Abstract:
A correlator extracts a plurality of related queries corresponding to each one of a plurality of answers. A classifier matches each one of the related queries corresponding to the answer with a category. A statistical tool determines the relevance of each category relative to the categories matched to the related queries corresponding to the answers.

Description:
BACKGROUND OF THE INVENTION 
     The internet is often used to obtain information regarding news, businesses, events, movies, etc. in a specific geographic area. A user interface is typically stored on a server computer system and transmitted over the internet to a client computer system. The user interface typically has a search box for entering a text search query. A user can then select a search button to transmit a search request from the client computer system to the server computer system. The server computer system then compares the text with data in a database or data source and extracts information based on the text from the database or data source. The information includes uniform resource locators (URLs) or other answers pertaining to the text search query. The information is then transmitted from the server computer system to the client computer system for display at the client computer system. 
     SUMMARY OF THE INVENTION 
     The invention provides a computer system, including a memory, a plurality of answers stored in the memory, and a classifier for matching each one of the answers with a category among a plurality of categories. 
     The computer system may further include a correlator utilizing the answer to extract at least one data query corresponding to the answer, the classifier matching the data query with the category. 
     The correlator may match the answer to the category according to the method including extracting a related query corresponding to the data query, and matching the related query to the category. 
     The correlator may match the answer to the category according to the method including extracting a plurality of related queries corresponding to the data query, and the categorizer matching each related query to a category, further including a statistical tool determining the relevance of each category. 
     The answers may be received over a network before storing the answers. 
     The answers for more frequently used categories may be updated more often than answers for categories used less often. 
     One of the categories may be a spam category, and answers in the spam category may not be downloaded. 
     The computer system may further include an indexer indexing the answers received over the network into indexed answers, the indexed answers being stored in the memory. 
     The indexer may index the answers into the categories. 
     The computer system may further include a search engine receiving a request from a client computer system at a server computer system and, in response to the request, transmitting a view from the server computer system to the client computer system for display at the client computer system, contents of the view being at least partially based on one selected category of the categories. 
     The request may be a search request, the classifier matching the request with a category among the plurality of categories, and associating at least one of a plurality of answers with the request due to association of the request and the answer with the select category. 
     The view may include different category areas, answers belonging to different categories being located in the respective category areas. 
     The category may be used to select the answer based on a media type of the answer. 
     The category may be used to select the answer based on a freshness of the answer. 
     The computer system may further include a correlator extracting a related query corresponding to the request, wherein the classifier matches the request with a category by matching the related query to the category. 
     The correlator may match the request to the category according to the method including extracting a plurality of related queries corresponding to the request, the categorizer matching each related query to a category, further including a statistical tool determining the relevance of each category. 
     The search engine may transmit a first view from a server computer system to the client computer system, the first view including a search identifier, the search engine receiving a search request from a client computer system at the server computer system and utilizing the search request at the server computer system to extract at least one search result from the answers, and transmitting at least part of a second view from the server computer system to the client computer system for display at the client computer system, wherein the second view includes the search result. 
     An advertisement may be selected among a plurality of advertisements based on the select category. 
     The request may be a browsing request based on the selected category selected at the client computer system among at least a subset of the categories. 
     The invention also provides a computer method, including storing a plurality of answers in memory of a computer, and matching each one of a plurality of the answers with a category among a plurality of categories. 
     In the method, each answer may be matched with a category according to the method including utilizing the answer to extract at least one data query corresponding to the answer, and matching the data query with the category. 
     The method of matching the answer to the category may further include extracting a related query corresponding to the data query, and matching the related query to the category. 
