 1 , to retrieve data (e.g., from tables) for processing. 
     In some example embodiments, the modules  120  and  122  may be operated on dedicated or shared machines (not shown) that are communicatively coupled to enable communications. It may be noted that the modules  120  and  122  may be implemented with hardware, software, and/or a combination of hardware and software. 
     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 modules  120  and  122  are shown to include at least one publication module  200  to publish information so as to allow users to receive online publications over the network  104  of  FIG. 1 . In an example embodiment, the publication module  200  may post online publications such as classified listings on Web pages that are served to clients via the Web interface  116 . Example listings and online publications are discussed further below with respect to the  FIGS. 3 and 4 . 
     A number of fixed-price modules  204  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. 
     The modules  120  and  122  may include one or more auction modules  202  which support auction-format listing and price setting mechanisms (e.g., English, Dutch, Vickrey, Chinese, Double, Reverse auctions etc.). The various auction modules  202  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. 
     Listing creation modules  218  may allow sellers conveniently to author listings pertaining to goods or services that they wish to transact via the networked system  102 . 
     Further, in the publication and transaction modules  120  and  122  shown in  FIG. 2 , one or more feedback modules  222  may also assist originators or sellers with a number of activities that typically occur post-listing. For example, upon completion of an auction facilitated by one or more auction modules  202 , a seller may wish to leave feedback regarding a particular buyer. To this end, a feedback module  222  may provide an interface to one or more reputation modules  208 , so as to allow the seller conveniently to provide feedback regarding multiple buyers to the reputation modules  208 . For some example embodiments, feedback provided by a user may be considered subjective information about another user. For example, subjective information provided by a buyer may be distinguished from a buyer&#39;s interactions or behavior associated with an online publication that may be used to infer characteristics of an originator of an online publication. 
     Reputation modules  208  allow users that transact, utilizing the networked system  102  of  FIG. 1 , 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 modules  208  allow a user, for example through feedback provided by 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. 
     A number of fraud prevention modules  226  implement fraud detection and prevention mechanisms to reduce the occurrence of fraud within the networked system  102 . 
     Navigation of the networked system  102  may be facilitated by one or more navigation modules  214 . For example, a search module (as an example of a navigation module) may enable key word searches of listings published via the networked system  102 . A browse module may allow users to browse various categories and catalogue or inventory data structures according to which listings may be classified within the networked system  102 . Various other navigation modules may be provided to supplement the search and browsing modules. 
     An example of how a user may browse a selection of listings for online publications is described now with respect to  FIG. 3 . 
       FIG. 3  is a block diagram illustrating examples of Web pages  302  and  308 , in accordance with an example embodiment.  FIG. 3  is shown to include a Web page  302  and a Web page  308  that may be viewed via an Internet browser. The arrow  307  represents the opening of the Web page  308  in response to a user interacting with the Web page  302 . 
     The Web page  302  is shown to include item listings  304 , which include selectable listing buttons one through four. Each listing button may be associated with a particular online publication. The selectable “LISTING  1 ” button  306  is one of the example item listings  304 . A user may browse the item listings  304  on the Web page  302  and click the selectable “LISTING  1 ” button  306  if the user is interested in viewing an associated online publication. The selection may be referred to as a “view” of the online publication and may launch the Web page  308 . 
     The Web page  308  illustrates an example online publication  310 , in accordance with an example embodiment. An online publication  310  may include any information (e.g., a classified advertisement) that an originator of the online publication  310  wishes to disseminate to users of the networked system  102  of  FIG. 1 . In some example embodiments, the online publication  310  may include an advertisement describing goods and/or services offered for sale by the originator of the online publication  310 . 
     The online publication  310  is shown to include a selectable “WATCH” button  312 , a selectable “RECOMMEND” button  314 , and a selectable “REPLY” button  316 , which are described in more detail throughout the example embodiments below. 
     Referring again to  FIG. 2 , the input tracking module  229  may track user input to the Web page  302  and  308  of  FIG. 3 . In an example embodiment, a selection of a button on a Web page  302  or  308  is considered user input. 
     For example, the input tracking module  229  may count the number of “views” associated with an online publication. For some example embodiments, the input tracking module  229  may determine a number of “views” for an online publication by counting the number of times users select a selectable item listing button such as the “LISTING  1 ” button  306  of  FIG. 3  to view the corresponding online publication  310 . 
     The input tracking module  229  may count the number of “watches” associated with an online publication by counting the number of times users select a selectable watch button such as the “WATCH” button  312  of  FIG. 3 . A “watch” may represent that the user would like to monitor activity related to an online publication. In an example embodiment, selection of a selectable watch button may cause reference data associated with the online publication to be stored by the networked system  102  or by a user machine. The reference data may be subsequently accessed to permit the user to view the status of the online publication. 
