Abstract:
A computer system, method, and media for associating locations with ranked websites are provided. The computer system includes a search engine, a log database, and a location database that are employed to respond to search requests from users by returning appropriately ranked websites to the user. The websites are ranked using the location of the website and the location of the user to select websites to receive high ranks. Additionally, the search engine includes a correction feature that reevaluates locations for a website or user when a large number of obtained locations suggest a different location than a currently associated location for the website or the user.

Description:
BACKGROUND 
     Conventionally, a search engine ranks websites based on a frequency of terms within a website that matches the terms included in a user query. The conventional search engine returns the ranked websites as a result set to the user. Additionally, the conventional search engine considers a location of a ranked website to decide whether the ranked website is of interest to the user. For instance, a website operated and located in the United States of America would more likely be of interest to a user in the United States of America accessing the Internet from a machine in the United States of America than a similar website operated and located in a foreign country. The conventional search engine is configured to return, within the top result set, the website operated and located in the United States of America. The similar website located and operated in the foreign country is not returned within the top result set. 
     The conventional search engine selects a location of a website by parsing a top level domain of a uniform resource locator of the website to extract a country identifier that corresponds to the location of the website. Alternatively, the conventional search engine selects a location of the website by identifying an internet protocol address corresponding to the uniform resource locator. The internet protocol address for the website corresponds to a specific location that is associated with the website. 
     The conventional search engines introduce errors when selecting top results using location. The conventional search engines erroneously omit a website that is globally popular from the top results set. For example, a website that individuals all over the world frequently access and operated but is located in the United States of America may be erroneously excluded from the top result set for users located in foreign countries. Because the website is operated and located in the United States of America, the conventional search engines erroneously omit websites from the top result set that should be included in the top result set for the foreign countries. 
     SUMMARY 
     A computer system includes a search engine that responds to user queries. The search engine receives the user queries and returns a result set that includes websites that match the user query. The search engine is configured with a location service that identifies locations for users and websites. The location service retrieves locations for users and websites from a location database. In turn, the location service uses the locations retrieved from the location database to identify unexpectedly popular websites. Additionally, the location service locates inconsistencies in the locations retrieved from the location database and resolves the inconsistencies in the retrieved locations. The locations selected by the location service for users and websites are used to impact ranks assigned to websites that match the user query. Accordingly, the search engine uses the location service to analyze, among other things, popularity of the websites, locations of the websites, and the locations of the users to generate the top results that are included in the result set. 
     This Summary is provided to introduce a selection of concepts in a simplified form. The selection of concepts 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 as an aid in determining the scope of the claimed subject matter. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a network diagram that illustrates an exemplary operating environment; 
         FIG. 2  is a logic diagram that illustrates a computer-implemented method for associating locations with websites based on user locations; 
         FIG. 3  is a logic diagram that illustrates a computer-implemented method for associating locations with websites based on anchor locations; 
         FIG. 4  is a logic diagram that illustrates a computer-implemented method for ranking a website based on location. 
     
    
    