     The invention also provides a computer-readable medium having stored thereon a set of instructions which, when executed by a processor of a computer, executes the method including storing a plurality of answers in memory of a computer, and matching each one of a plurality of the answers with a category among a plurality of categories. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The invention is further described by way of example with reference to the accompanying drawings, wherein: 
         FIG. 1  is a block diagram of a network environment in which a user interface according to an embodiment of the invention may find application; 
         FIG. 2  is a flowchart illustrating how the network environment is used to search and find information; 
         FIG. 3  is a block diagram of a client computer system forming an area of the network environment, but may also be a block diagram of a computer in a server computer system forming area of the network environment; 
         FIG. 4  is a block diagram illustrating a Query-to-Pick and Pick-to-Query correlation; 
         FIG. 5  is a block diagram illustrating a classifier learning phase; 
         FIG. 6  is a block diagram illustrating a Query-to-Query correlation; 
         FIG. 7  is a block diagram illustrating a URL (Answer) classification; 
         FIG. 8  is a block diagram illustrating a query classification; 
         FIG. 9  is a screenshot showing a view wherein a URL and query classification have been used to return results; 
         FIG. 9A  illustrates that answers for different categories are located at different physical areas on a results page; 
         FIG. 10  is a block diagram illustrating integration of the subsystems of  FIGS. 7 and 8 ; 
         FIG. 11  is a block diagram illustrating how a profile of a client computer system can be used to further improve search results; 
         FIG. 12  is a diagram illustrating how an advertising engine utilizes categories to select an advertisement or advertisements; 
         FIG. 13  is a block diagram illustrating how a crawler can make use of categories to download certain answers at a higher rate, and can make use of the categories to store the answers in different categories or partitions; 
         FIG. 14  is a block diagram illustrating how a profile is created for each category; 
         FIG. 15A  illustrates how a page is ranked in different categories using traditional ranking as herein described; and 
         FIG. 15B  illustrates how a page is ranked higher in some categories than in others, utilizing a modified ranking system as herein described. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
       FIG. 1  of the accompanying drawings illustrates a network environment  10  that includes a user interface  12 , according to an embodiment of the invention, including the internet  14 A,  14 B, and  14 C, a server computer system  16 , a plurality of client computer systems  18 , and a plurality of remote sites  20 . 
     The server computer system  16  has stored thereon a crawler  19 , a collected data store  21 , an indexer  22 , a plurality of search databases  24 , a plurality of structured databases and data sources  26 , a search engine  28 , and the user interface  12 . The novelty of the present invention revolves around the user interface  12 , the search engine  28 , and one or more of the structured databases and data sources  26 . The crawler  19  is connected over the internet  14 A to the remote sites  20 . The collected data store  21  is connected to the crawler  19 , and the indexer  22  is connected to the collected data store  21 . The search databases  24  are connected to the indexer  22 . The search engine  28  is connected to the search databases  24  and the structured databases and data sources  26 . The client computer systems  18  are located at respective client sites and are connected over the internet  14 B and the user interface  12  to the search engine  28 . 
     Reference is now made to  FIGS. 1 and 2  in combination to describe the functioning of the network environment  10 . The crawler  19  periodically accesses the remote sites  20  over the internet  14 A (step  30 ). The crawler  19  collects data from the remote sites  20  and stores the data in the collected data store  21  (step  32 ). The indexer  22  indexes the data in the collected data store  21  and stores the indexed data in the search databases  24  (step  34 ). The search databases  24  may, for example, be a “Web” database, a “News” database, a “Blogs &amp; Feeds” database, an “Images” database, etc. The structured databases and data sources  26  are licensed from third-party providers and may, for example, include an encyclopedia, a dictionary, maps, a movies database, etc. 
     A user at one of the client computer systems  18  accesses the user interface  12  over the internet  14 B (step  36 ). The user can enter a search query in a search box in the user interface  12 , and either hit “Enter” on a keyboard or select a “Search” button or a “Go” button of the user interface  12  (step  38 ). The search engine  28  then uses the “Search” query to parse the search databases  24  or the structured databases and data sources  26 . In the example of where a “Web” search is conducted, the search engine  28  parses the search database  24  having general Internet Web data (step  40 ). Various technologies exist for comparing or using a search query to extract data from databases, as will be understood by a person skilled in the art. 
