Abstract:
Obtaining search results responsive to a query, each search result identifying a respective resource and each resource having a respective rank determined by a primary ranking algorithm. The method includes obtaining primary user feedback data and secondary user feedback data for the resource. The primary and secondary user feedback data representing previous user interactions with the resource when the resource was presented as a search result responsive to the query. The method also includes applying a weight to the secondary user feedback data, the weight being at least partially based on a threshold quantity of the primary user feedback data. The method also includes aggregating the primary user feedback data and the weighted secondary user feedback data and modifying the respective rank of the resource as a search result for the query based at least partially on the aggregated data.

Description:
BACKGROUND 
     This specification relates to digital data processing and, in particular, to ranking of search results. 
     Internet search engines provide information about Internet accessible resources (e.g., web pages, images, electronic books, text documents, sounds, music, videos, electronic games, and multimedia content) by returning search results in response to queries. A search result includes, for example, a Uniform Resource Locator (URL) and a snippet of information for resources responsive to a query. The search results can be ranked (i.e., put in an order) according to scores assigned to the search results by a scoring function. The scoring function ranks the search results according to various signals, for example, where (and how often) query terms appear in the search results and how common the query terms are in the search results indexed by the search engine. 
     SUMMARY 
     This specification relates to digital data processing and, in particular, to combining user feedback. 
     Particular embodiments of the subject matter described in this specification can be implemented so as to realize one or more of the following advantages. The quality of search results may be improved. Quality metrics from one search engine may be used to improve the ranking of search results for another search engine. Fresh content from video property could get chance to surface on web search engine which serves large volume of pages from variety of properties. The details of one or more embodiments of the subject matter described in this specification are set forth in the accompanying drawings and the description below. Other features, aspects, and advantages of the subject matter will become apparent from the description, the drawings, and the claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates an example of video search results displayed in a web page presented in a web browser. 
         FIG. 2  illustrates an exemplary video player as displayed in a web page presented in a web browser or other software application. 
         FIG. 3  illustrates an example search system for identifying search results in response to search queries. 
         FIG. 4  is a diagram illustrating example user interfaces for search systems. 
         FIG. 5  illustrates a diagram of an example video search system utilizing user feedback data from a web search system. 
         FIG. 6  illustrates example search result rankings being adjusted based on combined quality metrics. 
         FIG. 7  is a flowchart illustrating an example process for combining user feedback. 
         FIG. 8  is a schematic diagram of an example system configured to derive a combined quality metric. 
     
    
    
