Abstract:
Described is a technology by which online advertisements for returning with a query response are ranked according to reputation. The reputation may correspond to a product or service and/or seller reputation. In one example, a set of relevant advertisement items are located and ranked using reputation data as a factor. For example, for each item, a ranking value is based on a mathematical combination of a product reputation score, a seller reputation score and a relevance score, with the items ranked by their computed values. The scores may be weighted differently. The reputation data may be mined from a review source, such as customer reviews available on the web. In one example implementation, a 3-gram model that considers terms in the review along with the two terms proceeding each term is used to analyze the reviews to determine whether each review is positive or negative with respect to the reputation.

Description:
BACKGROUND 
       [0001]    Advertisement search, or “ads” search, is a popular web technique that helps websites gain profits from free search and other online services. For example, search engines like MSN Search operate online advertising businesses within their search result pages. In general, advertisers pay the search engines for user clicks, whereby the more clicks that occur (that is, the greater the conversion rate of users&#39; clicks on advertisements), the more profit that is made. 
         [0002]    Typically, advertisements are ranked by automatic ranking algorithms similar to those used in web query searching, which generally calculate the similarities between advertisement content and user queries, search results, each advertiser&#39;s per-click payment amount, and so forth. However, heretofore such ranking algorithms have not recognized the characteristics of the advertisements themselves, and any mechanism that improves the user click rate on advertisements would be commercially valuable. 
       SUMMARY 
       [0003]    This Summary is provided to introduce a selection of representative concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used in any way that would limit the scope of the claimed subject matter. 
         [0004]    Briefly, various aspects of the subject matter described herein are directed towards a technology by which items corresponding to online advertisements that are to be returned with a query response are ranked using reputation data. The reputation may correspond to a reputation of a product or service and/or a seller (e.g., retailer or wholesaler, or service provider). 
         [0005]    In one implementation, advertisement items are previously processed based on relevance, which may include relevance to the search terms and/or advertiser payment. A reputation ranking mechanism ranks (or re-ranks) the advertisement items using reputation data as a factor in the ranking. For example, for each item of information corresponding to an advertisement, the ranking mechanism determines a value based on a mathematical combination of a product reputation score, a seller reputation score and a relevance score, and ranks the items according to the values. The scores may be weighted differently relative to one another in the mathematical combination. 
         [0006]    The product (or service) and/or seller reputation data may be mined from a review source, such as customer reviews available on the web. In one example implementation, a model is used to analyze the text of the reviews to determine whether each review is more likely positive or more likely negative with respect to the reputation. One such model is a 3-gram model that considers terms in the text along with the two terms proceeding each term. 
         [0007]    Other advantages may become apparent from the following detailed description when taken in conjunction with the drawings. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0008]    The present invention is illustrated by way of example and not limited in the accompanying figures in which like reference numerals indicate similar elements and in which: 
           [0009]      FIG. 1A  is a block diagram representing an example system for processing a query to rank advertisements provided as part of the response thereto based on reputation data. 
           [0010]      FIG. 1B  is a block diagram representing an alternative example system for processing a query to rank advertisements provided as part of the response thereto based on reputation data. 
           [0011]      FIG. 2  is a flow diagram representing example steps taken to rank advertisements based on reputation data. 
           [0012]      FIG. 3  is a block diagram representing an example architecture for determining reputation of a product (or seller) based on mining data corresponding to reviews of that product. 
           [0013]      FIG. 4  is a flow diagram representing example steps for determining reputation of a product (or seller) based on mining data corresponding to reviews of that product. 
           [0014]      FIG. 5  shows an illustrative example of a general-purpose network computing environment into which various aspects of the present invention may be incorporated. 
       
    
    
     DETAILED DESCRIPTION 
       [0015]    Various aspects of the technology described herein are generally directed towards a ranking mechanism that in part uses reputation data to select and/or rank which advertisements (e.g., a link comprising an image and/or text) to provide to users in conjunction with a query response. In general, because consumers tend to be more interested in reputable products, services and/or suppliers, the ranking mechanism described herein ordinarily increases the overall user click rate (and thus profits) generated from online advertising. Indeed, reputation may be one of the most important factors for a user that is deciding whether to click on an advertisement. Notwithstanding, as can be readily appreciated, the various aspects of the ranking mechanism are independent of any particular business or revenue model. For example, the use of reputation data in selecting and/or ranking any set of data may benefit from the aspects described herein. 
