Patent Publication Number: US-2017371967-A1

Title: Relevancy-based domain classification

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present application claims benefit to U.S. patent application Ser. No. 13/301,510, entitled “Relevancy-Based Doman Name Classification” and filed on Nov. 21, 2011, which is a continuation of U.S. patent application Ser. No. 11/739,611, entitled “Relevancy-Based Domain Name Classification” and filed on Apr. 24, 2007, which claims benefit to U.S. Provisional Application 60/794,754, entitled “Relevancy-Based Doman Name Redirecting” and filed on Apr. 24, 2006. Each of these applications is hereby incorporated by reference for all they disclose and teach. 
    
    
     BACKGROUND 
     Directing Web users to relevant Web content can generate significant revenue for those content providers. For example, if a user is looking for information about purchasing an automobile, then an automobile dealership owning a website has an improved chance of making a sale or at least marketing to an apparently interested consumer if the user navigates or is directed to the dealership&#39;s website. As such, website owners are willing to pay for increasing traffic to their websites, especially when the increased traffic is concentrated with users particularly interested in the products and services their businesses offer. 
     To this end, website owners, and those who direct Web users to such websites, are challenged to distinguish which users are interested in which websites. In some existing approaches, banner ads may be associated with Web pages of similar subject matter (e.g., Ford may advertise on a Web page containing an article about automobile safety), banner ads may be served up based on cookie information on the user&#39;s system, and sponsored links may be shown on search engine results pages based on the search keywords entered by the user. Nevertheless, many other opportunities to capture relevant user interest are missed because it remains difficult to discern a user&#39;s navigation intentions from the information available during a browsing session. 
     SUMMARY 
     Implementations described and claimed herein address the foregoing problems by developing and maintaining historical relevance data about specific domains based on target context data and/or source context data. Such data may be used to classify the user&#39;s interest in attempting to visit a specific target domain and thereby redirect the user to a website expected to be aligned with the user&#39;s interest (or otherwise provide relevant new Web content). In various implementations, the historical relevance data is derived from target context data and/or from source context data and/or post-visit user behavior collected from previous attempts to visit a specific domain. The source and/or target context data collected from the current visit may also be used as context-factors to influence domain classification. Based on such historical context data, context factors from the current visit, and/or the domain address provided in the user&#39;s navigation request, a domain classifier consults the historical relevance data and provides the user with Web content that is identified as likely to be relevant to the user&#39;s interests (e.g., by redirecting the user to a designated Web site or providing designated Web content according to the user&#39;s anticipated interests). In an alternative implementation, the domain classifier generates one or more keywords representing a topic of interest and submits it to a system capable of serving relevant new Web content or identifying a relevant new content domain (e.g., serving up a new banner ad). 
     In some implementations, articles of manufacture are provided as computer program products. One implementation of a computer program product provides a tangible computer program storage medium readable by a computer system and encoding a computer program. Another implementation of a computer program product may be provided in a computer data signal embodied in a carrier wave by a computing system and encoding the computer program. Other implementations are also described and recited herein. 
     This Summary is provided to introduce a selection of 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 to limit the scope of the claimed subject matter. 
    
    
     
       BRIEF DESCRIPTIONS OF THE DRAWINGS 
         FIG. 1  illustrates a browsing configuration including an exemplary domain classifier. 
         FIG. 2  illustrates an exemplary domain classifier configuration providing access to Web content based on requests originating from links on Web pages. 
         FIG. 3  illustrates an exemplary domain classifier configuration providing access to Web content based on a request originating from a link on a search results page. 
         FIG. 4  illustrates an exemplary domain classifier configuration providing access to Web content based on request a originating from an address typed by a user into an address field. 
         FIG. 5  illustrates exemplary operations for relevancy-based domain classification. 
         FIG. 6  illustrates an exemplary system useful in implementations of the described technology. 
     
    
    
     DETAILED DESCRIPTIONS 
       FIG. 1  illustrates a browsing configuration  100  including an exemplary domain classifier  102 . A user can use a browser  104  at a client system to connect across a communications network  106  (e.g., the Internet, an intranet, an extranet, a mobile communications network, some combination thereof, etc.) to access Web content available provided by Web servers.  FIG. 1  illustrates various Web servers at various domains, including Domain A server  108 , Domain B server  110 , Domain C server  112 , and Domain D server  114 . 