     Like reference numbers and designations in the various drawings indicate like elements. 
     DETAILED DESCRIPTION 
       FIG. 1  illustrates an example of video search results displayed in a web page  100  presented in a web browser or other software application. The web page  100  includes a text entry field  102  which accepts queries from users. A query is submitted by a user and specifies general or specific topics of interest to the user. A query may be in the form of text (e.g. “palm tree”) or a spoken word or phrase, for example. Queries can also comprise images or videos. A submitted query is transmitted to a search engine. The search engine identifies videos in an index of resources that are responsive to the query. The search engine transmits search results  106 ,  108  and  110  that reference the identified videos to the web browser for presentation in region  112  of the web page  100 . 
     Each search result can include summary information such as a representative frame from the video (e.g., frame  106   a ), the video&#39;s title (e.g., title  106   b ), a textual summary (e.g., synopsis  106   d ), the running time of the video (e.g., time data  106   c ), and a Uniform Resource Locator (URL) or other identifier specifying the location of the video (e.g., identifier  106   e ). In some implementations, time data  106   c  includes the duration of the video and the date on which the video was uploaded. Other search result information is possible. A video may be stored at the location (e.g., identifier  106   e ) in a portion of a file that holds other content, in a single file dedicated to the video in question, or in multiple coordinated files. A video may, but need not, correspond to a file. A user can select a search result (e.g., frame  106   a  or identifier  106   e ) with a mouse click, a finger gesture on a touch-sensitive surface, a speech command to a microphone, or by way of another input device, for example, in order to view the video identified by the search result. 
     Search results  112  can be ranked according to traditional techniques for determining an information retrieval (IR) score for indexed resources in view of a given query, for example. The relevance of a particular resource with respect to a particular search term or other provided information may be determined by any appropriate scoring technique. The score of the resource can be modified based on prior interactions of users with the resource in view of the given query. 
       FIG. 2  illustrates an exemplary video player as displayed in a web page  250  presented in a web browser or other software application. In some implementations, the video player is a stand-alone software application. The video player includes a playback window  252  for displaying a video. Different video formats are supported including, but not limited to, HD, HDV, DVD, Blu-Ray, HD-DVD, HD-VMD, CH-DVD, HDTV, Adobe Systems Incorporated FLASH, MPEG, MPEG-4, and Apple Inc. QUICKTIME. A play button  260   a  starts and pauses the video playback while a progress bar indicator  260   b  indicates how much of the video has been played. The current time offset into the playback or (“watch time”) is displayed at  260   c  and the total running time of the video is displayed at  260   d . In this illustration, the watch time is 1 minute and 15 seconds, and the total running time is 2 minutes and 33 seconds. A rating  262  indicates the average rating (e.g., three out of five stars) of all user ratings of the video and the total number of ratings (e.g., fourteen). By way of illustration, a video rating system can have rating values of one, two, three, four or five stars, with one star indicating strong dislike and five stars indicating strong enjoyment on the part of the user rating the video. Other rating systems are possible, however. The total number of users who have viewed the video (or “view count”)  264  in part or in its entirety is also presented. 
     In various implementations, a system described below with reference to  FIG. 3  maintains user feedback data including user ratings, watch times, view counts, and other data. User interactions with a video are logged. For example, the amount of time a user plays the video is logged as a watch time for the video. In some implementations, the watch time is the total time the user spent watching the video before navigating off of the web page. The watch time can be unaffected by whether the user watched portions of the video out of sequence or skipped portions. If the watch time for a video is not known (e.g., because the video is not hosted by a system that keeps track of user watch times), the watch time can be estimated based on when users navigate off of the web page for the video. For example, a web browser plug-in or other software can monitor when users navigate off of web pages and record this information. If the system hosting the video does not programmatically provide a view count for the video (e.g., through an application programming interface), the view count can be detected by a plug-in or other software that analyzes the web page for text indicating the view count. 
     User feedback data can also include user interaction counts, targeted interaction counts, and impression counts. As used herein, a user interaction represents users&#39; selections (e.g., clicks with a mouse) of search results that reference resources (e.g., videos or web pages). A targeted interaction represents a user interaction where the time duration the user interacts with the resource referred to by selected search result satisfies a threshold (e.g., the time duration is greater than a threshold or less than a threshold). For example, for videos a targeted interaction can be a user interaction where the watch time exceeds a threshold (e.g., one minute). For a web page, targeted interactions can be user interactions where a period of time beginning when a user selects a search result from a search web page and ending when the user returns to the search web page exceeds a threshold (e.g., thirty seconds, one minute, two minutes, five minutes). An impression is a presentation of a search result that references a resource. In some implementations, an impression is limited to presentations of a search result identifying the resource where the search result is ranked among the top search results for the query (e.g. in the top twenty search results, on the first page of search results). 
     Several quality metrics may be determined based on user interactions and targeted interactions. For example, the number of targeted interactions for a resource may be divided by the number of impressions of the resource. Another quality metric may be calculated by dividing the count of the number of targeted interactions for the resource by the number of targeted interactions with any resource responsive to the query. Examples of quality metrics include a traditional quality metric, a targeted interaction quality metric, and an impressions quality metric. 
     The traditional quality metric is described by the formula:
 
TQM=TI i /ΣTI
 
     Where the TQM represents a traditional quality metric. TI i  is the number of targeted interactions for a particular resource responsive to a query. TI is a targeted interaction for any resource responsive to the same query. 
     The targeted interaction quality metric is described by the formula:
 
TIQM=TI/ I  
 
     Where the TiQM represents the Targeted Interactions Quality Metric, TI is the number of targeted interactions for a particular resource. I is the number of user interactions with the resource. 
     The impression quality metric is described by the formula:
 