         [0016]    Further, while as described herein the term “reputation” generally includes concepts such as user opinions about advertised products or services and/or the advertisers (e.g., retailers, wholesalers or service providers) providing the products or services, there is no requirement as to any particular source of reputation data. For example, the general public&#39;s overall reviews may be one source, a professional reviewing enterprise (or the like) an alternative or additional source, a limited group of individuals or the like (e.g., only reviewers that fit a certain demographic) yet another possible source, and so forth. Moreover, as used herein, the terms “product” and “service” are interchangeable, such as in the various examples, for purposes of simplicity. 
         [0017]    As such, the present invention is not limited to any particular embodiments, aspects, concepts, protocols, formats, structures, functionalities or examples described herein. Rather, any of the embodiments, aspects, concepts, protocols, formats, structures, functionalities or examples described herein are non-limiting, and the present invention may be used various ways that provide benefits and advantages in computing and information retrieval technology in general. 
         [0018]    Turning to  FIG. 1A , there is shown a data store of advertisements  102 , which any suitable relevance ranking mechanism  104  may search when given a query to obtain a set of advertisements  106  ranked by relevance as well as typically the per-click payment amounts by the advertisers. An example of one such relevance ranking mechanism  104  is described in copending United States patent application entitled “Efficient Retrieval Algorithm by Query Term Discrimination,” assigned to the assignee of the present invention and hereby incorporated by reference. Note that the set of relevance ranked advertisements  106  may be some limited number, such as a fixed number and/or only those meeting a threshold relevance score. However, how advertisements in a given implementation may be chosen and ranked are determined by an online advertising company strategy; e.g., there may be various factors considered, including relevance, click-through rate, geographical position, and so forth. 
         [0019]    As described herein, a reputation ranking (or re-ranking) mechanism  108  processes the relevance-ranked set of advertisements  106 , using reputation data  110  and/or the web  112  as part of the criteria to determine a set of reputation ranked relevant advertisements  114 . Note that the reputation ranking mechanism  108  of  FIG. 1A  is shown as processing the already-ranked advertisements  106 , however it is feasible to incorporate the reputation mechanism into a relevance-ranking mechanism, such as including a pre-computed reputation score in an inverted query index or the like that is used to search for relevant advertisements based on the query&#39;s search terms. 
         [0020]      FIG. 1B  shows such an alternative with a relevance/payment/reputation ranking mechanism  105  that generates a single ranked output set  107 , where like numbers represent like components. Thus, as used herein, “ranking” by reputation includes ranking as part of an original ranking process (e.g., in conjunction with a relevance and/or payment ranking process), as a pre-ranking process (e.g., before ranking by a relevance and/or payment ranking process), or re-ranking (e.g., following a relevance and/or payment ranking process as exemplified in  FIG. 1A ). 
         [0021]    As represented in  FIGS. 1A and 1B , the reputation ranking mechanism  108  ( FIG. 1A ) or mechanism  105  ( FIG. 1B ) is shown as dynamically crawling the web  112  and/or using cached reputation data  110 . In an alternative implementation, another mechanism may regularly obtain at least some of the reputation data and cache it independent of the query processing, such as an offline mechanism that regularly updates the reputation data store  110 . 
         [0022]    To automatically rank advertisements by product (equivalent to service) reputation considerations, the technology described herein uses one or more various factors with respect to traditional content relevance ranking algorithms. Such factors include the reputation of products and/or services, and/or the reputation of sellers (e.g., retailers, wholesalers, service providers and the like). As described below, the reputation data may be predicted by mining reviews and the like that are available from various sources, such as online customer reviews. 
         [0023]    For example, surveying product and other information before making an online transaction is a fairly popular consumer trend. Various product information portals usually provide product specifications, seller prices and customer reviews. Many users use such portals to compare specifications of similar products, to choose a particular seller based on price, to review others&#39; comments to learn about their consumer experiences, and so forth. However, the number of products and sellers is very large, making it difficult and time-consuming for consumers to collect the necessary information. 
         [0024]    To this end, an automatic prediction mechanism (e.g., incorporated into the reputation ranking mechanism  108 ) predicts product/seller reputations by mining customer reviews, such as those that are published on product information portals. The reputation data is represented as the positive review percentage, which in one example implementation is formalized as set forth herein. 