     DETAILED DESCRIPTION 
     This patent describes the subject matter for patenting with specificity to meet statutory requirements. However, the description itself is not intended to limit the scope of this patent. Rather, the inventors have contemplated that the claimed subject matter might also be embodied in other ways, to include different steps or combinations of steps similar to the ones described in this document, in conjunction with other present or future technologies. Moreover, although the terms “step” and “block” may be used herein to connote different elements of methods employed, the terms should not be interpreted as implying any particular order among or between various steps herein disclosed unless and except when the order of individual steps is explicitly described. Further, embodiments are described in detail below with reference to the attached drawing figures, which are incorporated in their entirety by reference herein. 
     As utilized herein, the term “anchors” refers to links within the content of a website that point to additional content, such as a website or other multimedia content. Additionally, as utilized herein, the term “component” refers to any combination of hardware, software, or firmware. 
     A search engine is configured with location services that impact a rank for a website matching a user query. The location service analyzes location data for websites and users to classify websites that are globally popular. Moreover, the location service identifies a location of a website by looking at locations of: users that visit the website; the top-level domain; and the locations of server devices that host the website. In some embodiments, the location service selects a common location associated with a large number of visitors to the website to be the location of the website even if the website is hosted at a different location. Moreover, the location service may identify a website as globally popular when users all over the world frequently access the website. 
     In some embodiments, the location service includes a User Location Bias (ULB) component to affect the rankings for websites that match the user query. The ULB component identifies websites that are unexpectedly popular for a location associated with the website. The unexpectedly popular websites are returned to the search engine for inclusion in the top result set generated by the search engine in response to the user query. When the search engine includes the unexpectedly popular websites in the top results, the rank for the unexpectedly popular websites are updated to reflect its position in the top result set. Alternatively, the location service may use static location sources to impact the rank for each website that is a candidate for the top result set. 
     Additionally, the location service includes a correction component that identifies inconsistencies between locations for websites received from a location database and locations for website assigned by the location service. The inconsistencies between the locations for websites are resolved by the correction component based on locations for users. Alternatively, the correction component identifies inconsistencies between location for users received from a location database and locations for users assigned by the location service, and the inconsistencies between the locations for users is resolved based on locations for websites accessed by the users. 
       FIG. 1  is a network diagram that illustrates an exemplary operating environment  100 . The operating environment  100  includes a network  110 , a search engine  120 , client devices  130  configured with search toolbars  140 , and location database  150 , log data  160 , location service  170 , and websites  180 , and index data  190 . 
     The websites  180  are documents that represent HyperText Markup Language pages or other content. The websites  180  are addressed using uniform resource locators. Additionally, the websites  180  include anchors that link to other websites or additional content. In an embodiment, the websites  180  may be formatted in extensible markup language or any other markup language. 
     The network  110  is configured to facilitate communication between the client devices  130  and the search engine  120 . The network  110  may be a communication network, such as a wireless network, local area network, wired network, or the Internet. In an embodiment, the client devices  130  communicate search queries to the search engine  120  utilizing the network  110 . In response, the search engine  120  may communicate result sets having websites that match terms included in the search requests. 
     The search engine  120  is a computing device that provides search results in response to search queries. In some embodiments, the search engine  120  is configured to execute on a server device. The search engine  120  receives search queries from the client devices  130 . The search queries are processed by the search engine  120  to identify websites  180  that match terms included in the search queries. Additionally, the search queries are processed by the search engine  120  to traverse the location database  150  to identify locations for the client devices  130  that transmitted the search queries to the search engine  120 . In turn, the search engine  120  transmits search results that include the websites that match the terms included in the search queries received from the client devices  130 . 
     In certain embodiments, the client devices  130  may transmit user activity logs, such as, but not limited to, tool bar logs, to the search engine  120 . The search toolbar  140  is a utility installed on the client devices  130 . The search toolbar  140  logs and track the user&#39;s visits to websites  180  on the network  110 , submits the user activity logs having websites visited by the user to the search engine  120 , and submits an internet protocol address of the client devices  130  to the search engine  120 . In certain embodiments, the search toolbar  140  is a component of the user&#39;s web browser that logs browse activity for every website  180  that the user visits. Essentially, the user activity logs identify each page that the user visits, the user&#39;s internet protocol address, and other important browse activity. The user activity logs received from the client devices  130  are used by the search engine  120  to identify locations for users, to identify locations for websites, and to impact a rank of websites that match terms included in user search queries in accordance with analysis performed by the location service  170  of the search engine  120 . 