IMPQM=TI/IMP
 
     Where IMPQM represents the impressions quality metric, TI is the number of targeted interactions for a particular resource. IMP is the number of impressions of the resource. 
       FIG. 3  illustrates an exemplary search system  300  for identifying search results in response to search queries as can be implemented in an Internet, intranet, or other client/server environment. The system  300  is an example of a search system in which the systems, components and techniques described herein can be implemented. Although several components are illustrated, there may be fewer or more components in the system  300 . Moreover, the components can be distributed on one or more computing devices connected by one or more networks or other suitable communication mediums. 
     A user  302  ( 302   a ,  302   b ,  302   c ) interacts with the system  300  through a client device  304  ( 304   a ,  304   b ,  304   c ) or other device. For example, the client device  304  can be a computer terminal within a local area network (LAN) or wide area network (WAN). The client device  304  generally includes a random access memory (RAM)  306  (or other memory and/or a storage device) and a processor  308 . The processor  308  is structured to process instructions on the client device  304 . The processor  308  is a single or multi-threaded processor having one or more processor cores, for example. The processor  308  is structured to process instructions stored in the RAM  306  (or other memory and/or a storage device included with the client device  304 ) to display graphical information for a user interface. 
     The RAM  306  on the client device  304  includes a tracker software program  360  for keeping track of user interactions and targeted interactions. For example, in the case of video resources, the tracker program can maintain a count of views, watch times, and user ratings of videos on the client device  304 . The tracker  360  can send the tracked data as a client-side signal  368   a  into the network  312  (e.g., the Internet or other network). The data is forwarded to an analysis system  364  as a signal  368   b . The analysis system  364  generally includes a RAM  367  (or other memory and/or a storage device) and a processor  366 . The processor  366  is structured to process instructions on the analysis system  364 . The processor  366  is a single or multi-threaded processor having one or more processor cores, for example. The RAM  367  includes an analyzer software program  362  for analyzing the tracking data  368   b  in order to derive or update predictor and voting functions. The tracking data  368   b  can be stored in one or more tracking logs  369  which are used to record the collected information for multiple users and resources. In various implementations, the recorded information includes log entries that indicate the IP (Internet Protocol) address of the client  304  which transmitted the information, the type of data (e.g., view count, watch time, user rating, time durations for targeted interactions), and a value for the data. 
     A user  302   a  connects to the search engine  330  within a server system  314  to submit a query. When the user  302   a  submits the query through an input device attached to a client device  304   a , a client-side query signal  310   a  is sent into the network  312  and is forwarded to the server system  314  as a server-side query signal  310   b . Server system  314  can be one or more server devices in one or more locations. A server system  314  includes a memory device  316 , which can include the search engine  330  loaded therein. A processor  318  is structured to process instructions within the device  314 . These instructions can implement one or more components of the search engine  330 . The processor  318  can be a single or multi-threaded processor and can include multiple processing cores. The processor  318  can process instructions stored in the memory  316  related to the search engine  330  and can send information to the client devices  304   a - c , through the network  312 , to create a graphical presentation in a user interface of the client device  304  (e.g., a search results web page displayed in a web browser). 
     The server-side query signal  310   b  is received by the search engine  330 . The search engine  330  uses the information within the user query (e.g. query terms) to find relevant resources (e.g., web pages, videos). The search engine  330  can include an indexing engine  320  that actively searches a corpus (e.g., web pages on the Internet) to index the resources found in that corpus, and the index information for the resources in the corpus can be stored in an index database  322 . This index database  322  can be accessed to identify resources related to the user query. Note that a resource does not necessarily correspond to a file. A resource can be stored in a portion of a file that holds other resources, in a single file dedicated to the resource in question, or in multiple coordinated files. Moreover, a resource can be stored in a memory without having first been stored in a file. 
     The search engine  330  includes a ranking engine  352  to rank the resources related to the user query using a scoring or ranking algorithm. The ranking of the resources can be performed using traditional techniques for determining an information retrieval (IR) score for indexed resources in view of a given query, for example. The relevance of a particular resource with respect to a particular search term or to other provided information may be determined by any appropriate technique. 
     To further improve such traditional resource ranking techniques, the ranking engine  352  receives quality signals from a quality rank modifier engine  356  to assist in determining an appropriate ranking for search results. As discussed above, in some implementations, the quality rank modifier engine  356  calculates the quality signal using a quality metric calculated from user feedback data  370  stored in tracking logs  369 . 
     The search engine  330  forwards the final, ranked result list within a server-side search results signal  328   a  through the network  312 . Exiting the network  312 , a client-side search results signal  328   b  is received by the client device  304   a  where the results are stored within the RAM  306  and/or used by the processor  308  to display the results on an output device for the user  302   a . The server system  314  may also maintain one or more user search histories based on the queries it receives from a user and which results that a user selected after a search was performed. 
       FIG. 4  is a diagram illustrating example user interfaces for search systems. In this example, a first user interface  408  is provided by a video search system  412 , the video search system may include a server system  414 , for example, the server system  314  of  FIG. 3 , and an analyzer system  416 , for example, the analyzer system  364  of  FIG. 3 . A second user interface  410  is provided by a web search system  418 . The web (or “universal”) search system  418  includes a server system  420  and an analyzer system  422 . 