         [0025]    More particularly, consider that the collected review set of a give product p is S(p)={r 1 , r 2 , . . . r n }. For each review r, the reputation R(r) can be either positive (POS) or negative (NEG). Typically, a review r is regarded as a series of terms, r=w 1 w 2  . . . w k , where w represents a word; (however as used herein, the concept of a “term” includes any single entity that can be represented in a data structure, such as a word, symbol, shape and so forth, and/or any phrase comprising a plurality of such entities.) For example, “good,” “bad,” “excellent,” “defective,” and so forth are all terms that may be associated with a product review. As described below, a reputation value R(r) is made by analyzing the term series using a 3-gram model (described below) so that terms such as “no good” or “not very good” will not be misinterpreted as good. 
         [0026]    Thus, given a query, one example implementation described herein ranks advertisements by considering each advertisement&#39;s relevance to the query and/or the payment of advertisers, as well as by analyzing reviews and the like with respect to the sellers and/or the products or services. In the example implementation, three general steps are performed, including collecting the reviews (or like data, which will be considered a “review” herein), classifying review opinions, and then using the review information to rank advertisements (or re-rank candidate advertisements previously ranked based on relevance and/or payment considerations). 
         [0027]    To collect reviews as generally represented via step  202  of  FIG. 2 , various web sites that contain reviews may be crawled. Additional sources of reviews, such as databases, reviewing enterprises, and so forth may likewise be accessed. 
         [0028]    As represented by step  204 , reviewer opinion classification is next performed, which classifies reviews into positive ones and negative ones. The result is a positive review percentage of each product and seller. Note that the number of reviews can also be counted, because not all of the reviews have a rating value or the like, and the reviews from different web sites usually have different rating mechanisms. For example, there may be ten ratings at xyz.com, while there are only five ratings at abcd.com. 
         [0029]    In this example, a-last step is to rank the advertisements, including ranking based on reputation data. For example, with the seller and product information provided by the advertisers, the relation between an advertisement and reviews can be easily established. The ranking mechanism generally analyzes the reviews&#39; text and calculates the reputation, in terms of whether the reviews are positive or negative. For example, for a given query (q), a set of relevant advertisements  106  may be ranked (or re-ranked) into the reputation based set  114  by the following scoring function for each advertisement (ad): 
         [0000]      Score(ad,q)=α R   p (Review Seller ( ad ))+β R   p (Review Product ( ad ))+θRelevance( ad, q ) 
         [0000]    where α+β+θ=1. 
         [0030]    As can be seen, the example scoring function above takes three factors into consideration, namely R p (Review Seller (ad)), which represents the positive rate of the comments to the associated seller, R p (Review Product (ad)), which represents the positive rate of the comments to the associated product (or service), and Relevance(ad, q), which represents the relevance between the advertisement (ad) and the query q. Weighting each factor may be accomplished via the variables α, β and θ. 
         [0031]    Turning to a consideration of mining reviews to predict product reputation, in one implementation, a 3-gram statistical approach is used. With respect to mining reviews, an online product information portal for example, is one valuable information resource that typically provides product specifications, seller price information and user comments. This information explicitly or implicitly correlates to the product reputation and quality. As can be readily appreciated, note that comments/reviews on sellers may be similarly processed, but for purposes of simplicity,  FIGS. 3 and 4  will refer to product reviews, analysis and reputation results. 
         [0032]      FIG. 3  represents an example architecture that automatically predicts product reputation. To this end, a 3-gram model  304  is built from training data  306  comprising some number of reviewer comments crawled from Web. The training data  304  can then be analyzed (e.g., manually) to build the 3-gram model  306  whereby it is known to be highly accurate with respect to what reviewers think of the product reputation and quality. (Note that seller reputation may be similarly used as training data for a 3-gram model.) Step  402  of the flow diagram of  FIG. 4  represents this learning/training step, which may be repeated as often as desired as new training data becomes available. 
         [0033]    After the 3-gram model is built, given a review  308  or like data of an unrated product, an analyzer  310  then analyzes the text of the user review data (e.g., comments) for that unrated product using the 3-gram model  306 . Note that the web may be crawled regarding comments on that product on demand as needed for a query, or in advance, such as in an offline reputation store building state. Step  404  locates finding one or more reviews for the product. 