     The location database  150  is a database the stores location data for websites and users. In some embodiments, the location database is a lookup table having location data and internet protocol address data. For each uniform resource locator, website, and internet protocol address, the lookup table includes a corresponding location. The location may specify the country, state, or municipality for a website having the specified uniform resource locator. The lookup table includes entries having an internet protocol address and corresponding location. For instance, the lookup table may store uniform resource locator “www.va.com.au” having internet protocol address 130.194.1.99 that corresponds to a machine located in Clayton, Victoria, Australia. The location database  150  may return the location for a website or a user based on the internet protocol address received from the search engine  120 . In some embodiments, the location database  150  may be updated and maintained by a third-party. For instance, the third-party may be Quova SM . 
     The log data  160  is a database that stores browse activity, such as query-click activity observed by the search engine  120 . The query-click activity includes records queries that the user issues to the search engine  120  and records of clicks a user initiates on results returned by the search engine  120 . Additionally, the log data  160  stores the browse activity collected from the search toolbar  140 . The toolbar logs from the client devices  130  are stored in the log data  160 . The index data  190  stores data for each website indexed by the search engine. The index data  190  allows the search engine to quickly compare search terms received from the user to the terms in the index  190  to find matches that are returned as a set of websites  180  that match the search terms. The index data  190  stores, among other things, uniform resource locators that correspond to each website associated with the anchors. 
     The search engine  120  is configured with location service  170 . The location service  170  generates a location profile for a user or a website based on the log data  160 . The location service  170  also impacts ranks for the websites, suggests corrections to locations obtained from the location database  150 , and promotes a website within the search results returned to a user of the search engine based on the location profile for the user and the website. The location service  170  comprises a rank component  171 , a correction component  172 , a ULB component  173 , and a promoter component  174 . 
     The rank component  171  generates a rank for each website  180  that matches the terms included in the user search query. The rank component  171  assigns a rank to each website  180  based on multiple factors. In some embodiments, the rank component  171  performs a statistical analysis on the terms in the search query and the terms in website  180 . The larger a statistical overlap between terms in the search query and the terms in the website  180 , the higher the rank. Additionally, the rank component  171  evaluates the location of the website and the location of the user to assign the appropriate rank to the website  180 . 
     The correction component  172  locates inconsistencies in the locations obtained from the location database  150  and corrects the inconsistencies based on the aggregate locations observed for users or websites  180 . For instance, the location database  150  may return the United States of America as a location for a user based on internet protocol address sent to the location database  150  from the search engine  120 . However, after the correction component  172  observes the browse activity stored in the log data  160  for the user, the correction component  172  determines that 90% of websites visited by the user are located in Great Britain. Based on this information, the correction component  172  changes the location of the user from the United Stated of America to Great Britain. 
     The correction component  172  performs a similar process to correct locations for websites  180 . For instance, the location database  150  may return France as a location for a website based on an internet protocol address of the uniform resource locator sent to the location database  150  from the search engine  120 . However, after the correction component  172  observes the browse activity stored in the log data  160  for the website, the correction component  172  determines that 90% of the users visiting the website are located in the United States of America. Based on this information, the correction component  172  changes the location of the website from France to the United States of America. 
     Additionally, the correction component  172  classifies the websites  180  or users based on expected global statistical distribution of websites  180 . The correction component may use Kullback-Leibler (KL) divergence values received from the ULB component  173  to identify the appropriate location for a website. For instance, the correction component  172  may observe a particular user has the following distribution of websites: 40% of the websites visited by the user are in the United States of America; 40% of the websites visited by the user are in Great Britain; and 20% of the website are located elsewhere. The correction component  172  compares the user&#39;s distribution of websites to an expected distribution of all websites on the Internet. The correction component  172  aggregates the number of websites in the United States of America, the number of websites in Great Britain, and each of the remaining countries in the world. In turn, the correction component  172  compares the global distribution to the user distribution. For instance, if the global distribution of websites was the following: 40% of all websites are in the United States of America; 40% of all websites are in the Great Britain; and the remaining websites are distributed among other countries in the world, the correction component  172  is unable to suggest a correction for the location of the user, and the location obtained from the location database  150  is used to classify the user. However, if the user distribution indicated that 70% of the websites visited by the user are in Great Britain and only 5% of websites are located in the United States of America, the correction component  172  classifies the user as being located in Great Britain. The correction component  172  uses the log data  160 , the location database  150 , or the KL values to assign the user with an appropriate location. In some embodiments, the correction component  172  uses the anchors pointing to the website to obtain a location classification for a website  180 . Generally, the location of the anchor corresponds to location of the website that the anchor points to. For instance, if a website contains a disproportionately large number of anchors having a location of Great Britain but the website is classified as having France as the location, the correction component  172  updates the location from France to Great Britain. In some embodiments, the correction component  172  may assign priorities to locations received from the location database  150 , locations suggested by the correction component  172 , and locations suggested by the ULB component  173 . 
     The ULB component  173  impacts the rank assigned to a website. The ULB component calculates the Kullback-Leibler Divergence to find websites that are disproportionately popular at a specified locations: 
     