     In one example, the user sends (e.g., POSTs) a request (through the client browser  104 ) to access content from the Domain A server  108 . A user can initiate such a request in a variety of ways, including selecting a hypertext link in a Web page that is displayed in the client browser  104 , selecting a hypertext link on a search results Web page, entering the address (e.g., domain address) of the Domain A server into an address field of an address bar in a browser, etc. 
     The request takes the form of a HyperText Transfer Protocol (HTTP) request  116  directed to a Uniform Resource Identifier (URI), a Uniform Resource Locator (URL) address, or other form of domain address. An example domain address for Domain A may be a domain name such as www.postage.com, although an IP address may also be used as a domain address. In this example, the Domain A server  108  no longer serves any content from the www.postage.com address and instead redirects the request  116  to a domain classifier  102  by way of an HTTP redirection response  118  specifying the domain address of the domain classifier  102 . The client browser  104  reacts to the redirection response by re-POSTing a similar request to the domain classifier  102 . In an alternative implementation, the address for the Domain A server  108  may have been merely transferred to the domain classifier  102  at some point in the past to avoid the intermediate redirection action. 
     Upon receiving the request  120  resulting from the redirection, the domain classifier  102  evaluates the request  120  and consults a domain classification database (not shown) that includes historical relevance data relating to previous attempts to access Domain A server  108 . In this process, the domain classifier  102  attempts to discern the user&#39;s interest in trying to access the Domain A server  108 . The domain classifier  102  may rely on various combinations of the domain address, target context factors, and source context factors. Target context factors represent context information collected about the target domain (i.e., the original target domain requested by the user&#39;s navigation). In contrast, the source context factors represent context information collected about the source of the request to the target domain, such as the user&#39;s browser configuration, the user&#39;s system configuration, the user&#39;s location, the user&#39;s Internet connection, etc. Both target context factors and source context factors may be evaluated with reference to the historical relevance information to identify a new content domain or to identify one or more keywords describing the user&#39;s perceived interest in attempting to access the target domain. 
     Based on analysis of the available information, the domain classifier  102  HTTP issues a redirection response  122  to redirect the client browser  104  to an appropriate Domain B server  110  believed to satisfy that interest. In reaction to the receipt of the redirection response  122 , the client browser  104  now re-POST a similar request  124  to the Domain B server  110 , which may, for example, including sponsored-links to Web content believed to be aligned with the user&#39;s interests. Domain B server  110  then returns Web content  124  that is expected to be of interest to the user. In another implementation, the domain classifier  102  generates one or more keywords intended to characterize the user&#39;s predicted interest and submits these to a computing system that can provide relevant Web content, such as a third-party ad server that can provide advertisements believed to be relevant to the user&#39;s interests or a third-party content management server that can provide documents (e.g., news, articles, papers, or other information) believed to be relevant to the user&#39;s interests. 
     In this manner, the owner of the Domain B server  110  is more likely to gain benefit by receiving a visit from a user believed to be interested in the Domain B content, and the owner of the domain classifier  102  may gain revenue by directing the potentially interested user to the Domain B server. It should be understood that, depending on the interest inferred by the domain classifier  102 , the user may have been redirected to one or more other domains, such as the Domain C server  112  or the Domain D server  114 . 
     For example, consider a user who selects a link that is embedded in an article (“www.postage.com/StampPriceIncrease.htm”) about an increase in stamp prices. The original article author intended to provide the link as a citation to a related article. However, over time, the owner of the www.postage.com domain removed the StampPriceIncrease.htm article or stopped maintaining the www.postage.com website altogether. Instead, the www.postage.com owner redirects requests to the www.postage.com domain so that requesting users are redirected to a domain classifier. The domain classifier receives the request, and consults a domain classification database that includes historical relevance data pertaining to previous attempts to visit the www.postage.com domain. For example, the domain classifier may have previously received many requests via the www.postage.com/StampPriceIncrease.htm link and determined that the historical target context data pertaining to www.postage.com suggests previous user interest in buying stamps. Based on this analysis, the domain classifier infers that the current user may have interest in buying stamps and therefore redirects the user to a Web page that includes content about buying stamps and perhaps that offers stamps for sale. 
     In an alternative implementation, the owner of www.postage.com could have merely transferred the domain address to the domain classifier at some previous point in time, thereby avoiding the need to maintain the www.postage.com server. In this implementation, the user&#39;s initial request is sent directly to the domain classifier through typical domain name server functionality, and the initial redirection action is unnecessary. In yet another implementation, the domain classifier may generate one or more keywords pertaining to the perceived user interest and pass those keywords to a third-party ad server to provide new Web content to the user. 