     The search engine  28  then transmits the extracted data over the internet  14 B to the client computer system  18  (step  42 ). The extracted data typically includes URL links to one or more of the remote sites  20 . The user at the client computer system  18  can select one of the links to the remote sites  20  and access the respective remote site  20  over the internet  14 C (step  44 ). The server computer system  16  has thus assisted the user at the respective client computer system  18  to find or select one of the remote sites  20  that have data pertaining to the query entered by the user. 
       FIG. 3  shows a diagrammatic representation of a machine in the exemplary form of one of the client computer systems  18  within which a set of instructions, for causing the machine to perform any one or more of the methodologies discussed herein, may be executed. In alternative embodiments, the machine operates as a standalone device or may be connected (e.g., networked) to other machines. In a network deployment, the machine may operate in the capacity of a server or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine may be a personal computer (PC), a tablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), a cellular telephone, a web appliance, a network router, switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein. The server computer system  16  of  FIG. 1  may also include one or more machines as shown in  FIG. 3 . 
     The exemplary client computer system  18  includes a processor  130  (e.g., a central processing unit (CPU), a graphics processing unit (GPU), or both), a main memory  132  (e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM) or Rambus DRAM (RDRAM), etc.), and a static memory  134  (e.g., flash memory, static random access memory (SRAM), etc.), which communicate with each other via a bus  136 . 
     The client computer system  18  may further include a video display  138  (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)). The client computer system  18  also includes an alpha-numeric input device  140  (e.g., a keyboard), a cursor control device  142  (e.g., a mouse), a disk drive unit  144 , a signal generation device  146  (e.g., a speaker), and a network interface device  148 . 
     The disk drive unit  144  includes a machine-readable medium  150  on which is stored one or more sets of instructions  152  (e.g., software) embodying any one or more of the methodologies or functions described herein. The software may also reside, completely or at least partially, within the main memory  132  and/or within the processor  130  during execution thereof by the client computer system  18 , the memory  132  and the processor  130  also constituting machine readable media. The software may further be transmitted or received over a network  154  via the network interface device  148 . 
     While the instructions  152  are shown in an exemplary embodiment to be on a single medium, the term “machine-readable medium” should be taken to understand a single medium or multiple media (e.g., a centralized or distributed database or data source and/or associated caches and servers) that store the one or more sets of instructions. The term “machine-readable medium” shall also be taken to include any medium that is capable of storing, encoding, or carrying a set of instructions for execution by the machine and that caused the machine to perform any one or more of the methodologies of the present invention. The term “machine-readable medium” shall accordingly be taken to include, but not be limited to, solid-state memories and optical and magnetic media. 
     Query-to-Pick and Pick-to-Query Correlation 
       FIG. 4  illustrates how queries and picks are associated with one another with the use of a correlator  160  connected to the search engine  28 . A query-to-pick (Q 2 P) correlation associates a query with a pick. When multiple independent users make the same association, that is a correlation candidate. When the search engine  28  returns a result in response to a query and a user picks that result, this is a special case of such a correlation (Q 2 RP). In effect, the search engine algorithm replaces a second independent user. In accordance with one embodiment of the invention, the Q 2 P correlation associates a query with all picks in a user session. This is in contrast to prior art schemes that terminated association of a given query with picks upon issuance of a subsequent query. 
     With Q 2 P, all picks recorded during a user session are associated with a given query issued during that user session. For one embodiment, a score is assigned to each association, based upon various factors, including the time between query and pick, the number of intervening queries and/or picks, and the order of queries with respect to picks. 
     In addition, each association&#39;s score can be adjusted based upon well-known factors, including rank of the pick in the result list at the time of association, duration of the pick (interval until next known user action), age or order of the association (relative to older or newer associations), and age of the first known instance of association. 
     Each user session can be of infinite duration. In a practical application, a reasonable time limit, or limit on intervening actions, should be imposed beyond which no relationship between picks and queries will be assigned. Alternatively or additionally, an interruption of sufficient duration can indicate a break in sessions. A search log excerpt, in accordance with one embodiment of the invention, is shown below as Table 1. In various alternative embodiments, any other items could be captured in the search log, but are excluded here for clarity: 
     