     Input tracking module  229  may count the number of “recommendations” associated with an online publication by counting the number of times users select a selectable recommendation button such as the “RECOMMEND” button  314  of  FIG. 3 . A user may make a recommendation of an online publication to inform, for example, another user of an online publication that may be of interest. In some example embodiments, the recommendation information may be sent via e-mail. Alternatively or additionally, the recommendation information may be sent to a mobile device, posted on a social networking site, a blog, or provided by any other online or network publication. 
     Input tracking module  229  may count the number of replies associated with an online publication by counting a number of times users select a selectable reply button, such as the “REPLY” button  316  of  FIG. 3 . A user may reply to an online publication or advertisement by indicating an interest in further correspondence with the originator (e.g., further to the initial contact made via the online publication). 
     It may be noted that various types of input associated with an online publication may be tracked. For example, an online publication could be “flagged” by a user as being inappropriate, and the input tracking module  229  may keep a record of the flagging (e.g., by counting the number of flags, recording the nature of the flags, or recording the source of the flag). Alternatively or additionally, the online publication may be “tagged”; a tag may include a user-selected keyword, category, or characterization associated with the online publication that may be recorded or counted by the input tracking module  229 . The flagging and tagging information or any other trackable user input may be input to the Web page via interface frames that are not shown in the example Web page  308  of  FIG. 3 . 
     Limits may be imposed on the amount of user input tracked by the input tracking module  229 . Imposing limits on tracking certain user input may help to reject user input meant to fraudulently influence ratings. For example, the input tracking module  229  may limit the number of views, watches, and/or recommends tracked for each user by limiting the number of views to one or more views per hour for each user&#39;s IP address. Alternatively or additionally, user input from an originator of an online publication may not be tracked at any time so as to avoid fraudulent publication and/or originator promotion. 
     The publication rating module  230  may calculate publication ratings for online publications. In an example embodiment, a publication rating may relate to measuring an extent to which an online publication embodies a characteristic (e.g., effectiveness). A person having ordinary skill in the art will recognize that various characteristics of an online publication may be inferred from various types of user inputs. Publication ratings calculated by the publication rating module  230  may be based on the number and type of user inputs tracked by the input tracking module  229 . The publication rating module  230  may employ an algorithm, equation, or logic structure that processes user input to generate a publication rating as the output. 
     The publication rating module  230  may normalize quantification of different types of tracked user input to a common unit that may be used as input to the algorithm, equation, or logic structure. In an example embodiment, the algorithm allots a particular weight or applies a particular factor to user input depending on a type of user input. For example, a 20 percent weight may be given to tracked “views,” 50 percent weight may be given to tracked “recommendations,” and a 30 percent weight may be given to tracked “watches.” A publication rating based on the above weights may be calculated to be the sum of the normalized and weighted user inputs. 
     For some example embodiments, the publication rating module  230  may apply a weight to a user input type that varies with current conditions. An example condition may include the time elapsed since an online publication was first posted. For example, an online publication may receive 70 percent of all of its views during the first 30 days of online publication. Views and recommendations made after 30 days may indicate intrinsic value or desirability of the online publication. In view of this indicator of potential value, the publication rating module  230  may assign a relatively larger weight to tracked views or recommendations that occur more than 30 days after the online publication was posted. 
     The originator rating module  232  may use the online publication ratings generated by the publication rating module  230  to generate an originator rating. For some example embodiments, the originator rating quantifies a characteristic (e.g., trustworthiness) of the originator of an online publication. The originator rating module  232  may weight, average, or apply any appropriate algorithm to generate the originator rating. For some example embodiments, the originator rating module  232  is to increase an originator rating if the originator has originated a number of online publications that meet or exceed a threshold number of online publications. Alternatively or additionally, an originator rating may be increased if the originator has purchased enhanced publication services (e.g., favored advertisement placement) from the networked system  102  of  FIG. 1 . 
     In some example embodiments, the networked system  102  may provide the publication ratings and the originator ratings to users to help users draw conclusions about, for example, online publications and/or their originators. In an example embodiment, the rating information may be published via Web page. 
       FIG. 4  is a block diagram, illustrating a further example of a Web interface, in accordance with an example embodiment.  FIG. 4  is shown to include a Web page  402 , which includes an originator rating  404  and an online publication rating  406 . In an example embodiment, originator rating  404  is presented for visual display. The online publication rating  406  is to present one or more publication ratings  408  and  410 . The originator ratings  404  and the publication ratings  408  and  410  may be calculated by the originator rating module  232  of  FIG. 2  and the publication rating module  230 , respectively, as described in the example embodiments above. 