     As discussed, the historical relevance data stored in the domain classification database can record a variety of relevance data types. The database is initially seeded with relevance data and redirection websites or keywords associated with such relevance data. For example, the database may be seeded with an initial profile for www.postage.com: (1) if a request is made to www.postage.com from a typed-in URL in a browser&#39;s address bar from an IP address in the United States, the user is directed to the United States Postal Service website; and (2) if the same request is made from an IP address in the United Kingdom, the user is directed to the Royal Mail website. This example uses both the domain name and a source context factor collected from the current visit (e.g., the IP address of the source system making the request) to identify a redirection website expected to be of interest to the user, although other examples may employ various combination of domain address, source context factors, and/or target context factors to classify the request for the target domain and discern user interest. 
     Both historical relevance data and context factors may be collected and analyzed to generate and contribute to aggregated relevance data informing the domain classifier  206  about the possible interests of users attempting to visit the target domain. Context factors from one visit may be fed back as historical relevance data for subsequent requests to the target domain. Such data may be obtained from many different sources, including without limitation:
         Clickstream data—A clickstream is the recording of what a user clicks on while browsing a Web site to which he or she is redirected. Clickstream data may be collected on the path a user takes when navigating through the redirected website. Other clickstream data may track how many pages are served to the user from the redirected domain, how long it takes pages to load, how often the user hits the browser&#39;s back or stop button, how much data is transmitted before a user moves on, which pages the user lingers on, which items the user puts in or takes out of their shopping cart, which items the user purchases, which links the user selects to navigate away from the domain, etc. As the user clicks anywhere in the webpage, the action is logged to reveal usage patterns and therefore to discern interest. For example, if more people click who request www.postage.com linger or click through a Web page on stamp collecting than a Web page for the post office, the historical relevance data will likely emphasize stamp collecting in association with the domain address www.postage.com.   User session data—Session data may include browser type and version, operating system type and version, time of day, day of week, day of month, day of year, year, screen resolution, screen color, user&#39;s ISP domain name gTLD or ccTLD, Java enabled, Flash enabled, preferred language, etc.   IP address-related information of the requesting system—Based on the user/host machine&#39;s IP address, the domain classifier may discern many characteristics of the source, including geographic location (continent, country, US region, time zone, state, city, postal code, longitude/latitude, phone prefix, Nielsen Designated Market Area, US Census Bureau Metropolitan Statistical Area, industry/company type, company name, company size, connection type, connection speed, ISP, Claritas Prizm Cluster, etc.   Content—A scan of the previously viewed Web page may be used to generate a variety of data types that can be used to influence the domain classification. The scan can yield information that includes but is not limited to meta keywords; meta descriptions; page title; alt tags; other meta tags; body text; links; file names; anchor text; referring URL; relative proximity and density of information, keywords and concepts therein; anchor keywords; etc.   Other structured sources of information may be used to influence the domain classification, such as by submitting a domain address for analysis, retrieving context factors for a source or target domain, and/or analyzing the components (e.g., text, address fields, etc.) contained in a source or target domain address:
           Alexa Web Information Service—primary and subcategory, related categories, related sites by category, metadata, list of sites linking in, historical traffic rank, related links, rank, rank by country, rank by city, reach, pageviews, owner contact information, presence of adult content, media load time, relative load speed, content language, related keywords, other domains owned, title, description, creation date, presence of pop ups, thumbnail, links pointing out   Open Directory—current and historic categories and related categories as classified by the submitting Web site owner or Open Directory category editor   NAICS (North America Industry Classification System)—maps industries by keyword   Yahoo—product ratings, retail category and department, user reviews, number of questions that match a query, category of questions for a query, longitude/latitude, traffic information, search listing title, search listing description, cached page analysis, historical news, number of links in, directory category, related categories, cached pages, related terms and phrases, suggested spelling/misspelling variations, related analysis of the content, find related terms   Blogpulse—related discussions, topic popularity, recency, trends   Simulicious—related web sites by keyword tag   MSN—category, sensitive content, keyword extraction, demographics by URL or search term, commercial