Abstract:
A computer-based system and method is described for converting non-standardized resumes and job listings into standardized profiles that can be easily searched, compared and referenced. Attributes are identified within the resumes and job listings, and are evaluated for various features. Each resume or job listing is broken down into its component parts and analyzed based on a logic-based routine to identify and package meaningful content.

Description:
CROSS-REFERENCE 
       [0001]    This application is a continuation of U.S. patent application Ser. No. 13/744,715, filed on Jan. 18, 2013 (to issue as U.S. Pat. No. 9,355,151 on May 31, 2016), which is a continuation of U.S. patent application Ser. No. 11/869,570, filed on Oct. 9, 2007 (issued as U.S. Pat. No. 8,375,026 on Feb. 12, 2013), which is a continuation-in-part of U.S. patent application Ser. No. 11/835,994, filed on Aug. 8, 2007 (issued as U.S. Pat. No. 8,103,679 on Jan. 24, 2012), which is a continuation-in-part of U.S. patent application Ser. No. 11/622,572 filed on Jan. 12, 2007 (now abandoned), which was a non-provisional patent application claiming priority to U.S. provisional application No. 60/759,242 filed on Jan. 13, 2006. These prior applications are incorporated herein in their entirety by reference. 
     
    
     BACKGROUND OF THE INVENTION 
       [0002]    This invention relates generally to a method and system for receiving a plurality of non-standardized data sets and generating respective standardized profiles  80  that can be used for efficiently comparing and matching the data sets. 
         [0003]    One application for the current invention is providing online recruiting services, and more specifically, for converting job seekers&#39; resumes on the one hand and job postings on the other hand into standardized profiles, which can be compared and matched to one another. Conventional online recruiting systems permit employers to create job posting for available positions and permit job seekers to post their resumes. Conventional online recruiting systems have also permitted job seekers to browse or conduct keywords searches through available job postings and submit their resumes for specific jobs. Conversely, these systems have also permitted employers to browse or conduct keyword searches through available candidate resumes. However, the task of browsing for candidate resumes or job postings is time consuming and can be a hit-or-miss proposition for both the job seeker and the employer. While conducting targeted keyword searches may reduce the total number of job postings or resumes, the only way to find the most suitable match is to review and evaluate each resume or job posting individually. 
       SUMMARY OF THE INVENTION 
       [0004]    A system and method is described for receiving a plurality of non-standardized data sets and generating respective standardized profiles that can be used for efficiently comparing and matching the data sets. One application of this invention is to convert job seekers&#39; resumes and job postings into respective standardized profiles and then ranking the standardized profiles according to their suitability for a particular job posting. Generally, the system includes a remote computer, which is connected to a server computer via a network system or the Internet and which is capable of exchanging files and information with the server computer. 
         [0005]    A better understanding of the objects, advantages, features, properties and relationships of the invention will be obtained from the following detailed description and accompanying drawings which set forth an illustrative embodiment and which are indicative of the various ways in which the principles of the invention may be employed. 
     
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         [0006]    For a better understanding of the invention, reference may be had to the following Appendices, which further describe a preferred embodiment of the present invention and which include drawings and exemplary screen shots therefore: 
           [0007]      FIG. 1  is a diagram depicting a computer network on which an embodiment of the invention may be operated. 
           [0008]      FIG. 2  is a sample graphical user interface of one screen employed by the present invention. 
           [0009]      FIG. 3  illustrates an exemplary data set in the form of a job posting. 
           [0010]      FIG. 4  illustrates an exemplary data set in the form of a candidate resume. 
           [0011]      FIG. 5A-5B  illustrates an illustrative band array generated from the data set shown in  FIG. 4 . 
           [0012]      FIG. 6  illustrates the steps for parsing a data set into bands. 
           [0013]      FIGS. 7A-7D  illustrate an illustrative word array generated from the data set shown in  FIG. 4 . 
           [0014]      FIG. 8  illustrates the steps for parsing the band array of  FIG. 4  into a word array shown in  FIGS. 7A-7D . 
           [0015]      FIG. 9  illustrates an excerpt of a substitute database, as used in the present invention. 
           [0016]      FIG. 10  illustrates the steps for evaluating words for entry into the attribute array. 
           [0017]      FIG. 11  depicts an excerpt from the common word database as used in the present invention. 
           [0018]      FIG. 12  illustrates an excerpt of the attribute dictionary, as used in the present invention. 
           [0019]      FIGS. 13A-13C  illustrate an exemplary attribute array generated from the data set shown in  FIG. 4  according to the present invention. 
           [0020]      FIG. 14  illustrates the steps for entering a word or phrase into the attribute array. 
           [0021]      FIG. 15  illustrates an excerpt from an exemplary pod, as used in the present invention. 
           [0022]      FIG. 16  illustrates the steps for calculating support values and ranking the attributes within the profile. 
           [0023]      FIG. 17  illustrates an exemplary profile generated from the data set shown in  FIG. 4  according to the present invention. 
           [0024]      FIG. 18  illustrates a recommendation engine, as used in the present invention. 
           [0025]      FIG. 19  illustrates the profile matching conducted by the recommendation engine shown in  FIG. 18 . 
           [0026]      FIGS. 20A-20E  illustrate an exemplary tagged job posting featuring an embodiment employing the pond, job level, and education level. 
           [0027]      FIG. 21  illustrates an exemplary tagged resume featuring an embodiment employing the pond, job level, and education level. 
           [0028]      FIG. 22A-22B  illustrate any exemplary recommendation page featuring dynamic pods corresponding to a profile. 
       
    
    