     For some example embodiments, the publication rating module  230  and/or the originator rating module  232  may provide the calculated ratings to the Web interface  116  of  FIG. 1  so that the Web page  402  of  FIG. 4  may be served to various client machines connected, via the network  104  of  FIG. 1 , to the networked system. 
     The ratings shown in  FIG. 4  need not be provided on a common Web page. For example, a publication rating for an online publication may be presented with the online publication itself (e.g., on the Web page  308  of  FIG. 3 ). Alternatively or additionally, a rating for an online publication may be presented with search results or on a category browsing page, such as the Web page  302 . For some example embodiments, the web page  402  of  FIG. 4  may be a seller profile page that provides biographical and/or other information about a seller (e.g., a publication originator). As such, the profile page may include publication ratings  408  and  410  for all the seller&#39;s advertisements, and an overall originator rating  404  for the seller. 
     The modules of  FIG. 2  may utilize and be supported by example tables stored by the database(s)  126  of  FIG. 1 . Example tables are shown in  FIG. 5  and are discussed below. 
       FIG. 5  is an entity-interaction diagram, illustrating example tables  500 , in accordance with an example embodiment. A user table  502  contains a record for each registered user of the networked system  102  of  FIG. 1 , and may include identifier, address and financial instrument information pertaining to each such registered user. A user may operate as a seller, a buyer, or both, within the networked system  102 . In one example embodiment, a buyer may be a user that has accumulated value (e.g., commercial or proprietary currency), and is accordingly able to exchange the accumulated value for items that are offered for sale by the networked system  102 . 
     The tables  500  also include an items table  504 , which may maintain item records for goods and services that are available to be, or have been, transacted via the networked system  102 . Each item record within the items table  504  may furthermore be linked to one or more user records within the user table  502 , so as to associate a seller and one or more actual or potential buyers with each item record. 
     A transaction table  506  contains a record for each transaction (e.g., a purchase or sale transaction) pertaining to items for which records exist within the items table  504 . 
     An order table  508  is populated with order records, each order record being associated with an order. Each order, in turn, may be with respect to one or more transactions for which records exist within the transaction table  506 . 
     Bid records within a bids table  510  each relate to a bid received at the networked system  102  in connection with an auction-format listing supported by an auction module  202  of  FIG. 2 . A feedback table  512  is utilized by one or more reputation modules  208  of  FIG. 2 , in one example embodiment, to construct and maintain reputation information concerning users. A history table  514  maintains a history of transactions to which a user has been a party. 
     The publication rating table  518  is to store user input tracked by the tracking module  229  of  FIG. 2 , and to store calculated publication ratings calculated by the publication rating module  230 . The rating rules table  520  is to keep a record for each user input type that sets forth a factors, weight, or other parameter to be applied to the tracked user input (e.g., after being normalized), under different conditions, when the publication rating module  230  of  FIG. 2  or the originator rating module  232  calculates a rating. The originator rating table  522  is to store an originator rating that has been calculated by the originator rating module  232  of  FIG. 2  for each originator. A discussion in  FIGS. 6-8  provides further details regarding the publication rating table  518 , the rating rules table  520  and the originator rating table  522 . 
       FIG. 6  is a table illustrating an example of a publication rating table  600 , in accordance with an example embodiment. The publication rating table  600  is shown to include a publication identifier column  602 , an originator identifier column  604 , a views column  606 , a watches column  608 , a recommendations column  610 , and a publication rating column  612 . The intersections of rows  614 ,  616 ,  618 , and  620  with the columns just described show specific rating-related values (e.g., number of views in column  606 , etc.) corresponding to publication identifier in column  602 . Some of those specific values are to be referenced below with respect to  FIG. 9  in a description of an example method for providing an originator rating. 
       FIG. 7  is a table illustrating an example of a rating rules table  700 , in accordance with an example embodiment. The rating rules table  700  is shown to include an input type column  702 , a default weight column  704 , a 10 to 30 days weight column  706 , and a greater than 30 days weight column  708 . In an example embodiment, values in the default weight column  704  are default factors to be applied to the values in the input type columns  606 ,  608 , and  610  of  FIG. 6  in the publication ratings calculation. The values in the 10 to 30 days weight column  706  are to be applied in the publication rating calculation when the online publication was posted greater than 10 days ago but fewer than 30 days ago. The values in the greater than 30 days weight column  708  are to be applied in the publication rating calculation when the online publication was posted greater than 30 days ago. 