       
         
               
               
               
               
               
             
           
               
                 TABLE 1 
               
               
                   
               
               
                 Row 
                 Timestamp 
                 User ID 
                 Query 
                 Pick (URL) 
               
               
                   
               
             
             
               
                 101 
                 1/1/03 00:00:00 
                 U1 
                 Q1 
                 P5 
               
               
                 102 
                 1/1/03 00:01:00 
                   
                 Q2 
                 P1 
               
               
                 103 
                 1/1/03 00:02:00 
                   
                   
                 P2 
               
               
                 104 
                 1/1/03 00:02:05 
                   
                   
                 P3 
               
               
                 201 
                 1/2/03 00:00:00 
                 U2 
                 Q2 
                 P4 
               
               
                 202 
                 1/2/03 00:01:00 
                   
                   
                 P1 
               
               
                 203 
                 1/2/03 00:02:00 
                   
                   
                 P4 
               
               
                 204 
                 1/2/03 00:04:00 
                   
                 Q1 
                 P2 
               
               
                 205 
                 1/2/03 00:04:05 
                   
                   
                 P3 
               
               
                 301 
                 1/3/03 00:00:00 
                 U3 
                 Q3 
                 P3 
               
               
                 302 
                 1/3/03 00:04:00 
                   
                 Q2 
                 P1 
               
               
                 303 
                 1/3/03 02:00:00 
                   
                 Q3 
                 P5 
               
               
                 401 
                 1/4/03 00:00:00 
                 U2 
                 Q1 
                 * 
               
               
                 402 
                 1/4/03 00:06:00 
                   
                 Q2 
                 P4 
               
               
                   
               
               
                 (*= query with no associated pick) 
               
             
          
         
       
     
     Table 1A illustrates a tabulation of the click information contained in Table 1 in accordance with an embodiment of the invention. For comparison, Table 1B illustrates a tabulation of the click information contained in Table 1 in accordance with a typical prior art scheme employing a Q 2 RP correlation: 
     
       
         
               
             
               
               
               
               
             
               
               
               
               
               
             
           
               
                 TABLE 1A 
               
             
             
               
                   
               
               
                 (Q2P Results) 
               
             
          
           
               
                   
                 Q1 
                 Q2 
                 Q3 
               
               
                   
                   
               
             
          
           
               
                   
                 P1 
                 2 
                 3 
                 1 
               
               
                   
                 P2 
                 1 
                 1 
                 — 
               
               
                   
                 P3 
                 2 
                 3 
                 1 
               
               
                   
                 P4 
                 1 
                 1 
                 — 
               
               
                   
                 P5 
                 1 
                 2 
                 1 
               
               
                   
                   
               
             
          
         
       
     
     
       
         
               
             
               
               
               
               
             
               
               
               
               
               
             
           
               
                 TABLE 1B 
               
             
             
               
                   
               
               
                 (Q2RP Results of Prior Art) 
               
             
          
           
               
                   
                 Q1 
                 Q2 
                 Q3 
               
               
                   
                   
               
             
          
           
               
                   
                 P1 
                 — 
                 3 
                 — 
               
               
                   
                 P2 
                 1 
                 — 
                 — 
               
               
                   
                 P3 
                 1 
                 1 
                 1 
               
               
                   
                 P4 
                 — 
                 2 
                 — 
               
               
                   
                 P5 
                 1 
                 — 
                 1 
               
               
                   
                   
               
             
          
         
       
     
     Due to the fact that numerous factors can vary or penalize the scores, we will assume 1 pick=a score increment of +1, except for the following penalization situations, where we will assume the pick represents a score increment of 0. Assuming a time threshold, the click in row  103  is penalized in both tabulations due to the user spending a very short time at the URL. Assuming daily database batch updates, the click in row  203  would typically be penalized by the prior art tabulation of Table 1B as a duplicate of click  201 . The clicks in rows  203  and  402  are penalized by the tabulation, in accordance with an embodiment of the invention, as duplicates of click  201 . 
     For Query Q 1 , URL P 1 , which was never clicked immediately subsequent to Q 1 , has garnered a high score in the tabulation, in accordance with an embodiment of the invention, because multiple users chose it before or after—though not immediately after—issuing Query Q 1 . The whole matrix of scores for the tabulation, in accordance with an embodiment of the invention, is richer, as many more associations are noted. Some scores, such as that for Q 2 P 4 , are lower, due to the retention of session data indicating that all the clicks came from a single user, permitting the identification of more duplicates. 
     In practical applications of Q 2 P, we can retain the distinction as to whether a particular association was Q 2 RP or non-Q 2 RP. A single, uncorrelated non-Q 2 RP click (such as Q 3 P 1  in the table) may not inspire enough confidence to release the result to users, whereas for a single, uncorrelated Q 2 RP click, the association is reinforced by the fact that the search engine presented the result for the original search. 
     A pick-to-query (P 2 Q) correlation associates all queries recorded during a user session that are correlated with a given pick issued during that user session. The search log excerpt of Table 1 illustrates the output of P 2 Q correlation. That is, the same data generated for Q 2 P can be re-indexed for P 2 Q. 
     Further details of Q 2 P and P 2 Q are described in U.S. Pat. No. 7,181,447, which is incorporated herein by reference in its entirety. 
     Classifier Learning Phase 
       FIG. 5  shows the learning phase of a classifier  162 . For each category, editors collect a large number of related documents  164  and store the documents in a repository  166 . The classifier  162  reads the related documents  164  and learns to recognize their important features. Important features may, for example, be identified when a particular word appears in a large percentage of the documents  164  for a particular category. 
     Query-to-Query Correlation 
       FIG. 6  illustrates how queries and queries are associated with one another with the use of the same correlator  160  connected to the search engine  28 . A query-to-query (Q 2 Q) correlation associates all queries issued during a user session with all other queries issued during that session. For one embodiment, a score may be assigned to each association based upon various factors, including the time between queries, the number of intervening queries and/or picks, age or order of the association (relative to older or newer associations), whether or not the query results generated picks, and the pair-wise order of the associated queries, among others. 
     Determining if the query results generated picks, as well as the pair-wise order of the associated queries, can be particularly informative, as they can indicate whether one query is a “correction” of another. For any practical application, it is useful to know which of two associated queries is an error, and which is a correction. 
     A search log excerpt, in accordance with one embodiment of the invention, is shown below as Table 2. Only the query portion of the search log is required to create a Q 2 Q table: 
     