     In this example, the video search system  412  provides video search results “Palm Tree Removal”  404 , “Pruning Palm Trees”  402 , and “Tree &amp; Plant Care: Palm Trees”  400  in response to receiving a query, “palm tree,” as shown by the text in the search text area  424  on the user interface  408 . The video search system provides links to the videos as part of the search results. Based on the ranking algorithm used by the first search system  412  and on user feedback data maintained by the analyzer system  416 , “Pruning Palm Trees”  402  is displayed first (i.e., has the highest rank), “Palm Tree Removal”  404  is displayed second (i.e., has the second highest rank), and “Tree &amp; Plant Care: Palm Trees”  400  is displayed third (i.e., has the third highest rank). 
     The web search system  418  provides a wider variety of resources as search results responsive to receiving the same query, “palm tree,” as shown by the text entered into the search text area  426  on the user interface  410 . The search results from the web search system include the video search result “Tree &amp; Plant Care: Palm Trees”  400  in addition to links to assorted web pages and images  430 . 
     Based on the ranking algorithm used by the second search system  420  and user feedback data maintained by the analyzer system  422 , the search system  418  ranks “Tree &amp; Plant Care Palm Trees”  400  highest. 
     The difference in the order of search results between the two search systems in this example is based, at least in part, on differences in the user feedback data in the video search system  412  and user feedback data in the web search system  418 . These differences may be caused by many factors both legitimate and idiosyncratic. For example, typical users of the video search system may have different interests than typical users of the web search system. The video search system may be a less popular fall back search system when compared to the Web search system, therefore, many of the users who search on the video search system have already explored the videos provided in the Web search system. The video search system may have only a few users, therefore the preferences of a few individual may disproportionately affect the user feedback data when compared to a larger user population of the Web search system. 
       FIG. 5  illustrates a diagram of an example video search system using the user feedback data from a Web search system. A video search system  502   a  contains a quality rank modifier engine  504   a  that references user feedback data  506   a , referred to as primary user feedback data. The web search system  502   b  contains a quality rank modifier engine  504   b  that references a user feedback data  506   b . Represented by process arrow  510 , the video quality rank modifier engine  504   a  accesses the user feedback data  506   b  of the web search system  502   b , as secondary user feedback. 
     Generally, primary user feedback data refers to user feedback data from the search system performing the ranking and secondary user feedback data refers to user feedback data taken from another search system. In some implementations, the quality rank modifier engine  504   a  combines primary user feedback data for a given video with the secondary user feedback data. The quality rank modifier engine creates a combined quality metric  508  from the combined user feedback data. 
     In some scenarios, the secondary user feedback data  506   b  may contain significantly more data than the primary user feedback data  506   a . The difference in the quantity of user feedback data presents a risk of bias. The secondary user feedback data could dominate the combined quality metric  508 . The unique interests of users of the primary video search system would be discounted. To prevent the secondary user feedback data from overwhelming the primary user feedback data, a weight is applied to the secondary user feedback data. The weight is based on the primary user feedback data and a smoothing factor. The smoothing factor identifies a threshold quantity of primary user feedback data. As the amount of primary user feedback data approaches the smoothing factor the weight of the secondary user feedback data decreases. When the primary user feedback data is equal to or greater than the smoothing factor, the secondary user feedback data no longer affects the combined quality metric. In this way, the smoothing factor provides a threshold quantity of primary user feedback data, above this threshold the secondary user feedback data is not used. 
     In some implementations, as an optimization, the combined quality metrics are not calculated when the quantity of primary user feedback data is greater than the smoothing factor. 
     In some implementations, the smoothing factor is selected to balance the risk of bias with the benefits of utilizing the secondary user feedback data. On the one hand, the users of the different systems have different preferences which should be respected. On the other hand, a small quantity of primary user feedback data allows individuals to skew video search results based on their idiosyncratic preferences. If the smoothing factor is too large then the secondary user feedback data can overwhelm the primary user feedback data, ignoring legitimate differences. If the smoothing factor is too small then the secondary user feedback data is underutilized, resulting in search results being skewed by the idiosyncrasies of a small number of users. 
     To achieve balance, the smoothing factor is based, in part, on the quality metric being calculated. For example, the smoothing factor for the traditional quality metric may be different from the smoothing factor for the targeted interactions quality metric. For example, when utilizing secondary feedback data from a video search system the smoothing factor for the traditional quality metric can be 25; the smoothing factor for the targeted interaction quality metric can be 10,000; and the smoothing factor for the impression quality metric can be 0. Other smoothing factors are possible. 
     The smoothing factor is also based, in part, on the source of the secondary user feedback data. For example, the smoothing factor applied to user feedback data from a general Web search system may be different from the smoothing factor applied to the user feedback data from a dedicated video search system. In some embodiments, the smoothing factor may be customized for specific video search systems. For example, when utilizing secondary feedback data from a general Web search system, the smoothing factor for the traditional quality metric can be 0. The smoothing factor for the targeted interaction quality metric may be 10,000. The smoothing factor for the impression quality metric can be 0. Other smoothing factors are possible. 
     In one implementation, user feedback data from search systems that have different ranking algorithms can be combined into a combined quality metric based on the following calculations. 
     Generally, the quality metrics are defined by the fraction 
     