     DETAILED DESCRIPTION 
       [0029]    Turning now to the Figures, wherein like reference numerals refer to like elements, there is illustrated a system and method for receiving a plurality of non-standardized data sets and generating respective standardized profiles  80  that can be used for efficiently comparing and matching the data sets. The system permits users to use the standardized profiles  80  to compare and match various data sets. 
         [0030]    As will be described, each data set is processed to (A) parse the data set into bands  92 ; (B) identify attributes  70   a ,  70   b ,  70   c , etc., such as concepts  85  or titles  87  related to the data set; (C) identify the band  92  in which each attribute  70  is first found; (D) identify the number of occurrences  108  in which each attribute is associated with each data set; and (E) identify what support  140  is present in the rest of each data set for each attribute  70 . The results provided in an array  25   c  can then be weighted to create a profile  80 . For example, all of the attributes  70   a ,  70   b ,  70   c , etc. can be ranked depending on one or more metrics  90   a ,  90   b ,  90   c , etc., which are described herein. The metrics  90   a ,  90   b ,  90   c , etc. may include band  92 , occurrences  108 , support  140  or various combinations of all three metrics. 
       The System 
       [0031]    Although not required, the system and method will be described in the general context of a computer network  20 , as is well know in the industry, and computer executable instructions being executed by general purpose computing devices within the computer network  20 . Referring to  FIG. 1 , in this regard, the general purpose computing devices may comprise one or more server computers  22   a  hosting a data set software application. If there are multiple server computers  22   a , they may interface via a network or serial interface either directly or over the Internet or other local or wide area network. The server computer  22   a  can also include one or more databases for storing data sets. Data sets can include resume information, job-posting information, personal profile information, housing information, or any other data sets for which it would be advantageous to compare one data set against other data sets to select appropriate matches. In the context of recruiting services, data sets may include (1) detailed information about a prospective applicant, such as, previous job history, experience, education, and job-search criteria, or (2) information about an employer or possible job posting, such as, hiring criteria, educational and skill qualifications, location, and employee benefits. It should be appreciated that the network components could be described as having client and server relationships, as generally known in the art. 
         [0032]    To allow each user having a client computer  22   b  to access and utilize the data matching system, the software application will reside on the server computer(s)  22   a . Further, it is preferable that client users access the software application via an internet browser, which acts as an interface between the software application and the operating system for the server computer  22   a . The operating system for the server computer  22   a  and the client computer  22   b  may be Windows®-based or could employ any one of the currently existing operating systems, such as LINUX®, MAC OS®, Mozilla®, etc. In addition, it should be appreciated by those with skill in the art that other applications besides the browser may also be utilized to act as an interface between the software application and the server computers  22   a.    
         [0033]    For editing, populating and maintaining the databases, the browser includes a graphical user interface  50 . As shown in  FIG. 2 , the graphical user interface  50  is further comprised of various menu bars, drop-down menus, buttons and display windows. 
         [0034]    As will be appreciated by those of skill in the art, the computers  22   a ,  22   b  need not be limited to personal computers, but may include hand-held devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers, personal digital assistants, cellular telephones or the like depending upon their intended end use within the system. For performing the procedures described hereinafter, the computer executable instructions may be written as routines, programs, objects, components, and/or data structures that perform particular tasks. Within the computer network  20 , the computer executable instructions may reside on a single computer  22 , a server computer  22   a , a client computer  22   b , or the tasks performed by the computer executable instructions may be distributed among any combination of those computers  22 ,  22   a ,  22   b . Therefore, while described in the context of a computer network, it should also be understood that the present invention may be embodied in a stand-alone, general purpose computing device that need not be connected to a network. 
         [0035]    To efficiently provide users with access to the software application  30 , the server computers  22   a  and the underlying framework for the computer network  20  may be provided by the service company itself or by outsourcing the hosting to an application service provider (“ASP”). ASP&#39;s are companies that provide server computers that store and run a software application on behalf of a third party, which is accessible to that party&#39;s users via the Internet or similar means. Therefore, companies are able to provide a computer network without supplying the server computer(s)  22   a . In addition, users are able to access and use software applications without storing the software application on their computers. It should be understood, however, that ASP models are well-known in the industry and should not be viewed as a limitation with respect to the type of system architectures that are capable of providing a computer network  20  that can properly operate the software application discussed herein. Similarly, a provider of the system may also choose to host the system on its own equipment or employ a third-party hosting service to maintain the system. 
         [0036]    To perform the particular tasks in accordance with the computer executable instructions, the computers  22   a ,  22   b  may include, as needed, a video adapter, a processing unit, a system memory, and a system bus that couples the system memory to the processing unit. The video adapter allows the computers  22   a ,  22   b  to support a display, such as a cathode ray tube (“CRT”), a liquid crystal display (“LCD”), a flat screen monitor, a touch screen monitor or similar means for displaying textual and graphical data to a user. The display allows a user to view information, such as, code, file directories, error logs, execution logs and graphical user interface tools. 
         [0037]    The computers  22   a ,  22   b  may further include read only memory (ROM), a hard disk drive for reading from and writing to a hard disk, a magnetic disk drive for reading from and writing to a magnetic disk, and/or an optical disk drive for reading from and writing to a removable optical disk or any other suitable data storage device. The hard disk drive, magnetic disk drive, optical disk drive or other data storage device may be connected to the system bus by a hard disk drive interface, a magnetic disk drive interface, or an optical disk drive interface, respectively, or other suitable data interface. The drives and their associated computer-readable media provide a means of non-volatile storage for the computer executable instructions and any other data structures, program modules, databases, arrays, etc. utilized during the operation of the computers  22   a ,  22   b.    
         [0038]    To connect the computers  22   a ,  22   b  within the computer network  20 , the computers  22   a ,  22   b  may include a network interface or adapter. For example, used in a wide area network, such as the Internet, the computers  22   a ,  22   b  typically include a modem, router or similar device. The modem, which may be internal or external, may be connected to the system bus via a serial port interface. It will be appreciated that the described network connections are exemplary and that other means of establishing a communications link between the computers  22   a ,  22   b  may be used. For example, the system may also include a wireless access interface that receives and transmits information via a wireless communications medium, such as a cellular communications network, a satellite communications network, or another similar type of wireless network. It should also be appreciated that the network interface will be capable of employing TCP/IP, FTP, SFTP, Telnet SSH, HTTP, SHTTP, RSH, REXEC, etc. and other network connectivity protocols. 
         [0039]    As mentioned above, in one embodiment, the software application  30  and databases reside on the server computer(s)  22   a  and are managed by the provider of the software application  30  or by a third-party. Those with skill in the art will understand, however, that the software application and databases may reside on the remote client computer  22   b  and be managed and maintained by a user. The graphical user interface  50  may load web pages via HTTP or HTTPS or other suitable application protocol. 
         [0040]    For populating the databases, the browser may be utilized, but this may also be accomplished via an MS-SQL Server Enterprise Manager. While the software application  30  may be programmed in any software language capable of producing the desired functionality, it is envisioned that the software application will be programmed using Microsoft ASP.net, HTML, Javascript, PHP3, or MS-SQL Stored Procedures. 