intention, geographic location, determine acronyms, related terms, determine misspellings, seasonality, search listing title, search listing description, cached page analysis, number of links in   Ask.com—search listing title, search listing description, cached page analysis, number of links in   SEMPunch—average click through rate for a term   Technorati—related keyword phrase tags   ZingoSoft—find related tags for a page   Del.icio.us—tags assigned, number of times bookmarked/popularity, analysis of tag descriptions, latitude/longitude, other triple tags, common tags for a URL   Compete—popularity and traffic statistics   Quantcast—keywords used to find this site, related sites, demographics of site visitors   Furl.net—tags assigned, number of times bookmarked   Pricegrabber—ecommerce shopping data   eBay—listings number, related listings, average price, descriptions, categories, buyer age, gender, geographic location   Amazon.com—product information, historical pricing   SenderBase—historical and current spam score   URL Trends—Google PageRank, Alexa Rank, links out, links in Google, links in Yahoo, links in MSN, links in Alexa, links in AltaVista, links in AllTheWeb, links in Teoma, links in IceRocket, links in Del.icio.us, links in FURL, Open Directory category, adult related content, title, description, online since, last updated, related search term keywords; all of this information currently and by historical date   Keyword Discovery—total searches and monthly searches for a given keyword phrase or term, related keyword phrases or terms, common spelling variations, degree of spelling variation difference, shopping term popularity, translation   Digg.com—popularity score   Trynt—extract related terms from a page   Find Forward—historical popularity   Archive.org—date page was created, scan of the historical page content (including, but not limited to meta keywords, meta descriptions, page title, alt tags, other meta tags, body text, links, file names, anchor text, referring URL and relative proximity and density of information, keywords and concepts therein; anchor keywords)   Google/Froogle—search listing title, search listing description, PageRank, number of news stories, notebooks containing a term or phrase or concept, related keyword terms, related links, related news, related videos, cached page, historical news, number of links in, historical news popularity, competitive or related advertisers, term popularity by country source, historical term popularity, historical news, retail fit, retail items for sale and descriptions, number of pages indexed   ZoomInfo—company or person description and information   Relevad—find related keyword terms and phrases and categorization, analyze text for common keyword terms, phrases or concepts   SpyFoo/VelocityScape—related terms used by competition, related terms, categories   TagThe.net—related keyword terms/tags of the page, title, page size, content type, geographic location of site, site language, date last modified   Tagtooga—title, description, popularity score, thumbnail, popularity score, inclusion of Flash, 10 important words on the page, inclusion ads, use of popunders, favicon, related categories, related tags   Topicalizer—top keywords and phrases and frequency/density for a given page, related keyword terms to a page or text block/phrase, category of a page, words per sentence, words per paragraph, lexical density, characters per word, syllables per word, longest word, shortest word, paragraph count, sentence count, sentences per paragraph, longest sentence, shortest sentence,   Thumbshots—thumbnail image of web sites, whether current or in their previous incarnations   Abbreviations/Acronyms/Synonyms—determine possible meaning of abbreviations/acronyms/synonyms (e.g., to identify additional keywords that may be associated with a specified target domain address)   Misspellings and Language Variations—(e.g., to identify additional keywords that may be associated with a specified target domain address)   
               

       FIG. 2  illustrates an exemplary domain classifier configuration  200  providing access to Web content based on requests originating from links on Web pages. In one implementation, a user selects a link  202  from a Web page  204 . A resulting request, which includes the domain address of the target website associated with the link, is transmitted to a domain classifier  206  (e.g., assume the domain address has been transferred from the original target website to the domain classifier&#39;s server). The domain classifier  206  evaluates the target domain address in the request (e.g., www.postage.com) against historical relevance data in a domain classification database  208 . The domain classifier  206  may also collect additional source context data and/or target context data pertaining to the current visit from one or more of the other source context information sources listed above. The context data about the current visit may be used as “search context factors”. Both the domain address and the context factors can be evaluated using the historical relevance data in the domain classification database  208  to allow the domain classifier  206  to identify a new website (e.g., one of Domain A server  210 , Domain B server  212 , and Domain C server  214 ) to which the browser should be redirected or to identify one or more keywords characterizing the user&#39;s perceived interest. After the redirection to the new website, the context factors of the current visit may be integrated into the historical relevance data to improve the accuracy of future redirections. 