       
         
           
             n 
             d 
           
         
       
     
     where n represents a measure of targeted interactions with the resource and d represents a measure of a larger set of user feedback, for example, all user interactions with the resource, targeted interactions with any resource provided as a search result, or impressions of the resource. 
     In some implementations, the weight applied to the secondary user feedback data is determined based on the following formula; 
     
       
         
           
             weight 
             = 
             
               min 
               ⁡ 
               
                 ( 
                 
                   1.0 
                   , 
                   
                     max 
                     ⁡ 
                     
                       ( 
                       
                         0.0 
                         , 
                         
                           
                             ( 
                             
                               smooth 
                               - 
                               
                                 d 
                                 1 
                               
                             
                             ) 
                           
                           
                             d 
                             2 
                           
                         
                       
                       ) 
                     
                   
                 
                 ) 
               
             
           
         
       
     
     where weight represents the weight, smooth is the smoothing factor, d 1  is the size of the larger population of primary user feedback data and d 2  is the size of the larger population of secondary user feedback data. The function min calculates to the minimum value of two arguments, and max calculates to the maximum value of two arguments. By decreasing the weight as the larger population of primary user feedback data increases, the risk of bias is managed. Secondary user feedback data is only relied upon when insufficient primary user feedback data exists to exceed the smoothing factor. 
     In some implementations, the combined quality metric is calculated based on the following formula: 
     
       
         
           
             cqm 
             = 
             
               
                 
                   n 
                   1 
                 
                 + 
                 
                   weight 
                   * 
                   
                     n 
                     2 
                   
                 
               
               
                 
                   d 
                   1 
                 
                 + 
                 
                   weight 
                   * 
                   
                     d 
                     2 
                   
                 
               
             
           
         
       