         [0034]    Step  406  represents the analysis against the 3-gram model to locate series of terms that determine (step  408 ) whether the review is more like the positive model or the negative model. Note that the review can be discarded or otherwise handled if, for example, the text is corrupted or otherwise nonsensical. Step  410  or  412  decreases or increases that product&#39;s reputation, respectively, as set forth above (e.g., via its positive review percentage). 
         [0035]    in one example implementation, the 3-gram statistical approach of mining customer reviews assumes that a term (e.g., “good” or “bad”) within a reviewer&#39;s comments is related to the former two terms (e.g., “not” or “not so”), as set forth below: 
         [0000]        P (ω 1 ω 2 ω 3 )= P (ω 3 |ω 1 ω 2 )=#(ω 1 ω 2 ω 3 )/#(ω 1 ω 2 ) 
         [0000]    where #(w) is the frequency of term series w. The learning process is used with training data  304  (step  402 ) to learn the 3-gram language model of both positive and negative comments. Both the positive comment model M p  and the negative comment model M n  comprise a set of term series representing their probabilities in the training set. 
         [0036]    In one example implementation, to predict a comment c=w 1  w 2  w 3  . . . w k  to be positive or negative, a decision is made as to which model a comment is more alike. Given m* as the model: 
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         [0037]    Any number of new (that is, not already processed) reviews may be analyzed, as represented via step  414 . The result is a prediction as to the product&#39;s reputation, shown in  FIG. 3  as that product&#39;s prediction data  312 . Via step  414 , the prediction data  312  can be mathematically combined from any number of user reviews. Note that while  FIGS. 3 and 4  refer to an “unrated” product, it is understood that an already rated product may be reanalyzed any number of times, such as to keep the reputation rating relatively updated, and/or reanalyzed on demand. Further, note that other reviews (e.g., step  404 ) may be located by crawling while the analysis and processing of located reviews (steps  406 ,  408  and  410  or  412 ) are taking place (e.g., in parallel). 
         [0038]    In this manner, the reputation of a product and/or seller may be used as factors in determining a ranking order of advertisements to provide as part of the response to a user query. In conjunction with relevance, the click-rate on advertisements will increase. 
       Exemplary Operating Environment 
       [0039]      FIG. 5  illustrates an example of a suitable computing system environment  500  on which the examples represented in  FIGS. 1-4  may be implemented. The computing system environment  500  is only one example of a suitable computing environment and is not intended to suggest any limitation as to the scope of use or functionality of the invention. Neither should the computing environment  500  be interpreted as having any dependency or requirement relating to any one or combination of components illustrated in the exemplary operating environment  500 . 
         [0040]    The invention is operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well known computing systems, environments, and/or configurations that may be suitable for use with the invention include, but are not limited to: personal computers, server computers, hand-held or laptop devices, tablet devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. 
         [0041]    The invention may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, and so forth, which perform particular tasks or implement particular abstract data types. The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in local and/or remote computer storage media including memory storage devices. 
         [0042]    With reference to  FIG. 5 , an exemplary system for implementing various aspects of the invention may include a general purpose computing device in the form of a computer  510 . Components of the computer  510  may include, but are not limited to, a processing unit  520 , a system memory  530 , and a system bus  521  that couples various system components including the system memory to the processing unit  520 . The system bus  521  may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus also known as Mezzanine bus. 
         [0043]    The computer  510  typically includes a variety of computer-readable media. Computer-readable media can be any available media that can be accessed by the computer  510  and includes both volatile and nonvolatile media, and removable and non-removable media. By way of example, and not limitation, computer-readable media may comprise computer storage media and communication media. Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by the computer  510 . Communication media typically embodies computer-readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Combinations of the any of the above should also be included within the scope of computer-readable media. 
         [0044]    The system memory  530  includes computer storage media in the form of volatile and/or nonvolatile memory such as read only memory (ROM)  531  and random access memory (RAM)  532 . A basic input/output system  533  (BIOS), containing the basic routines that help to transfer information between elements within computer  510 , such as during start-up, is typically stored in ROM  531 . RAM  532  typically contains data and/or program modules that are immediately accessible to and/or presently being operated on by processing unit  520 . By way of example, and not limitation,  FIG. 5  illustrates operating system  534 , application programs  535 , other program modules  536  and program data  537 . 