         [0041]    For maintaining the security associated with the software application and databases, a unique login page may be maintained for each user including, for example, individuals and employers. The login page may also be used to control the access privileges for various levels of users. In addition, each login page may also require a user name and password. For security purposes, the user names and passwords may be kept separately for each company that is accessing the software application. To gain access to the software application, the user must enter the proper user name and password. It should be appreciated that different login procedures may be employed, which are well know in the industry, on an as-needed basis. 
         [0042]    To maintain edit, populate and maintain the databases, the graphical user interface  50  allows the user to perform standard text editing functions, including, mouse placement of the cursor, click-and-drag text selection and standard Windows® key combinations for cutting, copying and pasting data. In addition, the graphical user interface  50  allows users to access, copy, save, export or send data or files by using standard Windows® file transfer functions. It should be understood that these editing and file transfer functions may also be accomplished within other operating system environments, such as LINUX®, MAC OS®, UNIX, Mozilla®, etc. 
       Data Sets 
       [0043]    While the system can be used for any application in which it would be desirable to compare non-standardized data sets, the following description applies the system in the context of employment recruiting and job searching. As shown in  FIG. 3 , job posting  61  for a Web Developer is an exemplary data set, which typically provides a title  62 , job description  64 , and the criteria  66  for the job posting  61 , including the type and level of education, professional credentials, and experience that a qualified job seeker should possess. As will be described in greater detail below, from each of these pieces of information, the system can generate an attribute. In this example, job posting  61  calls for a job seeker with, among other things, a bachelors degree in computer science and experience in development in HTML and ASP. 
         [0044]    Similarly, a resume  71  represents another data set that comprises information about a job seeker.  FIG. 4  provides an illustrative resume  71  for an individual seeking position as a software developer. Information about a job seeker may include, for example, professional objectives  72 , qualifications  73 , levels of education  74 , past and present job titles and experience  76 , and personal interests  78 . As described below, the system may optionally permit a user to input her last job title  75  and offer pre-defined categories from which the user can select. The title  75  and categories can then be associated in the data set. As with job postings  61 , the system can generate one or more attributes from each of these pieces of information. 
         [0045]    In one embodiment of the invention, each data set is processed by system to generate a corresponding profile  80  comprising a plurality of attributes  70   a ,  70   b ,  70   c , etc. generated from each of the respective data sets. An exemplary profile  80  is shown in  FIG. 17 . Each data set may comprise a job posting  61  or a resume  71 . In another embodiment of the invention, the system may generate attributes  70  that are separately sub-categorized into concepts  85  and titles  87 . As will be appreciated by those of ordinary skill in the art, without departing from the invention, attributes  70  may optionally remain consolidated or may be categorized by any number of characteristics other than concepts  85  and titles  87 , such as, for example only, education, interests, and work schedule. 
       Profiles 
       [0046]    Bands 
         [0047]    The system and process for creating a profile  80  from each data set will now be described.  FIG. 4  illustrates data set comprising a user-provided resume  71 . The system associates at least one of a plurality of metrics  90   a ,  90   b ,  90   c , etc. (identified in  FIGS. 13A and 17 ) with at least one attribute  70  (for example, concept  85  or title  87 ) generated from resume  71 . In one embodiment, a metric  90   a  is a band  92  representing the relative position of text within the data set. Frequently, the relative location of data within a data set is indicative of the relative importance of that data. For example, in resume  71 , the most recent experience  76  or the job seeker&#39;s professional objective  72  is typically near the top of the resume  71 . In the context of a data set for a real estate listings, the address and price of the property is typically also at the top of the listing. Accordingly, metric  90   a  for band  92 , which represents the location of data within the data set, is helpful in assigning relative importance to each datum within the data set as the corresponding attributes  70  are generated. 
         [0048]    As shown in  FIG. 5 , Resume  71  is first broken into bands  92  and placed into band array  25   a . In one embodiment, when a user uploads or enters her resume  71  into the system, the user assigns the resume  71  a title  75  and the user&#39;s most recent job title  81 . The system may also request that the user select a job category  83  from a predetermined list of categories  83 . The steps of parsing the data set into bands  92  is shown in  FIG. 6 . In step  210 , system assigns the title  75 , if any, to band “0”  92   a . The remaining text of resume  71  is parsed by dates. At step  220 , after the title  75  is assigned to band “0”, the entire remaining text of resume  71  is entered into a memory field of band array  25   a  identified as band “1”  92   b , as shown in  FIG. 5A . System may use a regular expression to locate a date expression  94  in various formats, for example, Jan. 1, 2005, Jan. 1, 2005, 1/1/05, etc. Once a first date  94   a  is found, the system dumps all of the text that appears in resume  71  after first date  94   a  into a second row in the array  25   a  called band “2”  92   c . System continues to run the regular expression through the text of data set of resume  71  until it finds the next date  94   b , at which time it dumps any text data appearing after next date  94   b  into a new row in the array  25   a  referred to as band “3”  92   d . Regular expression continues to search for dates  94   c ,  94   d ,  94   e , etc. and dumps the text that follows each of those dates  94   c ,  94   d ,  94   e , etc. into respective bands  92   e ,  92   f ,  92   g  until no further dates are found in the remaining text. Finally, at step  230 , the system dumps the user-selected categories  83  in a final band  92   g , which may optionally be segregated by an open band  92   f , as depicted in  FIG. 5B . 
         [0049]    As will be appreciated by those of skill in the art, without departing from the invention, other variables may be used to parse bands  92 , for example, biographical data like “education”, “experience,” “skills,” and “professional associations”. In one embodiment, the system may permit yet another band (not shown) that could be manually populated with key words by the system provider or user. 
       Word Array 
       [0050]    Next, at step  250  of  FIG. 6 , and as shown in greater detail in  FIGS. 7A-7D and 8 , the system analyzes the text in each band  92   a ,  92   b ,  92   c ,  92   d , etc. to create word array  25   b . The steps to create the word array  25   b  are shown in  FIG. 8 . Starting with band “0”  92   a  shown in  FIG. 5 a   , and continuing with each subsequent band  92   b ,  92   c , etc., all of the text in each band  92  of  FIGS. 5 a  and 5 b    is dumped into the word array  25   b , shown in  FIGS. 7A-7D . At step  260  in  FIG. 8 , each character string  96  is parsed by spaces, line feeds or carriage return characters (e.g., word or phrase) to occupy a separate row of array  25   b , along with a second column that identifies the band  92  from which the word was found. At steps  265 ,  270  and  275 , system then runs through each row of array  25   b  and uses another regular expression to identify and remove undesirable punctuation, such as asterisks or to separate words by slashes. As shown in  FIG. 8 , at steps  280  and  285 , the system may optionally check each character string  96   a ,  96   b ,  96   c , etc., against substitute database  102  to replace certain character strings  96  that have well-known abbreviations. An excerpt of substitute database  102  is shown in  FIG. 9 . For example, the word “a/p” or “ap” may be replaced with “accounts payable.” By substituting equivalent terms, a more standardized lexicon of attributes  70  is ultimately generated in profile  80 , while the original data set, such as resume  71 , remains unchanged. In addition, at step  285 , the system may replace irregular word spacing, e.g., “r_&amp;_d”. 
         [0051]      FIG. 10  illustrates the steps for determining whether a character string  96  contained in the word array  25   b  should generate an entry into the attribute array  25   c . Initially, at step  305 , each word found in the word array is placed into a multi-word buffer, as described below. Then, at step  310 , the system checks the words in the buffer to determine whether any pre-defined “spam” term is found within the multi-word buffer. If such a spam word is identified, at step  315 , a flag is set to mark the entire profile  80  as including spam, so that the profile and associated data set can later be eliminated from matching searches or optionally called up for further investigation or review. 
         [0052]    After stripping each character string  96  of punctuation, at step  320 , the character string  96  may be searched against common word database  98 . An excerpt from the common word database is illustrated in  FIG. 11 . If the character string  96   a  is found in the common word database  98 , further processing can be aborted at step  345 , and system increments to the next word in array  25   b  comprising character string  96   b . By avoiding processing a common, and therefore, unhelpful word, the system processing speed is increased. As shown in  FIG. 11 , “N” designates that the word is common and therefore “not allowed.” An entry labeled “Y” designates that the word may be part of a multi-word phrase, and is therefore retained. 