     
     Where cqm is the combined quality metric, n 1  is the number of targeted interactions from the primary user feedback data, n 2  is the number of targeted interactions from the secondary user feedback data, weight is the calculated weight, d1 is the size of the larger population of primary user feedback data and d2 is the size of the larger population of secondary user feedback data. 
       FIG. 6  illustrates example search result rankings being adjusted based on combined quality metrics. A video, “Tree &amp; Plant Care: Palm Trees”  600 , is indexed by two different search systems  602   a ,  602   b . Each search system maintains user feedback data. For example, a first search system  602   a  maintains first user feedback data in a data store  606   a . Similarly, a second search system  602   b  maintains second user feedback data in a data store  606   b . The quality rank modifier engine  604   a  for the first search system  602   a  uses first user feedback data  606   a  to adjust the ranking for search results. In this example, user interface  608   a  shows a search for “palm tree.” The video, “Tree &amp; Plant Care: Palm Trees”  600 , appears at the top of the rankings. In second search system  602   b , the quality rank modifier engine  604   b  uses the second user feedback data  606   b  combined with the first user feedback data  606   a  to adjust the ranking for search results. In this example, the first user feedback data affects the combined quality metrics for the “Tree &amp; Plant Care: Palm Trees’ video  600 . As a result (as represented by the double arrow  616 ) the increase in the score attributable to the combined quality metric is sufficient to promote the “Tree &amp; Plant Care: Palm Trees” video  600  ahead of the “Palm Tree Removal” video  612 . However, the “Tree &amp; Plant Care: Palm Trees” video  600  remains below the “Pruning Palm Trees” video  614 . 
       FIG. 7  is a flowchart illustrating an example process for combining user feedback. The example process  700  may be implemented by a quality rank modifier engine, for example the quality rank modifier engine  356  of  FIG. 3 . 
     The process obtains search results ( 702 ). Search results may be obtained from a search system, for example the index engine  320  of  FIG. 3 . In general, search results are obtained which are responsive to a query. The search results are provided a score based on a ranking algorithm. 
     The process obtains primary user feedback data ( 704 ). The primary user feedback data represents interactions previous users of the system have had with the search result. For example, the primary user feedback data may include the number of targeted interactions, the number of user interactions, and the number of impressions. The primary user feedback data may also include user feedback data for other videos, for example, the number of targeted interactions for any video responsive to the query. 
     The process obtains secondary user feedback data ( 706 ). The secondary user feedback data is user feedback data provided from a second search system. The secondary user feedback data represents interactions previous users of the second search system have had with the search result. For example, the secondary user feedback data may include the number of targeted interactions, the number of user interactions, and the number of impressions. The secondary user feedback data may also include user feedback data for other videos, for example, the number of targeted interactions for any video responsive to the query. 
     The process applies a weight to the secondary user feedback ( 708 ). The weight applied to the secondary user feedback may be determined based on a smoothing factor, the primary user feedback data, and the secondary user feedback data. In general, the larger the sample size of primary user feedback the smaller the weight applied to the secondary user feedback. For example, if the primary user feedback data contains a small sample set the secondary user feedback has greater weight than if the primary user feedback contains a larger sample set. 
     The process aggregates the primary and secondary user feedback data ( 710 ). The process calculates combined user feedback data based on the primary user feedback data, the secondary user feedback data and the weight. 
       FIG. 8  is a schematic diagram of an example system configured to derive a combined quality metric. The system generally consists of a server  802 . The server  802  is optionally connected to one or more user or client computers  890  through a network  870 . The server  802  consists of one or more data processing apparatuses. While only one data processing apparatus is shown in  FIG. 94 , multiple data processing apparatus can be used. The server  802  includes various modules, e.g. executable software programs, including an analyzer  804  for analyzing the tracking data in order to derive or update predictor and voting functions. A quality rank modifier  806  is configured to calculate the quality metrics of a video on the fly using the predictor and voting functions derived by the analyzer  804 . Alternatively, the quality metrics can be calculated for videos ahead of time so that the quality rank modifier engine  806  only needs to look up the value for a given video. A ranking engine  808  ranks videos responsive to a query which were identified using one or more indexes maintained by the indexing engine  810 . The ranking engine  808  can use the quality signal provided by the quality signal engine  806  as an additional input to is ranking algorithm. 
     Each module runs as part of the operating system on the server  802 , runs as an application on the server  802 , or runs as part of the operating system and part of an application on the server  802 , for instance. Although several software modules are illustrated, there may be fewer or more software modules. Moreover, the software modules can be distributed on one or more data processing apparatus connected by one or more networks or other suitable communication mediums. 