     For example, the link  202  includes a domain address—“www.postage.com”. If the target website (“www.postage.com/prices.htm”) is not available at the domain address (and so the browser is redirected to the domain classifier) or if the domain address has been transferred to the computing system executing the domain classifier  206 , then the domain classifier  206  may consult the domain classification database  208  to infer the user&#39;s interest based on the domain name. In one scenario, the domain classifier  206  may redirect the browser to a website at Domain A server  210  that relates to stamp collection, because the domain classification data classifies the target domain address as being aligned with collecting stamps (e.g., based on the historical relevance data and the domain address alone, the domain classifier classifies www.postage.com as being associated with stamp collecting and redirects the browser to such as website). In a typical arrangement, the owner of the Domain A server  210  would compensate the domain classifier for the referral. Compensation may vary based on the user&#39;s activity at Doman A subsequent to the redirection. 
     However, the domain classifier  206  may further collect source and/or target context factors about the current visit from the source web page  204 , which can further inform the domain classification. For example, by scanning and indexing the content of the source web page  204 , the domain classifier  206  may observe a high frequency of words such as “post” and “office” and a low frequency of “collect” and “appreciate”, which can server as source context factors to be used in the domain classification. The domain classifier  206  may then identify a more appropriate domain and therefore redirect the browser to a different webpage at Domain B Server  212 , which has content pertaining to U.S. Postal Service stamp prices. 
     In yet another scenario, the domain classifier  206  may further collect source context factors about the current visit from the source IP address available in the client browser&#39;s request and thereby identify a geographical region in which the browser is operating. For example, in addition to the stamp price association made in the previous paragraph, the domain classifier  206  may detect that the browser is located in the United Kingdom, and therefore the domain classifier  206  may then identify and redirect the browser to a different webpage at Domain C Server  213 , which has content pertaining to Royal Mail stamp prices. 
     It should be understood that links from other web pages, such as pages  216  and  218 , are likely to spawn a different classification, particularly as more source and/or target context factors are added to the domain classification database. Therefore, requests through links from pages  216  and  218  will likely result in redirection to a different web page, because the combination of the domain address and context factors suggests different user interests. 
       FIG. 3  illustrates an exemplary domain classifier configuration  300  providing access to Web content based on a request originating from a link on a search results page. In one implementation, a user selects a link  302  from a search results page  304 . A resulting request, which includes the domain address of the target website, is transmitted to a domain classifier  306  (e.g., assume the domain address has been transferred from the original target website to the domain classifier&#39;s server). The domain classifier  306  evaluates the domain address in the request (e.g., www.postage.com) against historical relevance data in a domain classification database  308 . The domain classifier  306  may also collect context factors pertaining to the current visit from one or more of the other context information sources listed above. Both the domain address and the context factors can be evaluated using the historical relevance data in the domain classification database  308  to allow the domain classifier  306  to identify a new website (e.g., one of Domain A server  310 , Domain B server  312 , and Domain C server  314 ) to which the browser should be redirected (or to identify one or more keywords characterizing the user&#39;s perceived interest). After the redirection to the new website, the context factors of the current visit may be integrated into the historical relevance data to improve the accuracy of future redirections. 
     In the illustrated example, the search link  302  includes, as before, a domain address—“www.postage.com”. If the target website (“www.postage.com/prices.htm”) is not available at the domain address (and so the browser is redirected to the domain classifier) or if the domain address has been transferred to the domain classifier  306 , then the domain classifier  306  may collect the search keywords (e.g., as source context factors) from the search that resulted in the search results page and consult the domain classification database  308  to infer the user&#39;s interest from the domain address and the search keywords. In one scenario, the domain classifier  306  may redirect the browser to a website at Domain A server  310  that relates to stamp collection, because the historical relevance data classifies the target domain address and the search key words as being aligned with collecting stamps. 
     In another scenario, the domain classification database  308  may include historical relevance data collected prior to the request. The domain classifier  306  can access third-party a web site, such as www.relevad.com, and submit keywords from the target domain address. In the case of www.relevad.com, the domain classifier  306  can obtain a weighted list of keywords that are related to the one or more keywords in the requested domain address, one or more of which can be used to classify the requested domain address and generate a redirection domain address. It should be understood that such a look-up using a third-party information source may occur a priori to the request or in real-time with the request, depending on the speed of the look-up process. As such, using such an information source, the domain classifier  306  can redirect the browser to a different webpage at Domain B Server  312 , which covers U.S. Postal Service stamp prices. 