       
         
           
             
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     The “W” represents a website, the “L” represents a location, “P(W,L)” represents the probability that a user is accessing a website “W” from location “L,” and “P(L)” represents the probability that a user accessing any website is from location “L.” The ULB component  173  analyzes the log data  160  to calculate P(W,L) and P(L). The ULB component  173  uses the log data  160  to identify, for each website “W,” location “L” where the website “W” is highly popular. The ULB component  173  calculates the number of users who visit each website “W” from each location “L” based on the log data  160  and location information provided by the location database  150 . For example, the log data  160  for website: www.whistlerblackcomb.com may indicate the following: 
     USA=50,000; Canada=40,000; Australia=50; Germany=40; Ireland=10. 
     In other words of the total 90,100 users that visit the website www.whistlerblackcomb.com, 50,000 users are located in the United States of America, 40,000 users are located in Canada, 50 users are located in Australia, 40 users are located in Germany, and 10 users are located in Ireland. 
     In turn, the ULB component  173  calculates the following probability value: 
     
       
         
           
             
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     Additionally, the log data  160  and locations from the location database  150  may indicate that 30% of all users of the search engine and toolbar are located in the United States of America. The ULB component  173  calculates the probability value for a user of the search engine as:
 
 P (USA)=0.30.
 
     The ULB component  173  uses P(USA) and P(www.whistlerblackcomb.com, USA) to calculate KL(www.whistlerblackcomb.com, USA), which provides an indication of when a website is unusually popular. 
     
       
         
           
             