       
         
               
               
               
               
             
           
               
                 TABLE 2 
               
               
                   
               
               
                 Row 
                 Timestamp 
                 User ID 
                 Query 
               
               
                   
               
             
             
               
                 101 
                 1/1/03 00:00:00 
                 U1 
                 Q1 
               
               
                 102 
                 1/1/03 00:01:00 
                   
                 Q2 
               
               
                 103 
                 1/1/03 00:02:00 
               
               
                 104 
                 1/1/03 00:02:05 
               
               
                 201 
                 1/2/03 00:00:00 
                 U2 
                 Q2 
               
               
                 202 
                 1/2/03 00:01:00 
               
               
                 203 
                 1/2/03 00:02:00 
               
               
                 204 
                 1/2/03 00:04:00 
                   
                 Q1 
               
               
                 205 
                 1/2/03 00:04:05 
               
               
                 301 
                 1/3/03 00:00:00 
                 U3 
                 Q3 
               
               
                 302 
                 1/3/03 00:04:00 
                   
                 Q2 
               
               
                 303 
                 1/3/03 02:00:00 
                   
                 Q3 
               
               
                 401 
                 1/4/03 00:00:00 
                 U2 
                 Q1 
               
               
                 402 
                 1/4/03 00:06:00 
                   
                 Q2 
               
               
                   
               
             
          
         
       
     
     Table 2A illustrates a tabulation of the click information contained in Table 2 in accordance with an embodiment of the invention (assuming the order of queries issued is ignored): 
     
       
         
               
             
               
               
               
               
             
               
               
               
               
               
             
           
               
                 TABLE 2A 
               
             
             
               
                   
               
               
                 (Q2Q Results) 
               
             
          
           
               
                   
                 Q1 
                 Q2 
                 Q3 
               
               
                   
                   
               
             
          
           
               
                   
                 Q1 
                 — 
                 2 
                 — 
               
               
                   
                 Q2 
                 — 
                 — 
                 1 
               
               
                   
                 Q3 
                 — 
                 — 
                 — 
               
               
                   
                   
               
             
          
         
       
     
     The lower triangular area of Table 2A can be used to retain the pair-wise query order information, avoiding double-booking cases like rows  301 - 303 . 
     As noted above, a scoring scheme may be employed in which numerous factors can vary or penalize the score. For example, duplicates (e.g., association in rows  101  and  102  and associations made in rows  401  and  402 ) could be penalized. Or, for example, an uncorrelated Q 2 Q association, like Q 2 Q 3 , would not inspire enough confidence to release the result to users. 
     URL (Answer) Categorization 
       FIG. 7  illustrates categorization of a plurality of answers in the form of URLs of the search database  24  in  FIG. 1  stored in the memory  132  in  FIG. 3 . 
     The same correlator  160  used in  FIG. 4  utilizes each URL to extract a plurality of data queries using P 2 Q as described with reference to  FIG. 4  corresponding to the URL. The same correlator  160  then utilizes each data query to extract a plurality of related queries corresponding to the respective data query using Q 2 Q as described with reference to  FIG. 6 . 
     The classifier  162  then matches the data query and each one of the related queries with a respective category utilizing the features identified for each category. A statistical tool  164  is then used to extract the most likely category among all the categories utilizing interpolation of the categories. The classifier thus matches each one of the URLs with a category among a plurality of categories. 
     Table 3 illustrates a P 2 Q correlation and classification for the URL http://www.apple.com/itunes/: 
     