         [0053]    At step  325 , the system then compares each character string  96  in word array  25   b  against the words contained in at least one attribute dictionary  104 . An excerpt of the attribute dictionary  104  is shown in  FIG. 12 . If character string  96  is found in attribute dictionary  104 , attribute array  25   c  is created at step  350  and character string  96  is placed in attribute array  25   c , along with an association to the band  92  in which the character string  96  was first found. A sample attribute array  25   c  is shown in  FIGS. 13A-13C .  FIG. 14  illustrates the steps for entering single (stand-alone) or multi-word phrases into the attribute array at step  360 . In addition, counter is incremented to track metric  90   b , which counts the number of occurrences  108  in which character string  96   a  is found in the word array  25   b . As will be described later, a third metric  90   c , defined as support  140 , is tabulated in another column of attributable array  25   c.    
         [0054]    After comparing character string  96  with the attribute dictionary  104 , character string  96  is also copied to buffer array to determine whether the character string  96  is part of a multi-word attribute  70 . If, however, character string  96  is followed by a hard carriage return, a comma or other similar punctuation that would signal that the adjacent words are unrelated, the buffer array is cleared, as indicated in  FIG. 8  at steps  290  and  295 . This flag for termination is shown in  FIG. 8 . If character string  96  does not include such a flag, the buffer array retains the character string  96   a  to be compared with the next few words that are found in the word array  25   b . The number of words to be saved in the buffer array can be varied within the system to optimize results. 
         [0055]    System then searches to see whether there are any more character strings  96  in word array  25   b , shown in  FIG. 8  at step  278 . If so, the steps shown in  FIGS. 10 and 14  are repeated. If the character string  96  is in the common word database  98  or ends in appropriate punctuation, then at step  295  on  FIG. 8 , the multi-word buffer array is cleared and the system processes the next character string  96  in the word array  25   b . If not, then at step  335  on  FIG. 10 , the multi-word buffer array is retained, and system searches attribute array  25   c  to see whether character string  96  has already been placed in attribute array  25   c . If the next character string  96  is already in the array  25   c , the occurrence counter is incremented by one. Within attribute array  25   c , the band designation  92  retains the original value of the band  92  in which the character string  96  was first found, even if later occurrences are identified in later bands. The system then checks, at step  335  on  FIG. 10 , to see whether the multi-word buffer array contains any multi-word attributes  70  contained in the attribute dictionary  104 . If so, the system checks to see whether the multi-word is found in the attribute dictionary  104 . If it is in the attribute dictionary  104 , then at steps  365 - 375  on  FIG. 14 , the attribute array  25   c  is populated with a new multi-word attribute  104 , then at steps  365 - 375  on  FIG. 14 , along with the band  92  from which the multi-word attribute word was triggered. 
         [0056]    An example will illustrate the population of the attribute array  25   c . Refer to the following text that is entered into band array  25   a  shown in  FIG. 5A : “attorney/software developer who has designed, written and been selling and supporting legal practice software applications.” As shown in  FIG. 7A , the character string  96  “attorney” is encountered in the word array  25   b  at line  2 . The word “attorney” is located in the attribute dictionary  104  (although the word “attorney” is not specifically shown), so it is placed in attribute array  25   c , shown in  FIG. 13A , along with the band 0. In addition, the occurrence counter is incremented to “1.” The word “attorney” is then saved in the buffer array. The system then finds the next character string  96 , in this example, “software.” As described below, because “software” is such a commonly-used word, it is considered a dependent attribute, and is not placed in the attribute array  25   c . Similarly, the next word, “developer,” another commonly-used word, is also designated a dependent attribute, and is therefore not placed in the attribute array  25   c . But, the multi-word buffer array  110  now contains the words “software” and “developer,” which, as a combined multi-word phrase, is found in the attribute dictionary  104  (multi-word phrase is not shown). Accordingly, system checks the attribute array  25   c  to see whether the multi-word attribute  70  “software developer” has already been entered. Since this is the first occurrence of “software developer,” the multi-word attribute  70  is entered in the array  25   c , along with its associated band  92 , band “0”  92   a , and the counter is initially incremented to “1.” As seen in  FIG. 13A , the multi-word “software developer” attribute is found in the word array  25   b  for a total of six occurrences. 
         [0057]    As also depicted in  FIG. 13A , the system also identified the multi-word attributes  70  “attorney software” and “attorney software developer.” As seen with this example, the generation of a single occurrence of the words “attorney”, “software” and “developer” in sequential order within the word array  25   b  yielded four separate attributes  70  in the array  25   c , namely, “attorney”, “software developer,” “attorney software,” and “attorney software developer.” Later, as shown on  FIG. 13B  at line  7 , when the system encounters “software” followed by “application,” it created a new entry in attribute array  25   c  for “software application,” which was incremented for a total of four occurrences. Referring to element  6  in  FIG. 7A , the word in word array  25   b  is “am,” which is found in the common word database  98 , so the “am” character string  96  is ignored, the buffer array is cleared and the system selects the next character string in band array  25   b , which is element  7 , “an.” 
         [0058]    In one embodiment, a further enhancement is provided by subcategorizing the attributes  70  as either concepts or titles. For example, the word “accountant” is identified as a title, whereas the word “accounting” is considered a concept. This can be accomplished by distinguishing between concepts and titles within the attribute dictionary  104  or by creating separate dictionaries, one title dictionary and another concept dictionary. For example, the excerpt from the attribute dictionary  104  shown in  FIG. 12  differentiates titles and concepts as follows: a “c” represents an independent (or stand-alone) concept; “cd” represents a dependent concept; “s” represents a stand-alone title; and “d” represents a dependent title. Alternatively, separate dictionaries may be used, and the system can look up each character string  96  first in the title dictionary and if no match is found, then character string  96  may be looked up in the concept dictionary. 
         [0059]    The idea of identifying independent attributes, which are entered in the attribute array  25   c  by themselves, and dependent attributes, which must be combined with other terms, can be applied to concepts and titles as shown in  FIG. 12 . The dependent concepts and titles are words that are commonly used, but provide little or no value in matching a candidate with a relevant job opening, unless combined with another word. As described in the example above, neither the concept “software” nor the title “developer” is helpful by itself in identifying qualifications of a job applicant or needs of an employer. But when the two words are combined, the phrase “software developer” is a recognized job title that is a helpful attribute. 
         [0060]    Alternatively, dependent concepts and dependent titles can be separated into separate databases, for example, in dependent concept database and dependent title database. If the character string  96  is found on either database, character string  96  is not placed in the array  25   c , but it is placed in the multi-word buffer and may be placed in the array  25   c  along with the next character string  96   b  if the next word meets the criteria in steps described in  FIGS. 10 and 14 . The system can be set to buffer a variable number of words, although buffering up to four words has been found advantageous. This permits multi-word attributes  70  comprised of four or less words to be identified, for example, “securities transactional paralegal,” “information technology consultant,” and “corporate securities transactional.” 
         [0061]    The steps in  FIGS. 8, 10, and 14  are repeated until there are no more character strings in the word array  25   b . At this point, attribute array  25   c  will be filled with the all of the attributes  70  (or substitutions) generated by the word array  25   b  that appear in attribute dictionary(ies) along with the identity of the respective band  92  in which each attribute  70  was first encountered and the total number of occurrences that each attribute  70   a ,  70   b ,  70   c , etc. appeared in word array  25   b.    
         [0062]    Next, the system checks each attribute  70  (concept or title) in the array  25   c  against the attribute dictionary ( 104 , shown in  FIG. 12 ) to identify synonyms as shown in column  105  to reduce redundancy and enhance the results during the searching and matching routine. For example, the words “a+”, “a+ certification” and “a+ certified” would all be replaced by the attribute “ID”  70  for the attribute “a+ certified” as provided in the synonym column  105 , shown in  FIG. 12 . As with the substitute list  102  described earlier, this routine adds consistency to the results. 