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     After evaluating KL(www.whistlerblackcomb.com, USA), the ULB component  173  may compare the KL divergence value to a threshold value, i.e., 0.25, to determine whether the website is unusually popular for the specified location. A high KL divergence value indicates that the website www.whistlerblackcomb.com is unusually popular in this location (USA). 
     The KL value compares what the ULB component  173  expects for website usage to what the ULB component  173  observes for a particular website and location. Generally, the KL value is a large number when the ULB component  173  observes a disproportionately large number of individuals accessing the particular website from a location that is unexpected and the KL value is a small number when ULB components  173  observes an expected number of individuals in the location are visiting the website. For a single website, the ULB component  173  may calculate high KL values for several different locations. 
     The ULB component  173  may use the KL values to improve the rank for the website  180 . If a website  180  has a KL value above a threshold for the user&#39;s location, then the website  180  is promoted in the ranking by the rank component  171 . In certain embodiments, the rank component  171  receives the KL value and incorporates the value in a neural network ranking algorithm that factors the query match, term frequency, KL value, and location of the user to assign a rank to the website. Moreover, the ULB component  173  may send the KL values to the correction component  172  to correct user or website locations by including the unexpectedly popular location as one of the correct locations for the website or user. In some embodiments, each website in an index associated with the search engine  120  is tagged with the locations where the KL value exceeds the threshold value. 
     In an alternate embodiment, the ULB component  173  is configured to analyze anchors pointing to a website obtained from index data. “P(W,L)” is calculated to represent the probability that an anchor pointing to a website “W” is from location “L.” “P(L)” is calculated to represent the probability that an anchor for any website is from location “L.” For instance, the ULB component  173  may observe that 60% of the anchors pointing to a website are from the United States of America, 30% of the anchors pointing to these websites are from Great Britain, and 10% of the anchors are from elsewhere. In turn, the ULB component  173  calculates the KL divergence value for the website for a particular location and compares the KL divergence value to the threshold value. When the KL divergence value is above the threshold value, the ULB component  173  sends the KL divergence value to the rank component  171  to assign an appropriate rank to the website, and sends the KL divergence value to the correction component  172  to associate the website with a proper location. 
     The promoter component  174  identifies country, state, and municipality information for a website  180 . The municipality information identifies the city, county, or town for the website  180 . The promoter component  174  receives location information from different sources selected from the following: Yellow Pages SM  data, open directory project (ODP), uniform resource locators, website content, or locations obtained from websites by the ULB component  173  using either log data or anchor data. The Yellow Pages SM  data is collected by a third-party and contains the website uniform resource locator and contact information, such as telephone number, state, city, and zip code. The ODP data includes data that is generated by a collection of editors. The editors receive and store contact information for each website that is included in the open directory project. The uniform resource locators or website content are parsed by the promoter component  174  to extract location information. For instance, the promoter component  174  may extract a country component from a uniform resource locator: http://www.va.com.au, the “au” portion of the uniform resource locator means Australia. Alternatively, the uniform resource locator or website content may include terms for a state or city. For instance, a page that has an address for Seattle, Wash. is relevant to the location Seattle, Wash. and is extracted by the promoter component  174 . 
     In turn, the promoter component  174  receives location information from one or more of these sources. In some embodiments, the promoter component  174  assigns a value of “1” to each source and aggregates the assigned value for a number of sources that return similar location information, i.e., state data or municipality data. The number of sources that return similar location information is sent to the rank component  171  to increase the rank for the website corresponding to obtained location information. 
     In an embodiment, the promoter component  174  sends the location information returned by the at least three sources to the correction component  172  to locate inconsistencies. For instance, the correction component  172  may observe that a website  180  classified with Texas as the location may have a large number of users from Washington state visiting the website  180 . The correction component  172  may suggest that the Washington state location is a better location. In turn, the correction component identifier Washington state as the high priority location for the website identifies Texas as a low priority location. 
     In an embodiment, the search engine  120  updates the index that is used to return results to the user to include one or more locations identified by the location service  170  for each website stored in the index. For instance, the search engine  120  may add “Seattle Wash. USA” to the index for SpaceNeedle.com based on the suggestions from the location service  170 . The location service  170  may suggest the location “Seattle Wash. USA” for the website SpaceNeedle.com because the log data  160  indicates that many users in Seattle visit the website SpaceNeedle.com. In some embodiments, the search engine  120  receives updates from the location service  170  and updates the index periodically, i.e., daily, weekly, monthly, or quarterly, etc, without user intervention. Alternatively, the updates may be performed manually after receiving the suggestions from the location service  170 . 
     Accordingly, location service  170  of the search engine may use, among other things, the location database  150 , KL divergence values, Yellow Pages SM  data, ODP data, page content, and uniform resource locators to identify a location for a website or a user. 
     One of ordinary skill in the art understands and appreciates the operating environment  100  has been simplified for description purposes and alternate operating environments are within the scope and spirit of the above description. 
     In certain embodiments, a search engine configured with location services associates a webpage with a location. The location service uses log data having browse activity for users of the search engine. Based on the browse activity the location service identifies a location for the website and returns suggestions that impact the rank assigned to the websites. 
       FIG. 2  is a logic diagram that illustrates a computer-implemented method for associating locations with websites based on user locations. The computer-implemented method initiates in step  210 . In step  220 , the location service of the search engine retrieves log data having uniform resource locators for websites from a plurality of users. The log data comprises tool bar logs and search logs. In turn, the location service organizes the log data based on websites, in step  230 . In step  240 , for each website, the location service obtains locations for the plurality of users from a location database. In step  250 , the location service groups the plurality of users based on the obtained locations. The location service counts a number of the plurality of users at each of the obtained locations, in step  260 . In step  270 , the location service selects the obtained locations having more of the plurality of users than any other obtained locations. In step  280 , the location service associates the selected location with the website. In step  290 , the computer-implemented method terminates. 
     In another embodiment, the search engine configured with the location services associates a webpage with a location. The location service uses index data having anchors pointing to the websites. Based on the anchors pointing to the website, the location service identifies a location for the website, and the identified location impacts the rank assigned to the websites. 
       FIG. 3  is a logic diagram that illustrates a computer-implemented method for associating locations with websites based on anchor locations. The computer-implemented method initiates in step  310 . The location service extracts anchors pointing to each website, in step  311 . In step  312 , for each website, the location service obtains locations for each anchor from a location database. In step  313 , the location service groups the anchors based on the obtained locations. In step  314 , the location service counts a number of anchors at each of the obtained locations pointing to the website. In step  315 , the location service selects the locations having more anchors than any other obtained location. In step  316 , the location service associates the selected location with the website. The computer-implemented method terminates in step  317 . 
     In other embodiments, the search engine configured with the location services alters a rank for a website based on location. The location services uses location information from at least three sources to obtain locations for a website. Based on the overlap and similarity of the locations from the at least three sources, a rank of the website related to the location is increased. 
       FIG. 4  is a logic diagram that illustrates a computer-implemented method for ranking a website based on location. The computer-implemented method initiates in step  410 . In step  420 , the location service selects a website. In step  430 , the location service identifies a location for the website from at least three sources. In step  440 , the location service counts a number of sources having similar locations for the website. In step  450 , the location service communicates with the search engine to increase a rank—based on the count—for the website in a result set returned to a user that issued a search query to a search engine. The user has a location similar to the identified location of the website. Additionally, in some embodiments, the search query issued by the user may include the identified location. The computer-implemented method terminates in step  460 . 
     In summary, a search engine is configured to identify locations for websites and users and to increase a rank for a website based on the location associated with the website. The search engine is configured to identify, among other things, a country, state, and municipally for each website. Additionally, the search engine may correct inconsistencies for location information of websites or users received from third parties. 
     The foregoing descriptions of the invention are illustrative, and modifications in configuration and implementation will occur to persons skilled in the art. For instance, while the present invention has generally been described with relation to  FIGS. 1-4 , those descriptions are exemplary. Although the subject matter has been described in language specific to structural features or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims. The scope of the invention is accordingly intended to be limited only by the following claims.