         [0045]    The computer  510  may also include other removable/non-removable, volatile/nonvolatile computer storage media. By way of example only,  FIG. 5  illustrates a hard disk drive  541  that reads from or writes to non-removable, nonvolatile magnetic media, a magnetic disk drive  551  that reads from or writes to a removable, nonvolatile magnetic disk  552 , and an optical disk drive  555  that reads from or writes to a removable, nonvolatile optical disk  556  such as a CD ROM or other optical media. Other removable/non-removable, volatile/nonvolatile computer storage media that can be used in the exemplary operating environment include, but are not limited to, magnetic tape cassettes, flash memory cards, digital versatile disks, digital video tape, solid state RAM, solid state ROM, and the like. The hard disk drive  541  is typically connected to the system bus  521  through a non-removable memory interface such as interface  540 , and magnetic disk drive  551  and optical disk drive  555  are typically connected to the system bus  521  by a removable memory interface, such as interface  550 . 
         [0046]    The drives and their associated computer storage media, described above and illustrated in  FIG. 5 , provide storage of computer-readable instructions, data structures, program modules and other data for the computer  510 . In  FIG. 5 , for example, hard disk drive  541  is illustrated as storing operating system  544 , application programs  545 , other program modules  546  and program data  547 . Note that these components can either be the same as or different from operating system  534 , application programs  535 , other program modules  536 , and program data  537 . Operating system  544 , application programs  545 , other program modules  546 , and program data  547  are given different numbers herein to illustrate that, at a minimum, they are different copies. A user may enter commands and information into the computer  510  through input devices such as a tablet, or electronic digitizer,  564 , a microphone  563 , a keyboard  562  and pointing device  561 , commonly referred to as mouse, trackball or touch pad. Other input devices not shown in  FIG. 5  may include a joystick, game pad, satellite dish, scanner, or the like. These and other input devices are often connected to the processing unit  520  through a user input interface  560  that is coupled to the system bus, but may be connected by other interface and bus structures, such as a parallel port, game port or a universal serial bus (USB). A monitor  591  or other type of display device is also connected to the system bus  521  via an interface, such as a video interface  590 . The monitor  591  may also be integrated with a touch-screen panel or the like. Note that the monitor and/or touch screen panel can be physically coupled to a housing in which the computing device  510  is incorporated, such as in a tablet-type personal computer. In addition, computers such as the computing device  510  may also include other peripheral output devices such as speakers  595  and printer  596 , which may be connected through an output peripheral interface  594  or the like. 
         [0047]    The computer  510  may operate in a networked environment using logical connections to one or more remote computers, such as a remote computer  580 . The remote computer  580  may be a personal computer, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to the computer  510 , although only a memory storage device  581  has been illustrated in  FIG. 5 . The logical connections depicted in  FIG. 5  include one or more local area networks (LAN)  571  and one or more wide area networks (WAN)  573 , but may also include other networks. Such networking environments are commonplace in offices, enterprise-wide computer networks, intranets and the Internet. 
         [0048]    When used in a LAN networking environment, the computer  510  is connected to the LAN  571  through a network interface or adapter  570 . When used in a WAN networking environment, the computer  510  typically includes a modem  572  or other means for establishing communications over the WAN  573 , such as the Internet. The modem  572 , which may be internal or external, may be connected to the system bus  521  via the user input interface  560  or other appropriate mechanism. A wireless networking component  574  such as comprising an interface and antenna may be coupled through a suitable device such as an access point or peer computer to a WAN or LAN. In a networked environment, program modules depicted relative to the computer  510 , or portions thereof, may be stored in the remote memory storage device. By way of example, and not limitation,  FIG. 5  illustrates remote application programs  585  as residing on memory device  581 . It may be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers may be used. 
         [0049]    An auxiliary subsystem  599  (e.g., for auxiliary display of content) may be connected via the user interface  560  to allow data such as program content, system status and event notifications to be provided to the user, even if the main portions of the computer system are in a low power state. The auxiliary subsystem  599  may be connected to the modem  572  and/or network interface  570  to allow communication between these systems while the main processing unit  520  is in a low power state. 
       CONCLUSION 
       [0050]    While the invention is susceptible to various modifications and alternative constructions, certain illustrated embodiments thereof are shown in the drawings and have been described above in detail. It should be understood, however, that there is no intention to limit the invention to the specific forms disclosed, but on the contrary, the intention is to cover all modifications, alternative constructions, and equivalents falling within the spirit and scope of the invention.