       Tagger 
       [0063]    In an alternative embodiment, rather than placing the words in the word array  25   b , pond  35  of words is created from the band array  25   a . The pond  35  is created by converting the data structure and data contents of the band array  25   a  into a doubly-linked list. As will be known by those of ordinary skill in the art, a doubly-linked list comprises a sequence of nodes, each containing a data field and having two references, one pointing to the previous node and the other to the next node in the list. 
         [0064]    Through a series of operations, the doubly-linked list, which comprises pond  35 , is tagged using HTML to assign tokens to certain items in the double-linked list. The tokens may identify attributes  70 , such as a dependant concept or title  36 , an independent concept or title  37 , a negator such as the word “not” (not shown), a break  38 , an unrecognized word  39 , a slash group  45 , or a connector  43 , as will be described further. In one embodiment, pond  35  tokens may be color coded in HTML. For example, in  FIGS. 20A-20E , pond  35  identifies several pond items, including “software” and “program,” which may be displayed in orange to designate dependant concepts or titles  36 ; “engineer” and “c++,” which may be displayed in green to designate concepts or titles  37 ; “&lt;—band—&gt;,” “:” “,” which may be displayed in red to designate hard breaks  38  between words in the pond; “in the,” “in,” “of,” and “&lt;—line—&gt;,” which may be displayed in blue to designate connector  43  that may join adjacent items; and finally, items grouped within “{ }” brackets to indicate slash groups  45 . 
         [0065]    The addition of the tokens may be done in sequential operations. For example, multiword concepts—such as “visual basic,” in which neither “visual” nor “basic” is, by itself, a concept or title but becomes a concept when appearing adjacent one another—may be identified and tagged. Similarly, multi-part concepts—such as “computer science,” which include dependant and independent concepts or titles—may be identified and tagged. The use of a double-linked list for the pond eliminates the need to maintain a multi-word buffer, and also permits greater flexibility for combining attributes  70  that are spaced more than a limited number of positions away from one another. For example, in the embodiment employing a buffer, described above, the combination of multi-word attributes is limited by the number of words or character strings  96  held in the buffer, whereas by using a double-linked list, the words may searched for attributes  70  anywhere within the double-linked list (i.e., pond  35 ), subject to any tagged limitations, such as hard breaks  38  or negators. This advantage increases the flexibility for combining separate attributes surrounding attributes within a slash group. The tokens further provide flexibility for defining and modifying behaviors (i.e., programming instructions) associated with particular types of words. For example, by retaining a connection between two lists of words, a connector  43 , such as “of,” may identify a useful attribute  70 , such as “human resources manager” from the original “manager of human resources.” 
         [0066]    A post processing step may be optionally provided in which “orphaned” attributes are identified and replaced with more meaningful attributes. For example, if the dependant title “engineer” appears by itself in one location in the pond but the multi-word title “electrical engineer” appears elsewhere in the pond, the orphaned dependant title “engineer” may be replaced with “electrical engineer.” 
         [0067]    In further aspect of this embodiment, because the attributes, such as independent concepts or titles (or both)  37 , have already been identified in the pond  35  during the tagging process, the attribute array  25   c  may be readily created by running through the tagged pond. The attribute array is illustrated in  FIGS. 13A, 13B, 13C, and 20A . 
       Assigning Support Metric 
       [0068]    To further enhance the accuracy of the profile generation, each attribute  70  that is entered into array  25   c  is evaluated by how closely the attribute  70   a ,  70   b ,  70   c , etc. is related to other attributes  70   a ,  70   b ,  70   c , etc. in the array  25   c . This is accomplished by the use of attribute “pods”  125 .  FIG. 15  shows excerpts from a sample pod  125   a .  FIG. 16  illustrates the steps described next for generating a support metric  90   c.    
         [0069]    Each pod  125   a ,  125   b ,  125   c , etc. identifies the relatedness of a “root” attribute  130  (for example, concept or title) to other words that may appear within word array  25   b  (which, in turn, are related to words appearing in the data set, for example, a resume  71  or a job posting  61 ). Each pod  125   a ,  125   b ,  125   c , is created by conducting an analysis for each root  130  to determine what other attributes  70   a ,  70   b ,  70   c , etc. are related to the root  130 . In one embodiment, every attribute  70   a ,  70   b ,  70   c , etc. is designated, in turn, as the root  130  and searches are conducted through a large number of sample data sets (for example, resumes  71   a ,  71   b ,  71   c , etc. and/or job postings  61   a ,  61   b ,  61   c , etc.) or sample sets of profiles  80  to identify each occurrence of another attribute  70 , which is referred to as a “leaf”  135 . 
         [0070]    The pod  125  information can be refined, for example, by counting the number of occurrences in which both the root  130  and each leaf  135  appears (a) within a given data set, (b) within the same paragraph of a data set, and/or (c) within the same sentence of a data set. Similarly, the comparisons could be made between attributes  70  appearing in profiles  80  and within the same bands  92 . The resulting occurrences  108  for the sample data sets are then compiled into a pod  125  for each root  130 , identifying how many times each leaf  135  is associated with the root  130 . Thus each pod  125  can list the number and percentage of occurrences that both the root  130  and each leaf  135  appeared within the same document, paragraph, and sentence of the sample data sets or same bands  92  of profiles  80   a ,  80   b ,  80   c , etc. An example of the pod  125   a  for the root, “accountant” is set forth in  FIG. 15 . 
         [0071]    Pods  125   a ,  125   b ,  125   c , etc. may be used to scale the profile  80  in several ways and to add various degrees of precision by assigning a metric  90   c  for “support”  140 , which signifies the presence of attributes  70  that are more likely related to the root  130 . For example, in one embodiment, the pod  125  may be truncated into a binary value, whereby “1” identifies the existence of a relationship and “0” identifies the absence of a relationship. This assignment of support value is shown in steps  405 - 430  on  FIG. 16 . To illustrate, in a given array  25   c , if a leaf  135  appears in the pod  125  for a root  130 , support  140  counter would be incremented by one, at step  430 , regardless of whether the leaf  135  appeared in all of the sample data sets or only one of the sample data sets. In this scenario, each time any leaf  135  is found in the pod  125  for a root  135   a , the counter would be incremented by 1 for that particular root  130   a . Thus, if many leafs  135   a ,  135   b ,  135   c , etc. for a particular root  130   a  are found in the attribute array  25   c , the support  140  for the root  130   a  is high and the root  130   a  is weighed more strongly in the profile  80 . 
         [0072]    In an alternate embodiment, the relative percentage of appearances of each leaf  135   a ,  135   b ,  135   c , etc. to each root  130   a  can be cumulatively added and then normalized with the other metrics  90  (e.g., the band  90   a  and occurrence  90   b  scores). For example, as seen in  FIG. 15  pod  125   a  for the root  130   a  “accountant” and the leaf  135   a  “certified” provides support  140   a  of 54.16%, and support  140   b  for the leaf  135   b , “gaap” of 76.00%. So, if a profile  80  includes the root “accountant” and the leafs “certified” and “gaap”, these support values can be added to 130.16%. Accordingly, the support  140  values for all the leafs  135   a ,  135   b ,  135   c , etc. in the attribute array  25   c  associated with each root  130  could be totaled for a grand support  140  value for each concept in the attribute array  25   c.    
         [0073]    In another embodiment this total support  140  value can then normalized to correspond with the approximate magnitude of the other metrics  90   a ,  90   b ,  90   c , etc. associated with the attribute array  25   c . Normalizing the support  140  value can be done many ways without departing from the invention. For example, in one embodiment, the support value  140  totals are divided by a value such as the highest score of all the support  140   a ,  140   b ,  140   c , etc. value totals and then multiplied by a multiplier. 
         [0074]    In another embodiment, each gross support  140   a ,  140   b ,  140   c , etc. value can merely be ranked. For example, the gross support  140  value can be replaced by the reverse rank (so the highest gross support  140  value would have the highest value). To illustrate, as shown in Table 1, if a series of root attributes  130  have a gross support  140  values of root  140   a= 1209, root  140   b= 2409, root  140   c= 478, root  140   d= 8904, root  140   e= 35, root  140   f= 0, the support  140  values assigned in attribute array  25   c  could be as follows: root  140   a= 3, root  140   b= 4, root  140   c= 2, root  140   d= 5, root  140   e= 1 and  140   f= 0. Various methods for using the pods  125  for assigning relative weighting for the support  140  value may be employed without departing from the invention. 
         [0000]    
       