       FIG. 8  is a table illustrating an example of an originator rating table  800 , in accordance with an example embodiment. The originator rating table  800  is shown to include an originator identifier column  802  and an originator rating column  804 . 
     Example embodiments illustrating the use of the example system structures and functions introduced above are now described with respect to the flow diagram of  FIG. 9 .  FIG. 9  is a flow diagram illustrating an example method  900  for rating an originator of a publication, in accordance with an example embodiment. 
     At block  902 , the example method  900  may include tracking user inputs associated with multiple online publications. As described above, the input tracking module  229  of  FIG. 2  may track the multiple user inputs related to the online publications presented to users of the networked system  102 . In various example embodiments, Web pages such as the Web pages  302  and  308 , for various different online publications, may be displayed to multiple users connected to the networked system  102  of  FIG. 1 . 
     Referring to row  614  of  FIG. 6 , the input tracking module  229  may record in the publication rating table  600  for the online publication corresponding to the publication identifier “PUB ID 1 ,” originated by an originator “O 1 ,” the receipt of one view, two watches and one recommendation. For a different online publication, the input tracking module  229  may further record in the table  600  that an online publication corresponding to the identifier “PUB ID 2 ” and the same originator “O 1 ” has been viewed twice, watched twice, and recommended twice, as is displayed in row  616 . Likewise, as further user input relating to online publications is received, the input tracking module  229  may write the relevant information to the appropriate row and column of the publication rating table  600 . In this manner, the input tracking module  229  may track user inputs associated with multiple online publications. 
     Returning to  FIG. 9 , at block  904 , the example method  900  may include using the tracked user inputs to calculate a publication rating for each of the online publications to generate multiple publication ratings. In an example embodiment, the publication rating module  230  may access (e.g., read from) the publication rating table  600  of  FIG. 6  to obtain input to be processed in a publication rating calculation. The publication rating module  230  may, in some example embodiments, obtain any rating rules from the rating rules table  700  that may be appropriate to apply in a publication rating calculation. For example, for the online publication represented by the publication identifier “PUB ID 8 ” of row  618  of  FIG. 6 , the default weights of column  704  in  FIG. 7  may be applicable. In an example embodiment, calculating the publication rating may include summing the normalized and weighted user inputs. Based on this example algorithm and the default weights in column  704  of  FIG. 7 , the publication rating for “PUB ID 8 ” is calculated as: 
       (2*0.2)+(1*0.3)+(3*0.5)=2.2. 
     For this example embodiment, the calculated publication rating would be 2.2. The default weights may be appropriate for calculating publication ratings for the online publications “PUB ID 1 ” of row  614 , “PUB ID 2 ” of row  616 , and “PUB ID 3 ” of row  620  in  FIG. 6 . For those example online publications, the calculated publication ratings are 1.3, 2, and 1.9, respectively. 
     In some example embodiments, a publication rating quantifies the effectiveness of an online publication. For example, the publication rating for “PUB ID 1 ” of 1.3 may be compared to the publication rating for “PUB ID 2 ” of 2, and it could be inferred that the online publication represented by “PUB ID 2 ” is relatively more effective than the online publication represented by “PUB ID 1 .” 
     Of course, confidence given to a calculated publication rating may depend on the number and type of user inputs associated with an online publication as well as weights assigned to different types of user inputs. For some example embodiments, the publication rating module  230  of  FIG. 2  may calculate a confidence score, based on the number and types of user inputs associated with an online publication. For example, a relatively large number of user inputs may correspond to a higher confidence score and the publication rating module  230  may factor the confidence score into the publication rating calculation to increase or decrease the publication rating with an increase or decrease in input confidence. 
     In various example embodiments, publication rating module  230  may provide the calculated publication ratings to the Web interface  116  of  FIG. 1 , to display the publication ratings to users. 
     At block  906 , the example method  900  may include using the online publication ratings to calculate an originator rating that rates the originator of each of the online publications. In an example embodiment, a rating may be sought for the originator having the originator identifier “O 1 ” in column  604  of  FIG. 6 . Referring to  FIG. 2 , to rate originator “O 1 ,” the originator rating module  232  may scan the originator identifier column  604  of  FIG. 6  to identify all the publications originated by “O 1 .” The originator rating module  232  of  FIG. 2  may collect, from column  612  of  FIG. 6 , all the publication ratings for the online publications originated by the originator “O 1 .” 