     In yet another scenario, the domain classifier  306  may further collect source context information from the user&#39;s session data available from cookies on the user&#39;s computer. For example, the user may have previously visited the target domain, only to now find it no longer available. Nevertheless, the domain classifier  306  may now access session data in a cookie associated with the target domain. The session data may provide information useful to the domain classifier in determining the interest of the user. For example, in addition to the stamp price association made in the previous paragraph, the domain classifier  306  may determine from the session data that the user&#39;s previous visits to www.postage.com domain resulted in online orders of postage products. Accordingly, the domain classifier  306  can then redirect the browser to a different webpage at Domain C Server  313 , which handles postage supply orders online. 
       FIG. 4  illustrates an exemplary domain classifier configuration  400  providing access to Web content based on request a originating from an address typed by a user into an address field. In one implementation, a user types a URL into an address field  402  of a browser&#39;s address bar  404 . A resulting request, which includes the domain address of the target website, is transmitted to a domain classifier  406  (e.g., assume the domain address has been transferred from the original target website to the domain classifier&#39;s server). The domain classifier  406  evaluates the domain address in the request (e.g., www.postage.com) against historical relevance data in a domain classification database  408 . The domain address can be evaluated using the historical reference data to allow the domain classifier  406  to identify a new website (e.g., one of Domain A server  410 , Domain B server  412 , and Domain C server  414 ) to which the browser should be redirected. 
     For example, the URL  402  includes a domain address—“www.postage.com”. If the target website (“www.postage.com/prices.htm”) is not available at the domain address (and so the browser is redirected to the domain classifier) or if the domain address has been transferred to the domain classifier  406 , then the domain classifier  406  may consult the historical relevance data to infer the user&#39;s intent. In one scenario, the domain classifier  406  may redirect the browser to a website at Domain A server  410  that relates to stamp collection, because the historical relevance data classifies the target domain address as being aligned with collecting stamps. 
     However, the domain classifier  406  may further collect target and/or source context factors for the current visit to further inform the domain classification. For example, the source domain address or the target domain address may be (or may have previously been) submitted to a misspellings database to generate variations of the domain name that can be used in the classification. For example, the user may have inadvertently entered www.potage.com. If the domain classifier  406  is configured to receive requests for www.potage.com, then the domain classifier  406  can have already classified a request to www.potage.com as being related to a “postage” keyword. Accordingly, the historical relevance data pertaining to www.potage.com can be associated with postage-related interest keywords. The domain classifier  406  may then redirect the browser to a webpage at Domain B Server  412 , which is related to postage services (or pass the one or more interest keywords on to an ad server, a content management server, etc.). 
     It should be understood that the misspellings database may also be used in real time, such that the domain classifier  406  collects misspelling variations of “potage” and recognizes “postage” as an interest keyword that it can work with. In one implementation, all variations can be evaluated against the historical relevance data, and the best-fit will be found based on all available information. 
       FIG. 5  illustrates exemplary operations  500  for relevancy-based domain classification. A characterization operation  502  characterizes a domain that has been designated for redirection or for other relevant provision of web content. Initially, such the characterization operation  502  associates the domain address of the target domain with one or more new content domains (e.g., the domains to which the domain classifier may redirect the user request). It should be understand that in an alternative implementation, rather than redirection, the domain classifier will cause relevant content to be served from an appropriate domain (e.g., from a third-party ad server or other content server). 
     If there is just one new content domain available for this domain address, then there not much need for domain classification. However, in the presence of two or more new content domains associated with the original target domain, each new content domain is associated with select historical relevance information, which is used by the domain classifier to identify the more relevant of the two new content domains from which to provide the new content. 
     For example, a domain classifier is configured to determine whether to send request to a certain target domain (www.postage.com) to one of two new content domains (www.USPS.gov or www.RoyalMail.com). As such, the domain classification database may be populated with relevance records indicating that:
         (1) if the target domain is www.postage.com and if a source context factor derived from the IP address of the request identifies the United States as the location of the client system issuing the request, then the request should be redirected to www.USPS.gov;   (2) if the target domain is www.postage.com and if a source context factor derived from the IP address of the request identifies the United Kingdom as the location of the client system issuing the request, then the request should be redirected to www.RoyalMail.com; and   (3) if the target domain is www.postage.com and if a source context factor derived from the IP address of the request identifies some other geographical region as the location of the client system issuing the request, then the request should be redirected to www.USPS.gov.       