       
         
               
               
             
           
               
                 TABLE 3 
               
               
                   
               
               
                 Query 
                 Classification 
               
               
                   
               
             
             
               
                 ipod nano 
                 Consumer_Electronics # 86% &gt;&gt; MP3_Players # 89% 
               
               
                 itunes 
                 Consumer_Electronics # 0% &gt;&gt; MP3_Players # 0% 
               
               
                 itunes music 
                 Consumer_Electronics # 53% &gt;&gt; MP3_Players # 62% 
               
               
                 store 
               
               
                 apple itunes 
                 Consumer_Electronics # 68% &gt;&gt; MP3_Players # 93% 
               
               
                 download 
                 Consumer_Electronics # 20% &gt;&gt; MP3_Players # 18% 
               
               
                 itunes 
               
               
                 itunes help 
                 Consumer_Electronics # 19% &gt;&gt; MP3_Players # 66% 
               
               
                 apple itunes 
                 Consumer_Electronics # 62% &gt;&gt; MP3_Players # 90% 
               
               
                 burn cds off 
                 Computers # 14% &gt;&gt; Software # 9% 
               
               
                 internet for 
               
               
                 free 
               
               
                 free 
                 Computers # 22% &gt;&gt; Software # 8% 
               
               
                 downloads 
               
               
                 itunes 
               
               
                 what is itunes 
                 Consumer_Electronics # 0% &gt;&gt; MP3_Players # 0% 
               
               
                 itunes store 
                 Consumer_Electronics # 46% &gt;&gt; MP3_Players # 49% 
               
               
                 what are itunes 
                 Consumer_Electronics # 0% &gt;&gt; MP3_Players # 0% 
               
               
                   
               
             
          
         
       
     