         
               
               
               
             
               
               
               
             
           
               
                 TABLE 1 
               
               
                   
               
               
                 ROOT 
                 GROSS SUPPORT 
                 SUPPORT VALUE 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 140d 
                 8904 
                 5 
               
               
                 140b 
                 2409 
                 4 
               
               
                 140a 
                 1209 
                 3 
               
               
                 140c 
                 478 
                 2 
               
               
                 140e 
                 35 
                 1 
               
               
                 140f 
                 0 
                 0 
               
               
                   
               
             
          
         
       
     
       Ranking the Profile 
       [0075]    To complete the profile  80  for each data set, the metrics  90  are used to rank the attributes according to relative importance, as identified in steps  450  and  455  of  FIG. 16 .  FIG. 17  shows an exemplary profile  80 . In one embodiment, all the generated attributes  70   a ,  70   b ,  70   c , etc. are placed in the array  25   c  in order of appearance within the bands  92  as shown in steps  450  and  455 , shown in  FIG. 16 . That is, all the attributes  70   a ,  70   b ,  70   c , etc. found for the first time in band “0”  92   a  are listed as band “0”, then band “1”, band “2”, and so on. Next, after the support values  140   a ,  140   b ,  140   c , etc. are assigned, the leafs  135   a ,  135   b ,  135   c , etc. that are found supporting each root  130   a ,  130   b ,  130   c , etc. are pulled up in order of descending support value  140  behind each related root  130 . Finally, within each group of root  130  and associated leafs  135   a ,  135   b ,  135   c , etc., the leafs are listed in order of number of occurrence  108 . This ranking or weighting scheme is exemplary and other schemes may be used without departing from the invention. 
         [0076]    Once the array  25   c  and associated metrics  90   a ,  90   b ,  90   c , etc., such as, band  92 , occurrence  108  and support  140 , are ranked, the attributes  70  and associated metrics  90   a ,  90   b ,  90   c , etc. can be saved as a profile  80 , which is associated with the respective data set from which the profile  80  was generated. For example,  FIG. 17  illustrates the ranked attributes  70  for the sample resume  71  shown in  FIG. 4 . In this example, titles  87  are broken out from concepts  85  into separate lists. The values in parenthesis after each attribute  70  represent the band  92 , occurrences  108 , and support  140  generated for each attribute  70 . The attributes  70  are thereby ranked in order of relative importance in the context of the originating data set. The respective list of titles  87  and concepts  85  can be selectively combined, for example, by interleaving the two ranked lists, (i.e., by placing the highest ranked title  87  first, then the highest ranked concept  85 , then the second highest ranked title, etc.) or by giving each variable weight. 
         [0077]    In addition, the data set may also be further associated with user account information. For example, a job seeker may have an account set up that can include contact information, history of job postings that the job seeker has reviewed, job postings that the job seeker has applied for, and other data associated with the individual. Similarly, a job poster or employer may have a user account that retains contact information, service packages, billing information, other job postings, applications received for each job posting, and other information associated with the employer. 
         [0078]    In one embodiment, a user may be given an opportunity to see the resulting profile  80 , for example in the format shown in  FIG. 17 , and be permitted to modify the profile  80 . For example, the user could be permitted to emphasize or deemphasize certain attributes  70 , their associated metrics  90  or manually adjust their ranking. A job seeker may notice that a particularly important attribute  70  is ranked lower than other less important (to the user) attributes  70 . Accordingly, the user may optionally be permitted to adjust one or more of the metrics  90  for the attribute(s)  70  to give the attribute(s)  70  more significance when used for matching, as described below. 
         [0079]    It will be appreciated by those of ordinary skill in the art that the system and method, which is described above in the context of data sets comprising resumes  71 , could just as readily be used for other data sets, including job postings  61 . For other data sets, the metrics  90  used to score the attributes  70  may be varied. For example, job postings  61  typically do not delineate information by date, as is typical with resumes  71 , but may instead parse the data by title, experience, and skills. Accordingly, bands  92  could use different character strings or words rather than dates to parse the data set. 
         [0080]    Moreover, the system and method for creating standardized profiles  80  for non-standard data sets can be used for data sets unrelated to recruiting and employment, including for example, dating or match-making services, real estate listings, classified advertising, used-car listings, etc. 
       Assigning a Level Metric 
       [0081]    In another embodiment, the profile  80  may include a level, which is commensurate with the degree of skill represented by the data set, that is, a level being sought by a job posting or attained by a job seeker. For example, education level  47  represents a metric  90  and job level  49  represents another metric  90 . 
         [0082]    The education level  47  is derived from a search of keywords contained in the data set that represent education, such as “high school”, “BA”, “BS”, “MBA”, “masters degree”, “MD”, “PhD”, etc. If such keyword are contained in the data set, a metric  90  representing  47  is assigned, which is indicative of the education level. Education levels may include: 
         [0000]    
       
         
               
               
               
             
           
               
                   
                   
               
             
             
               
                   
                 EL_HS 
                 High School 
               
               
                   
                 EL_BA 
                 Bachelor of Arts 
               
               
                   
                 EL_BS 
                 Bachelor of Science 
               
               
                   
                 EL_RN 
                 Registered Nurse 
               
               
                   
                 EL_MS 
                 Master of Science 
               
               
                   
                 EL_MBA 
                 Master of Business 
               
               
                   
                   
                 Administration 
               
               
                   
                 EL_MD 
                 Medical Doctor 
               
               
                   
                 EL_PHD 
                 Doctor Philosophy 
               
               
                   
                   
               
             
          
         
       