     As an alternative to the conditional, Boolean tests exemplified above, a statistical classification algorithm may be used to classify a specified domain address and collected source context factors and pertaining to a particular new content domain. Generally, statistical classification places individual items (e.g., target domain address) into classifications or groups (e.g., a class associated with a select new content domain) based on quantitative information (e.g., source context factors) on one or more characteristics (e.g., geographical region, textual features of the previously visited webpage) inherent in the items. The placement is based on a training set of previously classified items (e.g., historical relevance data mapping specific target domain addresses and combinations of historical search context factors to specific new content domains). 
     Formally, the statistical classification problem for domain classification can be stated as follows: given historical relevance data (x 1 , y 1 ), . . . , (x n , y n ), produce a domain classifier h:x→y that maps an object xεX (e.g., a combination of a domain address and potentially source context search factors) to its classification label yεY (e.g., a new content domain address). There are many types of statistical classification that can be applied to the problem of domain classification, although they are generally solving one of three related mathematical problems. 
     The first problem is to find a map of a feature space (which is typically a multi-dimensional vector space) to a set of labels. This is equivalent to partitioning the feature space into regions, then assigning a label to each region. Such algorithms (e.g., the nearest neighbor algorithm) typically do not yield confidence or class probabilities, unless post-processing is applied. Another set of algorithms to solve this problem first apply unsupervised clustering to the feature space, then attempt to label each of the clusters or regions. 
     The second problem is to consider classification as an estimation problem, where the goal is to estimate a function of the form P(class|{right arrow over (x)})=ƒ(|{right arrow over (x)};{right arrow over (θ)}), where the feature vector input is {right arrow over (x)}, and the function ƒ is typically parameterized by some parameters {right arrow over (θ)}. In the Bayesian approach to this problem, instead of choosing a single parameter vector {right arrow over (θ)}, the result is integrated over all possible θs, with the θs weighted by how likely each θ is, given the training data D: 
         P (class|{right arrow over ( x )})=∫ƒ({right arrow over ( x )},{right arrow over (θ)}) P ({right arrow over (θ)}| D ) d {right arrow over (θ)}
 
     The third problem is related to the second, but the problem is to estimate the class-conditional probabilities P({right arrow over (x)}|class) and then use Bayes&#39; Rule to produce the class probability as in the second problem. Examples of classification algorithms include: linear classifiers, such as Fisher&#39;s linear discriminant, logistic regression, naive Bayes classifier, and perceptron; quadratic classifiers, k-nearest neighbor, boosting, decision trees, neural networks, Bayesian networks, support vector machines, and hidden Markov models. 
     A receiving operation  504  receives a user&#39;s request for content from a target domain, specified by a domain address included with the request. A collection operation  506  collects source context factor information associated with the source of the request, although in some scenarios, such source context factor information is not required to perform an appropriate domain classification. When source context factor information is collected (e.g., such as from one or more of the information sources previously discussed), a classification operation  508  evaluates the domain address and the source context factors against the historical relevance data to identify a new content domain from which to serve content to the user. The collected source context factor information may also be fed back into the historical relevance data for use with subsequent requests for the target domain (see dashed arrow  509 ). In this manner, the historical relevance data can be updated on the basis of other subsequent redirections and the apparent user reaction to the redirection (see collection operation  512 ). 
     A transmission operation  510  causes the transmission of content from the identified new content domain. In one implementation, the domain classifier causes said transmission by redirecting the user&#39;s browser to the new content domain, whereby the user&#39;s redirected request results in a response from the new content domain. In another implementation, the domain classifier performs similar processing functions as it would for a redirection implementation. However, instead of selecting a redirected URL location to send the user, the domain classifier specifies one or more appropriate interest keywords, which can be submitted to a Web content provider (e.g., an ad server or content management server) to provide the user with content (e.g., a banner ad from a new content domain) associated with the interest keywords. In yet another implementation, the domain classifier selects a Web content object (e.g., a relevant news article from a new content domain) identified in the domain classification as pertaining to a perceived interest. For example, the Web content object may include an associated redirection URL that is not spawned automatically on deliver to the user&#39;s browser. Content from the new content domain is received by the user&#39;s browser as a result of the transmission operation  510 . 
     A collection operation  512  collects clickstream data from the user&#39;s post-visit behavior at the new content domain. In one implementation, the collection operation  512  evaluates the clickstream data to determine whether the identified new content domain was truly of interest to the user (e.g., user lingers on site, user navigates some level into the site, etc.). This determination can be used to update the historical relevance data (see dashed arrow  514 ). 