     The statistical tool  164  has thus classified each one of the correlated queries according to a degree of confidence. 
     The statistical tool  164  then proceeds to determine the most relevant category or categories among the categories in Table 3. In the present example, the most relevant categories are as follows: 
     Level 1: Consumer_Electronics (3.54), Computers (0.36); 
     Level 2: Consumer_Electronics/MP3_Players (2.67), Computers/Software (0.03). 
     Query Classification 
       FIG. 8  illustrates categorization of a plurality of a search request received at the client computer system  18 .  FIG. 4 ,  FIG. 5 ,  FIG. 6 , and  FIG. 7  are carried out ahead of time, and  FIG. 8  is carried out almost in real time when a search request is received. 
     The same correlator  160  utilizes the search request to extract a plurality of related queries using Q 2 Q as described with reference to  FIG. 7  corresponding to the search request. The classifier  160  then matches the search request and each one of the related queries with a respective category utilizing the features identified for each category. A statistical tool  170  is then used to extract the most likely category among all the categories utilizing a stochastic method. The classifier thus matches the search request with a category among a plurality of categories. 
     Search Results Based on Classification 
     Search results are generated as hereinbefore described with a reference to  FIG. 2 . The search results include a plurality of URLs. The most relevant category for the query is used to provide URLs that are primarily in the same category as the query. 
       FIG. 9  illustrates search results for the search request (“oscar”). In this example, a Q 2 Q categorization has determined that the search request is primarily for computers, entertainment, or education, and secondarily, perhaps, for health and sports. As such, URLs in the categories “computer,” “education,” and “entertainment” are primarily provided, followed by “health” and “sports.” A browser is used to display a user interface that includes the search results. The browser  160  may, for example, be an Internet Explorer™, Firefox™, Netscape™, or any other browser. The browser has an address box, a viewing pane  166 , and various buttons such as back and forward buttons. The browser is loaded on a computer at the client computer system  18  of  FIG. 1 . A user at the client computer system  18  can load the browser into memory, so that the browser is displayed on a screen such as the video display  138  in  FIG. 3 . 
     As better illustrated in  FIG. 9A , different categories are placed at different physical locations. Search results are also separately ranked within the physical area of each category. 
     In  FIG. 10 , the same correlator is indicated with reference numerals  160 A and  160 B, and the same classifier is indicated with reference numerals  162 A and  162 B. The correlator  160 A, classifier  162 A, and the statistical tool  164  form part of a categorizer  180  in a learning phase  182  of the system. The same categorizer  180  is used a plurality of times. The statistical tool  164  is a low-level statistical tool  164 . A high-level statistical tool  184  is used to combine the data from the multiple uses of the categorizer  180 . An output from the high-level statistical tool  184  is used to develop a categorized database  186 . 
     The correlator  160 B, classifier  162 B, and the statistical tool  170  form another categorizer  190  in a real-time phase  192  of the system. The real-time phase  192  also includes a look-up module  194  that retrieves categories from the categorized database  186  based on an output of the statistical tool  170 . 
     The categorizer  180  in the learning phase  182  is thus the same as in  FIG. 7 . The categorizer  190  in the real-time phase  192  is the same as in  FIG. 8 . 
     As shown in  FIG. 11 , a profile  200  for a client computer system  18  is established. The profile  200  is based on queries that are received from the client computer system  18  (Query  1 , Query  2  . . . Query N) and selections that are made (e.g., click on link  4 , category B). When the same client computer system  18  is used to submit a query on a search page  202 , and the query is categorized in the categorizer  190 , multiple factors are used to determine relevant pages for a results page  204 . The factors that are taken into account for the results page  204  include the query and category from the categorizer  190 , relevant pages and categories from the categorizer  180  received over the internet  14 A, and the profile  200  of the client computer system  18 . Certain pages may, for example, be ranked higher than other pages, than when the profile  200  is not used for ranking the pages. 
     As shown in  FIG. 12 , following entry of a query in a search page and categorization by the categorizer  190 , an advertising engine  206  utilizes the query and the category to extract an advertisement or advertisements from a plurality of advertisements. The advertisement or advertisements that are selected utilizing the category are different than advertisements that are selected without using the category. The same query and category are used to extract relevant pages from a search database  24 . The relevant pages and selected advertisements are provided together on the results page  204 . The results page  204  is then transmitted back to the client computer system  18 . 
     As further illustrated in  FIG. 13 , the crawler  19  can use a statistical tool  208  to determine which categories are searched the most by users. The crawler  19  utilizes the statistics provided by the statistical tool  208  to download web pages belonging to certain categories more frequently than web pages belonging to other categories. The categories that are downloaded more often are typically the ones that are searched more often by users. A spam category can also be created, and downloads of web pages belonging to the spam category can be avoided or be totally eliminated. 
     The crawler  19  can also store the downloaded pages in separate categories, even separate partitions (Part 1 , Part 2  . . . Part N). By storing the web pages in separate categories, retrieval speed can be increased. 
       FIG. 14  illustrates that a profile  216  is created for each category. In the present example, the profile  216  is a profile for sports queries. A separate profile (not shown) is created for each category. Queries are received from different client computer systems  18 , and are categorized as “sports queries,” as hereinbefore described. Results are also returned to the client computer systems  18 . Users at the client computer systems  18  then select answers from the results. The answers may be links to web pages, images, video, or other media types. The answers also differ in their freshness. The profile  216  that is built reflects the media types that are most frequently selected for the particular category “sports queries,” and also reflects the freshness of the answers that are more frequently selected. In the present example, the profile  216  for “sports queries” may reflect that users typically select fresher content, i.e., content within the last week as opposed to content that is more than ten years old. The profile  216  may also reflect that users select web pages approximately 40% of the time, images approximately 30% of the time, and videos approximately 30% of the time. When results are provided to future users of client computer systems such as the client computer systems  18 , web pages, images, and videos are provided to such client computer systems in the same ratio as reflected in the profile  216  for “sports queries.” 
     As shown in  FIG. 15A , traditional ranking of answers does not take into account the category of a web page or other answer.  FIG. 15B  illustrates that the scores of a page may differ, depending on the category. The same page A can, for example, belong to categories A, B, and C. When the page A is provided within category B, the page A would have a lower ranking than when the page A is provided in category C. 
     While certain exemplary embodiments have been described and shown in the accompanying drawings, it is to be understood that such embodiments are merely illustrative and not restrictive of the current invention, and that this invention is not restricted to the specific constructions and arrangements shown and described since modifications may occur to those ordinarily skilled in the art.