     
         [0083]    The job level  49  may be derived using the education level  47  as an input, as well as by factoring in additional data from which a job level  49  may be assigned. For example, in one embodiment, the job levels  49  may be assigned as “entry”, “mid”, “senior”, or “executive.” In addition to the education level  47  described above, another input may include the results of a search identifying other keywords that are indicative of job level  49 , for example, “vice president”, “vp”, “manager”, “supervisor”, and others. 
         [0084]    Beyond keyword searching, the method and system may also seek other indicia of job responsibility, like managing other people. In one embodiment, upon a job seeker posting his or her resume, the system may ask whether the job seeker has ever managed people, and the resulting answer may be used as an input for evaluating job level  49 . Similarly, an input for the appropriate job level  49  may include the numbers of years of experience in a particular field. The number of years of experience can be identified by using regular expressions to identify all the date designations for a particular position on a resume, and calculating the number of years from the date of one position to the date of the prior position. 
         [0085]    Based on one or more of the inputs described above, scores may be tallied to assign a job level  49 . In one embodiment, job levels  49  are assigned as either “entry”, “mid”, “senior”, or “executive”. In one embodiment, the data set begins with a default job level of “mid” and the inputs described above are used to tally points for the respective levels. For example, the presence of “high school” may generate adding a point to the job level, “entry”, whereas the presence of “MBA” may generate adding a point to the job level, “senior”. Similarly, having less than two years of experience may generate a point for the job level, “entry”, while more than five years experience may generate a point for the job level  49 , “senior”. Upon tallying all the points based on the various inputs described above, the tallies are compared to assign a job level  49 . In one exemplary tally, if the tally for “executive” had more points than “entry” and “senior,” the data set would be assigned an “executive” job level  49 . If the tallies for “entry” is less than “senior”, and “senior” is tied with “executive,” a job level  49  of “senior” would be assigned. If the tally for “senior” is two or more than the tally for “entry”, the data set may be assigned “senior”, whereas any differential less than two will result in an assignment of “mid.” In another example, if the tally for “entry” is two or more than the tally for “senior,” the data set may be assigned “entry,” whereas any differential less than two will result in an assignment of “mid.” In the last two examples, requiring at least a two-point differential between “senior” and “entry” results in a “mid” assignment for any close results, and prevents skewing the assigned job level  49  unless there is a meaningful indication that a job level  49  is higher. In the described embodiment, the default job level  49  when there are no indicia of seniority is “mid.” 
         [0086]    As will be understood by a person of ordinary skill in the art, the various levels create a spectrum of experience and skill, and various logic can be used to generate job level  49  assignments that will be beneficial in matching data sets. 
         [0087]    Once assigned, the job level  49  may be used in various ways to filter, search and display the output of matched profiles, as will be described next. 
       Matching Profiles 
       [0088]    Once profiles  80  are generated for a series of data sets, the profiles  80  may be leveraged in many ways. Because the data sets—be they resumes  71 , job postings  61 , or others—are generated into profiles  80  having standardized sets of attributes  70  and organized in a standard ranking or scaling scheme, disparate data sets can be efficiently compared, grouped, and ranked. One use for the profiles  80  is to match prospective job seekers having respective resumes  71  to a particular job posting  61 . Conversely, the profiles  80  can be used to match prospective job postings  61  to a particular job seeker having a resume  71 . In addition, a job seeker who is interested in a particular job posting  61  can leverage that particular job posting profile  80  to search for other job postings that are similar to the job posting of interest. Similarly, employers can leverage the profile  80  of a particular job seeker&#39;s resume to search for other job seekers whose resumes are similar to the resume of interest. 
         [0089]    Once the profiles  80  of the data sets are generated, there are many ways known in the art to conduct searches and generate matches between one profile  80   a  of a data set to find the closest matching other profiles  80   b ,  80   c  . . .  80   n . In one embodiment, the system converts each profile  80  into a series of numerical values, where each available attribute  70  is assigned a unique numeric integer value or identifier (e.g., “ID”). Such numeric IDs are illustrated in  FIG. 12 . Converting the text value of each attribute  70   a ,  70   b ,  70   c , etc. into a numeric value increases the efficiency of commercially available search engines. Accordingly, each of the attributes  70   a - 70   n  in a profile  80  can be converted into its assigned numeric value, for example, the attribute  70  “.net” shown in  FIG. 12  may be assigned numeric value “80 4685.” Because integer values can comprise significantly smaller amounts of data than full ASCII character words, this translation can speed up the processing time for the search engine. This conversion from text character to integer value can be performed while the profile  80  is being created or after it is done. 
         [0090]    One example of a suitable search engine  60  for use in generating searches to match various profiles  80  is offered by Fast Search &amp; Transfer ASA. One search engine solution offered by Fast and suitable for use with an embodiment of this invention is FAST Data Search™. 
         [0091]    To conduct a candidate search of a plurality of resumes  71   a ,  71   b ,  71   c , . . .  71   n  based on a profile  80   a  for a job posting, (for ease of reference, the “subject profile  80 ”), the subject profile  80   a  can be readily converted into a search query for input into the search engine  160  to conduct a search of a plurality of resume profiles  80  (the “target profiles  80   b - 80   n ”). 
         [0092]    The search can optionally be weighted to further enhance the search results. In one embodiment, the query based upon the subject profile  80   a  can be created by weighting each attribute  70   a - 70   n  according to its ranking within profile  80   a , so that the highest ranking attribute  70   a  is weighted highest in the search, the second highest-ranking attribute  70   b  is weighted second highest, and so on through all the attributes  70   n.    
         [0093]    Similarly, it is beneficial to weight the target profiles  80   b - 80   n  to enhance the search results. While the search query can include as many attributes  70  as desired, it is more practical and efficient to limit the number of attributes  70  that are separately weighted among the target profiles  80   b - 80   n ; otherwise, the amount of data for all the attributes  70  associated with all the target profiles  80   b - 80   n  would slow the search engine. Accordingly, the attributes  70  of the target profiles  80   b - 80   n  may be weighted in tiers. If each target profile  80  (e.g., resume profile) contains a ranked list of, for example, forty-three separate attributes  70 , the forty-three attributes  70  can be weighted according to the following tiers. The first 10 attributes can each be assigned a weight of, for example, 5000 points, while attributes  11 - 20  may each be assigned a weight of, for example, 700 points, and attributes  21 - 43  may be assigned a weight of, for example, 10 points. 
         [0094]    The query generated from the subject profile  80   a  will then cause the search engine  160  to return a list of target profiles  80   b - 80   n  (in the foregoing example, resume profiles) in a ranked order by how closely the weighted target profiles  80   b - 80   n  match the subject profile query. These are matching profiles  165 , as identified in  FIG. 18 . 
         [0095]    The system can optionally provide even further refinement of the search results by using a recommendation engine  155 , as illustrated in  FIG. 18 , to select recommended profiles  175  from the matching profiles  165 . The recommendation engine  155  may eliminate target profiles  80   b - 80   n  that fail to meet a minimum threshold matching score or modify the ranking of the profiles  80   b - 80   n . In other words, the subject profile  80   a  may be compared against each target profile  80   b - 80   n  retrieved by the search engine and through the following process matching scores may be assigned to each target profile  80   b - 80   n . Specifically, the system checks each attribute  70  in the subject profile  80   a  against each target profile  80   b - 80   n  retrieved by the search engine and, using a suitable formula that will be described below, assigns points corresponding to how closely the attributes  70  in the subject profile  80   a  correlate with the attributes  70  in the target profile  80   b - 80   n . An attribute  70  that is listed in both the subject profile  80   a  and a target profile  80   b - 80   n  can be referred to as a “matching attribute”  150 . The degree with which a subject profile  80   a  matches a target profile  80   b - 80   n  will depend on the number of matching attributes  150  and the relative ranking of each matching attribute  150  within the subject profile  80   a  and a target profile  80   x . For example, a target profile  80   x , shown in  FIG. 19 , whose lowest-ranked attribute matches the highest-ranked attribute of the subject profile  80   a  will likely be less relevant than a target profile  80   y , whose highest-ranked attribute matches the highest-ranked attribute of the subject profile  80   a.    