     In some example embodiments, the calculation of the originator rating for “O 1 ” may be a sum or average of the applicable publication ratings. Alternatively or additionally, various weights and/or rating rules may be used to attempt to optimize the accuracy of the originator rating. 
     The originator rating module  232  may write the calculated originator rating to the row of the originator rating column  804  of  FIG. 8  that corresponds to the originator “O 1 ” in column  802  of  FIG. 8 . In an example embodiment, the originator rating may be calculated as the sum of the publications ratings for the originator&#39;s (e.g., “O 1 ”) online publications, which would result in an originator rating of 6.4. This originator rating may be compared to the ratings of other originator ratings, to gauge, for example, the trustworthiness of the originator “O 1 .” As described with respect to publication ratings, confidence in the input data may be considered in the rating or assessment of an originator. 
     Still referring to  FIG. 9 , at block  908 , the example method  900  may include providing the originator rating for display. As described above, the publication ratings and/or the originator rating may be output to users via Web page, such as the Web page  402  of  FIG. 4 , to allow users to make a decision about interacting, for example, with the originator of a publication. 
     Through practice of the techniques described above, an originator rating may be generated that may, for example, provide a measure of trustworthiness or credibility for an originator of a publication such as a seller who posts advertisements to online classifieds. Even after an originator&#39;s online publications have expired, and the online publication ratings are no longer available, the originator rating may remain available for inspection by users. In example embodiments in which an originator seldom posts advertisements (e.g., a seller may post one classified ad for a rare coin), the publication rating may be a more useful indicator of advertisement credibility or effectiveness than an originator rating would be. 
     Users who believe an online publication is effective and/or who believe that an originator of the online publication is trustworthy may be more likely to reply to an online publication if the user is interested in its subject matter. Thus, techniques disclosed above may promote the use and popularity of an online classified or marketplace service, which ultimately may result in increased revenue to the service. 
     Tracking the way that users interact or behave with a Web page including an online publication (e.g., views, watches, etc.) may provide relatively objective input that may be used to characterize an online publication. In the example embodiments described herein, the characteristics of effectiveness and trustworthiness have been described; however, the type of input to be tracked may be selected by a designer or programmer based on the characteristic about a publication that the designer wishes to measure. 
     A machine and its features are described below with reference to  FIG. 10 . The machine may be representative of the machines described with respect to FIG.  1 , including the client machines  110  and  112 , and the machines operating the systems and modules of the networked system  102 . 
       FIG. 10  shows a diagrammatic representation of machine in the example form of a computer system  1000  within which a set of 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 a user machine 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 server computer, a client computer, a personal computer (PC), a tablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), a cellular telephone, a web appliance, a network router, switch or bridge, or any machine capable of executing a set of 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  1000  includes a processor  1004  (e.g., a central processing unit (CPU), a graphics processing unit (GPU) or both), and a main memory  1010  and a static memory  1014 , which communicate with each other via a bus  1008 . The computer system  1000  may further include a video display unit  1002  (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)). The computer system  1000  also includes an alphanumeric input device  1012  (e.g., a keyboard), a cursor control device  1016  (e.g., a mouse), a drive unit  1020 , a signal generation device  1040  (e.g., a speaker) and a network interface device  1018 . 
     The drive unit  1020  includes a machine-readable medium  1022  on which is stored one or more sets of instructions  1024  (e.g., software) embodying any one or more of the methodologies or functions described herein. The instructions  1024  may also reside, completely or at least partially, within the main memory  1010 , the static memory  1014 , and/or within the processor  1004  during execution thereof by the computer system  1000 , the main memory  1010 , the static memory  1014 , and the processor  1004  also constituting machine-readable media. 
     The instructions  1024  may further be transmitted or received over a network  1030  via the network interface device  1018 . 
     While the machine-readable medium  1022  is shown in an example embodiment to be a single medium, the term “machine-readable medium” should be taken to 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 sets of instructions. The term “machine-readable medium” shall also be taken to include any medium that is capable of storing or encoding a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the claimed subject matter. The term “machine-readable medium” shall accordingly be taken to include, but not be limited to, solid-state memories, optical, and magnetic media. 
     Thus, a method and system to rate an originator of a publication has been described. Although the claimed subject matter has been described with reference to specific example embodiments, it will be evident that various modifications and changes may be made to these embodiments without departing from the broader spirit and scope of what is claimed. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense. 
     The Abstract of the Disclosure is provided to comply with 37 C.F.R. §1.72(b), requiring an abstract that will allow the reader to quickly ascertain the nature of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. In addition, in the foregoing Detailed Description, it can be seen that various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter may lie in less than all features of a single disclosed embodiment. Thus the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separate embodiment.