Patent Application
20110078017
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.
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.
Some embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings in which:
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'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.
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
Further, while the networked system 100 shown in
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.
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
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
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
Reputation modules 208 allow users that transact, utilizing the networked system 102 of
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
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
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
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
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
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
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
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
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'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
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.
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
The ratings shown in
The modules of
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
The publication rating table 518 is to store user input tracked by the tracking module 229 of
Example embodiments illustrating the use of the example system structures and functions introduced above are now described with respect to the flow diagram of
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
Referring to row 614 of
Returning to
(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 ID1” of row 614, “PUB ID2” of row 616, and “PUB ID3” of row 620 in
In some example embodiments, a publication rating quantifies the effectiveness of an online publication. For example, the publication rating for “PUB ID1” of 1.3 may be compared to the publication rating for “PUB ID2” of 2, and it could be inferred that the online publication represented by “PUB ID2” is relatively more effective than the online publication represented by “PUB ID1.”
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
In various example embodiments, publication rating module 230 may provide the calculated publication ratings to the Web interface 116 of
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 “O1” in column 604 of
In some example embodiments, the calculation of the originator rating for “O1” 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
Still referring to
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'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
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.