     The server  802  also includes hardware or firmware devices including one or more processors  812 , one or more additional devices  814 , a computer readable medium  816 , a communication interface  818 , and one or more user interface devices  820 . Each processor  812  is capable of processing instructions for execution within the server  802 . In some implementations, the processor  812  is a single or multi-threaded processor. Each processor  812  is capable of processing instructions stored on the computer readable medium  816  or on a storage device such as one of the additional devices  814 . The server  802  uses its communication interface  818  to communicate with one or more computers  890 , for example, over a network  870 . Examples of user interface devices  820  include a display, a camera, a speaker, a microphone, a tactile feedback device, a keyboard, and a mouse. The server  802  can store instructions that implement operations associated with the modules described above, for example, on the computer readable medium  816  or one or more additional devices  814 , for example, one or more of a floppy disk device, a hard disk device, an optical disk device, or a tape device. 
     Embodiments of the subject matter and the operations described in this specification can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions, encoded on computer storage medium for execution by, or to control the operation of, data processing apparatus. Alternatively or in addition, the program instructions can be encoded on an artificially-generated propagated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal, that is generated to encode information for transmission to suitable receiver apparatus for execution by a data processing apparatus. A computer storage medium can be, or be included in, a computer-readable storage device, a computer-readable storage substrate, a random or serial access memory array or device, or a combination of one or more of them. Moreover, while a computer storage medium is not a propagated signal, a computer storage medium can be a source or destination of computer program instructions encoded in an artificially-generated propagated signal. The computer storage medium can also be, or be included in, one or more separate physical components or media (e.g., multiple CDs, disks, or other storage devices). 
     The operations described in this specification can be implemented as operations performed by a data processing apparatus on data stored on one or more computer-readable storage devices or received from other sources. 
     The term “data processing apparatus” encompasses all kinds of apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, a system on a chip, or multiple ones, or combinations, of the foregoing The apparatus can include special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit). The apparatus can also include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, a cross-platform runtime environment, a virtual machine, or a combination of one or more of them. The apparatus and execution environment can realize various different computing model infrastructures, such as web services, distributed computing and grid computing infrastructures. 
     A computer program (also known as a program, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, declarative or procedural languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, object, or other unit suitable for use in a computing environment. A computer program may, but need not, correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub-programs, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network. 
     The processes and logic flows described in this specification can be performed by one or more programmable processors executing one or more computer programs to perform actions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit). 
     Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and data from a read-only memory or a random access memory or both. The essential elements of a computer are a processor for performing actions in accordance with instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto-optical disks, or optical disks. However, a computer need not have such devices. Moreover, a computer can be embedded in another device, e.g., a mobile telephone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a Global Positioning System (GPS) receiver, or a portable storage device (e.g., a universal serial bus (USB) flash drive), to name just a few. Devices suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry. 
     To provide for interaction with a user, embodiments of the subject matter described in this specification can be implemented on a computer having a display device, e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, for displaying information to the user and a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input. In addition, a computer can interact with a user by sending documents to and receiving documents from a device that is used by the user; for example, by sending web pages to a web browser on a user&#39;s client device in response to requests received from the web browser. 
     Embodiments of the subject matter described in this specification can be implemented in a computing system that includes a back-end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front-end component, e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the subject matter described in this specification, or any combination of one or more such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (“LAN”) and a wide area network (“WAN”), an inter-network (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks). 
     The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. In some embodiments, a server transmits data (e.g., an HTML page) to a client device (e.g., for purposes of displaying data to and receiving user input from a user interacting with the client device). Data generated at the client device (e.g., a result of the user interaction) can be received from the client device at the server. 
     While this specification contains many specific implementation details, these should not be construed as limitations on the scope of any inventions or of what may be claimed, but rather as descriptions of features specific to particular embodiments of particular inventions. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination. 
     Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products. 
     Thus, particular embodiments of the subject matter have been described. Other embodiments are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In certain implementations, multitasking and parallel processing may be advantageous.