         [0096]    Accordingly, in one embodiment, points are assigned to each target profile  80   b - 80   n  based on how high the matching attributes  150  for both the subject profile  80   a  and the target profile  80   x  rank. For example, the system checks each attribute  70   x  in the subject profile  80   a  to determine whether the same attribute  85  is also included in the target profile  80   x . For attributes that do not match, no points are assigned, and the system moves to the next attribute  70  in the subject profile  80   a . If the system finds a matching attribute  150 , it assigns points based on how high the matching attribute  150  is ranked in the subject profile  80   a . The system runs through all the attributes in the subject profile  80   a  and compiles the total points based on the ranking of the matching attributes  70  within the subject profile  80   a . Obviously, if only the five bottom ranked attributes  70  in the subject profile  80  matched the attributes in the target profile  80   x , there may not be a very good match, even if such five matching attributes  150  were ranked high in the target profile  80   x . As a result, the system then repeats the process, but this time assigns points based on how high the matching attributes  150  are ranked in the target profile  80   x . Then the points assigned for the subject profile  80   a  and the points for the target profile  80   x  are added together for a total matching score. 
         [0097]    To convert the highest rank (which is typically represented by the lowest number, i.e., first or 1) to the highest points, the system assigns the total number of attributes in the subject profile  80   a , minus the rank of each matching attribute  150 . For example, assuming there are 50 attributes in the subject profile  80 , if a matching attribute  150  is the highest ranking attribute in the target profile  80   x , the target profile  80   x  would be assigned points equal to 50−1=49. 
         [0098]    In one embodiment, to enhance the screening and create even more differentiation between the rankings, the results are then squared. So in the last example, (50−1) 2 =49 2 =2401 would be assigned to the target profile  80   x . The system may then search for the next matching attribute  150  and continue assigning points until all the matching attributes  150  were assigned points. The total points will identify how high the matching attributes  150  were ranked in the target profile  80   x . Then the system repeats the tally by assigning points for how high the matching attributes  150  ranked in the subject profile  80   a.    
         [0099]    This can be illustrated by an example, as shown in  FIG. 19 . Assume that there are five matching attributes  150  between a subject profile  80   a  and a target profile  80   x , and for simplicity, assume that both the target profile and subject profile each have 50 attributes. Further assume that the matching attributes  150  were the top five ranked attributes in the subject profile  80   a . In this case, the score would be (50−1) 2 +(50−2) 2 +(50−3) 2 +(50−4) 2 +(50−5) 2 =11055. If the five matching attributes  150  were ranked 46-50 (at the bottom) in target profile  80   x , the totals would be (50−50) 2 +(50−49) 2 +(50−48) 2 +(50−47) 2 +(50−46) 2 =0+1+4+9+16=30. To further enhance the matching results, the two scores can be added together for a total score of 11085. In contrast, compare to another example using a target profile  80   y  having the same matching attributes  150  as target profile  80   x , but where they are ranked in the top five on the subject profile  80   a . This would yield a score of 11055, so when the two scores were added together, the total score would be 22110. 
         [0100]    This calculation can be completed for each target profile  80   b - 80   n  retrieved by the search engine. Finally, the point totals are normalized by dividing the score for each target profile  80   b - 80   n  by a perfect score for the subject profile  80   a , where a perfect score would be the matching score that would be yielded by a profile that exactly matched the subject profile  80   a . Using this scoring method, it has been found that matching scores of less than 18% yield unsatisfactory results. Thus, target profiles  80  yielding a match score less than a preset threshold may be optionally discarded. It should be understood that this threshold can be changed or varied to optimal values without departing from the invention. 
         [0101]    While this describes one method for identifying how closely a target profile matches a subject profile, many other methods can be employed without departing from the invention. For example, the ranking of each matching attribute within the subject profile and the target profile can be compared to determine the relative degree of similarity between the two profiles. For example, if a matching attribute is ranked third in the subject profile and ranked 34 th  in the target profile, the matching attribute could be assigned a score of the difference, i.e., 34−3=31, and this score can be used to screen or weight the importance of the matching attribute. So, for example, the system could optionally discard any matching attributes that are not within a predetermined number of ranking from each other. 
         [0102]    The same technique can be used to input a resume profile  80  into the search engine and generate job posting profiles. Indeed, the system can be used to create matches between the profiles created for any data sets. For example, the system could be used to compare individual profiles for a personal match-making service, real estate listings, classified advertising, used car listings, etc. 
         [0103]    As will be appreciated by those of skill in the art, the present system may be used to generate matches between various data sets. For example, upon uploading a new resume, a user could be provided with a list of suitable job postings. Similarly, upon an employer uploading a job posting could be provided a list of suitable resumes based on the output of the system and method described herein. In addition, a job seeker who has found one job posting of interest could request that the system find other job postings that are similar to the job posting of interest. Conversely, an employer who finds a candidate of interest could request the system generate a search using the system and method disclosed herein to provide a list of similar candidate resumes. 
       Recommendation Page 
       [0104]    Once matching profiles are selected, they can be further arranged or categorized in groups to assist the job seeker or job poster in selecting the most appropriate matches. 
         [0105]    For example, in one embodiment, to further enhance searching and matching capabilities, job levels  49  may be used to rank, organize or filter the data set search results. For example, upon searching for job postings that match a particular job seeker&#39;s resume, the resulting job postings could be presented in groups according to discrete job levels. Similarly, upon searching for resumes that match a particular job posting, the resulting resumes may be presented in groups according to job levels, such as “entry-level,” “mid-level,” “senior,” and “executive.” Further, the system may optionally filter out the results to limit the results only to matching job levels. In yet another embodiment, searches may permit a user to specify a particular job level to match the interests of a given job seeker or job poster. 
         [0106]    In addition, matching profiles may be organized by “fresh” for more recent postings, “close” for ranking profiles that are located geographically nearby, “region” for ranking profiles by geographical region, or “relevant” for ranking profiles by how closely the match. 
         [0107]    In another embodiment, the target profiles can be organized by various topics within the subject profile. In other words, attributes contained in a profile may be identified and grouped together in order to conduct searches that are focused on specific topics. In one embodiment, each attribute in the subject profile is compared to every other attribute in the profile and uses the pods  125  to determine how related (if at all) the two attributes are. Each attribute is then paired with its highest matching other attribute to form “dynamic pods” containing two attributes. To ensure that attributes have some minimum level of relatedness, thresholds can be set. The system then takes another pass comparing each dynamic pod to each other using a lower threshold, and the highest matching dynamic pods are combined to form larger pods. This comparison process continues until an optimum dynamic-pod size is reached. For example, the system can be configured to continue iterations until there is an optimum number of attributes within each dynamic pod (e.g., 4-8) or until there is an optimum number of dynamic pods (e.g., 3-5). In further enhancement, the system may optionally add attributes to the dynamic pods by adding attributes that are highly related based on the pods  125 , even where the related attribute is not actually in the profile. This can create more robust results based on the dynamic pods, as will be described next. 
         [0108]    Once the dynamic pods are generated, user may seek profiles that match only the attributes captured within the dynamic pod. For example, the dynamic pod may be displayed in a tab on a recommendation page, and by clicking on the tab, the system can fire a new search seeking for profiles matching just the attributes listed in the dynamic pods. This search will generate more focused profile search results. 
         [0109]    While specific embodiments of the present invention have been described in detail, it will be appreciated by those skilled in the art that various modifications and alternatives to those details could be developed in light of the overall teachings of the disclosure. For example, the processes described with respect to computer executable instructions can be performed in hardware or software without departing from the spirit of the invention. Furthermore, the order of all steps disclosed in the figures and discussed above has been provided for exemplary purposes only. Therefore, it should be understood by those skilled in the art that these steps may be rearranged and altered without departing from the spirit of the present invention. In addition, it is to be understood that all patents discussed in this document are to be incorporated herein by reference in their entirety. Accordingly, the particular arrangement disclosed is meant to be illustrative only and not limiting as to the scope of the invention which is to be given the full breadth of the appended claims and any equivalents thereof.