       FIG. 6  illustrates an exemplary system useful in implementations of the described technology. A general purpose computer system  600  is capable of executing a computer program product to execute a computer process. Data and program files may be input to the computer system  600 , which reads the files and executes the programs therein. Some of the elements of a general purpose computer system  600  are shown in  FIG. 6  wherein a processor  602  is shown having an input/output (I/O) section  604 , a Central Processing Unit (CPU)  606 , and a memory section  608 . There may be one or more processors  602 , such that the processor  602  of the computer system  600  comprises a single central-processing unit  606 , or a plurality of processing units, commonly referred to as a parallel processing environment. The computer system  600  may be a conventional computer, a distributed computer, or any other type of computer. The described technology is optionally implemented in software devices loaded in memory  608 , stored on a configured DVD/CD-ROM  610  or storage unit  612 , and/or communicated via a wired or wireless network link  614  on a carrier signal, thereby transforming the computer system  600  in  FIG. 6  to a special purpose machine for implementing the described operations. 
     The I/O section  604  is connected to one or more user-interface devices (e.g., a keyboard  616  and a display unit  618 ), a disk storage unit  612 , and a disk drive unit  620 . Generally, in contemporary systems, the disk drive unit  620  is a DVD/CD-ROM drive unit capable of reading the DVD/CD-ROM medium  610 , which typically contains programs and data  622 . Computer program products containing mechanisms to effectuate the systems and methods in accordance with the described technology may reside in the memory section  604 , on a disk storage unit  612 , or on the DVD/CD-ROM medium  610  of such a system  600 . Alternatively, a disk drive unit  620  may be replaced or supplemented by a floppy drive unit, a tape drive unit, or other storage medium drive unit. The network adapter  624  is capable of connecting the computer system to a network via the network link  614 , through which the computer system can receive instructions and data embodied in a carrier wave. Examples of such systems include personal computers offered by Dell Corporation and by other manufacturers of Intel-compatible personal computers, PowerPC-based computing systems, ARM-based computing systems and other systems running a UNIX-based or other operating system. It should be understood that computing systems may also embody devices such as Personal Digital Assistants (PDAs), mobile phones, gaming consoles, set top boxes, etc. 
     When used in a LAN-networking environment, the computer system  600  is connected (by wired connection or wirelessly) to a local network through the network interface or adapter  624 , which is one type of communications device. When used in a WAN-networking environment, the computer system  600  typically includes a modem, a network adapter, or any other type of communications device for establishing communications over the wide area network. In a networked environment, program modules depicted relative to the computer system  600  or portions thereof, may be stored in a remote memory storage device. It is appreciated that the network connections shown are exemplary and other means of and communications devices for establishing a communications link between the computers may be used. 
     In an exemplary implementation, the domain classifier, collection modules, identification modules, characterization modules, and other modules may be incorporated as part of the operating system, application programs, or other program modules. Historical relevance data, domain addresses, source context factors, web content, and other data may be stored as program data. 
     The technology described herein is implemented as logical operations and/or modules in one or more systems. The logical operations may be implemented as a sequence of processor-implemented steps executing in one or more computer systems and as interconnected machine or circuit modules within one or more computer systems. Likewise, the descriptions of various component modules may be provided in terms of operations executed or effected by the modules. The resulting implementation is a matter of choice, dependent on the performance requirements of the underlying system implementing the described technology. Accordingly, the logical operations making up the embodiments of the technology described herein are referred to variously as operations, steps, objects, or modules. Furthermore, it should be understood that logical operations may be performed in any order, unless explicitly claimed otherwise or a specific order is inherently necessitated by the claim language. 
     The above specification, examples and data provide a complete description of the structure and use of example embodiments of the invention. Although various embodiments of the invention have been described above with a certain degree of particularity, or with reference to one or more individual embodiments, those skilled in the art could make numerous alterations to the disclosed embodiments without departing from the spirit or scope of this invention. In particular, it should be understood that the described technology may be employed independent of a personal computer. Other embodiments are therefore contemplated. It is intended that all matter contained in the above description and shown in the accompanying drawings shall be interpreted as illustrative only of particular embodiments and not limiting. Changes in detail or structure may be made without departing from the basic elements of the invention as defined in the following claims. 
     Although the subject matter has been described in language specific to structural features and/or methodological arts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts descried above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claimed subject matter.